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  1. gpt-2#gpt-2-1-5b

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  2. GPT-3: Language Models are Few-Shot Learners

  3. GPT-1: Improving Language Understanding with Unsupervised Learning

  4. Better Language Models and Their Implications

  5. gpt-2#training-gpt-2-poetry-prefix

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  6. gpt-2#gpt-2-345m

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  7. twdne#text

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  8. GPT-2 Folk Music

  9. GPT-2 Preference Learning for Music Generation

  10. A Very Unlikely Chess Game

  11. Update: Upgrading to 1.5B GPT-2, and adding 22 new subreddit-bots

  12. GPT-3 paper § Figure F.1: Four uncurated completions from a context suggesting the model compose a poem in the style of Wallace Stevens with the title ‘Shadows on the Way’

  13. GPT-3 Github JSON Dump Reformatted to Readable HTML

  14. OpenAI API Beta homepage

  15. AI Dungeon 2

  16. AI Dungeon 2: Dragon Model Upgrade—You Can Now Play AI Dungeon With One of the Most Powerful AI Models in the World.

  17. I’ve Been Testing the Largest of @OpenAI’s Models With AI Dungeon and Been Constantly Impressed at How Interesting and Dynamic the Characters Are, like This Queen, Long Thought to Be Dead, Hiding from Enemies and Not Happy about Me Prying into Her Personal Life.

  18. Excel_tabulate_v3_biz on Vimeo

  19. https://cdn.openai.com/API/English_Bash_Python.mp4

  20. The AI Channels Project

  21. ‘AI|Writer': an AI | Channels Project by @AndrewMayne Using the OpenAI API; 'AI|Writer’ Is an Experiment Using Artificial Intelligence to Create Simulated Hypothetical Correspondence With Famous Personalities, Both Real and Fictitious

  22. Hi @ID_AA_Carmack, This Is My Attempt to Learn How to Move General AI Forward. I Used OpenAI‘s GPT-3 Beta API to Incarnate a Version of You from the Future. I Am Shocked at GPT-3’s Responses, Especially How It Introduced You. All of the Bold Text Is 100% Generated by the Model

  23. OpenAI API Alchemy: Summarization

  24. ‘Simplify: Simple, Easy-To-Understand Explanations for Everything’, Chris Lu

  25. https://x.com/Wattenberger/status/1412480516268437512

  26. Introducing AI Dungeon Translate: AI Dungeon Players Can Now Translate Their Stories into Emojis by Just Clicking a Button. [ 🤔 💯 🤷‍♂️ 🤔 🤔 🤔 💯]

  27. OpenAI API Alchemy: Emoji Storytelling 🤖

  28. Multimodal Few-Shot Learning with Frozen Language Models

  29. OpenAI API Alchemy: Turn a Script into a Novel (And vice Versa)

  30. Say Goodbye to Painful Email Reading and Writing: Magic Email Is Your AI-Powered Email Assistant That Summarises Your Emails and Generates Professional Emails from Brief One-Line Descriptions. Get through All of Your Emails 5x Faster so You Can Free up More Time for Your Important Work.

  31. https://x.com/michaeltefula/status/1285505897108832257

  32. OpenAI API Alchemy: Smart Formatting and Code Creation

  33. I Made a Fully Functioning Search Engine on top of GPT-3. For Any Arbitrary Query, It Returns the Exact Answer AND the Corresponding URL. Look at the Entire Video. It’s MIND BLOWINGLY Good.

  34. Interactive Decomposition of Forecasting Questions Using GPT-3. All Questions Auto-Generated. Part of Our Work on Tools for Thought @oughtinc.

  35. ETHICS: Aligning AI With Shared Human Values

  36. ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

  37. https://x.com/jephjacques/status/1279537349974732800

  38. https://x.com/StarTrekAI

  39. Unlike That other Guy Who Just Wrote Silly Things and Lied to Pass Them off As the Work of an AI, I Actually did Get the GPT-3 Language Model to Generate New Seinfeld Scripts. Behold: 4 New Puffy Shirt Episodes. (The First 5 Lines Are Canon, the Rest New)

  40. Gpt-3-Experiments/examples at Master

  41. This Is the OpenAI API. It Makes Spookily Good Twitter Bots. 13⁄10 Would Retweet

  42. A 10,000 Year Warning

  43. Expert judgment on markers to deter inadvertent human intrusion into the Waste Isolation Pilot Plant

  44. Fiction by Neil Gaiman and Terry Pratchett by GPT-3

  45. GPT-3: An AI That's Eerily Good at Writing Almost Anything

  46. Elon Musk By Dr. Seuss (GPT-3)

  47. A Wild Adventure With GPT-3: Featuring Indian Mythology and Neruda

  48. Apropos of nothing

  49. https://www.youtube.com/watch?v=7Y5KsN6ehvk

  50. Love Letters, Written by a Toaster. The Poetic Power of Artificial Intelligence (GPT-3)

  51. Singular: Possible futures of the singularity

  52. An Essay about Artificial Intelligence, Emotional Intelligence, and Finding an Ending

  53. https://x.com/danielbigham/status/1295864369713209351

  54. AI Am I? (The New Aesthetic)

  55. https://x.com/TomerUllman/status/1363851329463087109

  56. https://www.reddit.com/r/aigreentext/

  57. Greentext Stories

  58. GPT-3 Generated These Color Scales, given Some Existing Scales and a Hue Name (Or Emoji‽) As a Prompt. Let That Sink In.

  59. Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color

  60. Shared understanding of color among sighted and blind adults

  61. https://x.com/sharifshameem/status/1282676454690451457

  62. I Just Built a functioning React App by Describing What I Wanted to GPT-3. I’m Still in Awe.

  63. I Built a Todo List App Simply by Describing It to GPT-3. It Generated the React Code for a Fully Functioning App within Seconds. I’m Becoming More Impressed and Aware of Its Capabilities Every Single Day.

  64. I Gave GPT-3 Access to Chrome With the Objective ‘Please Buy Me AirPods’…It Successfully Made It to the Product Page, but Got Sidetracked With Walmart’s Privacy Policy. Since Even a Simplified DOM Is Far Too Large for a Single Prompt, Multiple Prompts Are given Different Chunks of the DOM, Each Generating Their Own ‘Interaction’. Another Prompt Then Takes All the Proposed Interactions and Selects the Best One, Sort of like a Tournament Bracket. For More Complex Web Pages, the Time It Takes to Generate an Action Scales at 𝒪(Log n) With the Size of the DOM—Really Fast! It Also Gets around Token Limits, so You Could Technically Process an Infinitely Large DOM!

  65. First Work With GPT-3, I Asked It to Draw an Image. I Gave It Seed SVG Code and Asked It to Generate an SVG Code by Itself. Turns out It Drew Something Resembling a Floppy Disk.

  66. GPT-3 Does The Work™️ on Generating SVG Charts, With a Quick Web App I Built With @billyjeanbillyj. With a Short Sentence Describing What You Want to Plot, Its Able to Generate Charts With Titles, Labels and Legends from about a Dozen Primed Examples.It Works by Compiling the Sentences to Vega-Lite (@vega_vis) by @arvindsatya1, @kanitw, @domoritz, and Jeffery Heer. Vega a High Level Grammar of Interactive Graphics Built for Exploratory Data Visualization.

  67. Starting the Day With a Chart Building Demo. Primed GPT-3 With Chart.js Scripts to Generate the Below.

  68. After Many Hours of Retraining My Brain to Operate in This "Priming" Approach, I Also Now Have a Sick GPT-3 Demo: English