How a Japanese Cucumber Farmer Is Using Deep Learning and TensorFlow
PlaNet—Photo Geolocation with Convolutional Neural Networks
Automatic Photography With Google Clips
Using Deep Learning to Create Professional-Level Photographs
Internet Search Tips
Leprechaun Hunting & Citogenesis
Why Stanford Researchers Tried to Create a ‘Gaydar’ Machine
Deep Neural Networks Are More Accurate Than Humans at Detecting Sexual Orientation from Facial Images
Facial recognition technology can expose political orientation from naturalistic facial images
Do Algorithms Reveal Sexual Orientation or Just Expose Our Stereotypes?
A Replication Study: Machine Learning Models Are Capable of Predicting Sexual Orientation From Facial Images
It Was Called a Perceptron for a Reason, Damn It
Dogs, Wolves, Data Science, and Why Machines Must Learn Like Humans Do
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
https://www.reddit.com/r/MachineLearning/comments/3ailzi/suddenly_a_leopard_print_sofa_appears/csczkqg/
Artificial Intelligence: A Beginner's Guide
Magical Categories
Marvin Minsky - Scientist - Embarrassing Mistakes in Perceptron Research
Marvin Minsky—Embarrassing Mistakes in Perceptron Research (122/151)
Eliezer S. Yudkowsky – "That Which Can Be Destroyed by the Truth Should Be."
Magical Categories
https://intelligence.org/files/AIPosNegFactor.pdf
Using Statistics: A Gentle Introduction
1998-cariani.pdf
Essentials of Computer Organization and Architecture
http://sapyc.espe.edu.ec/evcarrera/DSP/pitch.pdf
https://www.clear.rice.edu/comp200/02spring/Lecture-notes/lec24.txt
2000-cartwright-intelligentdataanalysisinscience.pdf
https://web.archive.org/web/20001029201251/http://ieee.uow.edu.au/~daniel/software/libneural/BPN_tutorial/BPN_English/BPN_English/node9.html
https://www.rifters.com/real/STARFISH.htm
Starfish § Bulrushes
Neural Network Follies
https://x.com/dribnet/status/914945926266970112
1997-dhar-intelligentdecisionsupportmethods.pdf
Quantitative Analysis of Multivariate Data Using Artificial Neural Networks: A Tutorial Review and Applications to the Deconvolution of Pyrolysis Mass Spectra
https://catless.ncl.ac.uk/risks/16.41.html
1993-harth-thecreativeloop.pdf
https://www.jefftk.com/dreyfus92.pdf
https://www.youtube.com/watch?v=cG7v9eCq2u4&t=33m49s
1992-dreyfus-whatcomputerstillcantdo.epub
Detecting Tanks
What Computers Can’t Do
1991-sethi-artificialneuralnetworksandstatisticalpatternrecognition.pdf
1964-kanal.pdf
1977-agrawala-machinerecognitionofpatterns.pdf
http://www.dtic.mil/docs/citations/AD0410261
1962-harley.pdf
Libor Špaček Homepage
https://x.com/cbrew/status/920088821823344640
https://www.lesswrong.com/posts/5o3CxyvZ2XKawRB5w/machine-learning-and-unintended-consequences?commentId=SNHJNFN9SjNW6djgc
Ed Fredkin and the Physics of Information: An Inside Story of an Outsider Scientist
https://archive.computerhistory.org/resources/access/text/2013/05/102630504-05-01-acc.pdf
A Proposal For The Dartmouth Summer Research Project On Artificial Intelligence
https://raysolomonoff.com/dartmouth/dartray.pdf
https://web.archive.org/web/20140908045019/http://www.bratislavaguide.com/radio-yerevan-jokes
1980-metzger.pdf
The Secret History of Facial Recognition: Sixty years ago, a sharecropper’s son invented a technology to identify faces. Then the record of his role all but vanished. Who was Woody Bledsoe, and who was he working for?
https://archive.computerhistory.org/resources/access/text/2013/05/102630504-05-01-acc.pdf#page=27
book#shannon-late-career
[Transclude the forward-link's
context]
https://web.archive.org/web/20220927022638/https://nautil.us/the-man-who-tried-to-redeem-the-world-with-logic-235253/
Revisiting Highleyman's Data
A Sociological Study of the Official History of the Perceptrons Controversy [1993]
Oral History Interview with Terry Allen Winograd (OH #237) § SHRDLU
Who Invented Backpropagation?
Gradient Theory of Optimal Flight Paths
Deep Learning in Neural Networks: An Overview
Picasso: A Modular Framework for Visualizing the Learning Process of Neural Network Image Classifiers
Picasso: A Free Open-Source Visualizer for Convolutional Neural Networks; Cloudy With a Chance of Tanks
https://x.com/Miles_Brundage/status/874448037929725952
Clarifai, the AI Workflow Orchestration Platform
Unbiased look at dataset bias
Measuring the tendency of CNNs to Learn Surface Statistical Regularities
Do CIFAR-10 Classifiers Generalize to CIFAR-10?
Do ImageNet Classifiers Generalize to ImageNet?
Identifying Statistical Bias in Dataset Replication [Blog]
Identifying Statistical Bias in Dataset Replication
Rip van Winkle’s Razor, a Simple New Estimate for Adaptive Data Analysis
Covariate Shift in High-Dimensional Random Feature Regression
Cold Case: The Lost MNIST Digits
Impact of ImageNet Model Selection on Domain Adaptation
Are we done with ImageNet?
Do Better ImageNet Models Transfer Better?
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
Big Transfer (BiT): General Visual Representation Learning
1988-damato.pdf
Gender-From-Iris or Gender-From-Mascara?
https://gidishperber.medium.com/what-ive-learned-from-kaggle-s-fisheries-competition-92342f9ca779
https://pdfs.semanticscholar.org/829e/6bcabe9cc1bd334429215404a5adaefc7ade.pdf
https://x.com/sigfpe/status/919995891502551042
Confounding variables can degrade generalization performance of radiological deep learning models
What Are Radiological Deep Learning Models Actually Learning?
https://x.com/tdietterich/status/1154839042623594496
https://pdfs.semanticscholar.org/1cd3/57b675a659413e8abf2eafad2a463272a85f.pdf
Case Study
Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135)
Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma Recognition
Man against Machine: Diagnostic Performance of a Deep Learning Convolutional Neural Network for Dermoscopic Melanoma Recognition in Comparison to 58 Dermatologists
Dermatologist-Level Classification of Skin Cancer With Deep Neural Networks
Why Doctors Aren‘t Afraid of Better, More Efficient AI Diagnosing Cancer: Just like Humans, AI Isn’t Perfect
Goal Misgeneralization in Deep Reinforcement Learning
Scaling Laws for Reward Model Overoptimization
The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models
On Seeing Through and Unseeing: The Hacker Mindset
Evolution as Backstop for Reinforcement Learning
turing-complete#security-implications
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context]
Parallel Universe
SM64—Watch for Rolling Rocks—0.5x A Presses (Commentated)
The Cost of Subsistence
The Diet Problem
Stigler's Diet Problem Revisited
https://x.com/oe1cxw/status/957409526940094464
https://x.com/oe1cxw/status/958704985495175169
https://openai.com/research/faulty-reward-functions
https://www.reddit.com/r/MachineLearning/comments/18eh2hb/p_the_power_of_reinforcement_learning_look_how/
Learning from Human Preferences
https://pdfs.semanticscholar.org/10ba/d197f1c1115005a56973b8326e5f7fc1031c.pdf
It Takes Two Neurons To Ride a Bicycle
2004-cook-twoneuronbicycle.avi
http://luthuli.cs.uiuc.edu/~daf/courses/games/AIpapers/ng99policy.pdf
Evolving 3D Morphology and Behavior by Competition
Recent Developments in the Evolution of Morphologies and Controllers for Physically Simulated Creatures § A Re-implementation of Sims’ Work Using the MathEngine Physics Engine
https://x.com/hardmaru/status/1050193431857774592
Nonlinear Computation in Deep Linear Networks
Human-level performance in 3D multiplayer games with population-based reinforcement learning
Artificial Intelligence Learns Teamwork in a Deadly Game of Capture the Flag
Data-efficient Deep Reinforcement Learning for Dexterous Manipulation
Deep Reinforcement Learning for Dexterous Manipulation—Grasp and Stack
https://arxiv.org/pdf/1703.04070.pdf#page=3
Deep DPG (DDPG): Continuous control with deep reinforcement learning
Our ‘NIPS 2017: Learning to Run’ Approach
Emergence of Locomotion behaviors in Rich Environments
Pass the Butter // Pancake Bot
Learnfun and Playfun: A General Technique for Automating NES Games
https://www.youtube.com/watch?v=DcYLT37ImBY
https://x.com/mat_kelcey/status/886101319559335936
Tales from the Trenches: AI Disaster Stories (GDC Talk)
https://pdfs.semanticscholar.org/24c7/4c798100d69555ace06145bc1ba4fd6df35d.pdf
https://www.lesswrong.com/posts/5o3CxyvZ2XKawRB5w/machine-learning-and-unintended-consequences?commentId=tKdjcCZAtbE6vJq4v
Why Dolphins Are Deep Thinkers: The More We Study Dolphins, the Brighter They Turn out to Be
An evolved circuit, intrinsic in silicon, entwined with physics
https://pdfs.semanticscholar.org/0adf/aaeebbf36f34ac97770adc2f52619a5d45c6.pdf
Evolving Stable Strategies
CycleGAN: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
CycleGAN, a Master of Steganography
CAR: Learned Image Downscaling for Upscaling using Content Adaptive Resampler
Your TL;DR by an AI: A Deep Reinforced Model for Abstractive Summarization
A Deep Reinforced Model for Abstractive Summarization
Deep Reinforcement Learning Doesn’t Work Yet
Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari
Canonical ES Finds a Bug in Qbert
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
Trial without Error: Towards Safe Reinforcement Learning via Human Intervention
This Post Explains the Paper Trial without Error: Towards Safe RL With Human Intervention, Which Was Authored by William Saunders, Girish Sastry, Andreas Stuhlmüller and Owain Evans.
Is Deep Reinforcement Learning Really Superhuman on Atari? Leveling the playing field
R2D3: Making Efficient Use of Demonstrations to Solve Hard Exploration Problems
https://deepmind.google/discover/blog/making-efficient-use-of-demonstrations-to-solve-hard-exploration-problems/
Emergent Tool Use From Multi-Agent Autocurricula
Emergent Tool Use from Multi-Agent Interaction § Surprising behavior
Fine-Tuning GPT-2 from Human Preferences § Bugs can optimize for bad behavior
https://x.com/smingleigh/status/1060325665671692288
Why Tool AIs Want to Be Agent AIs
Surprisingly Turing-Complete
Feynman’s Maze-Running Story
Concrete Problems in AI Safety
https://arbital.com/p/edge_instantiation/
https://arbital.com/p/nearest_unblocked/
Adversarial Examples Are Not Bugs, They Are Features
Specification Gaming: the Flip Side of AI Ingenuity
Were Armed Kangaroos Added to a Military Combat Simulation Program?
https://www.reddit.com/r/MachineLearning/comments/76qua8/d_that_urban_legend_about_neural_nets_tanks/
https://news.ycombinator.com/item?id=15485538
https://news.ycombinator.com/item?id=36416895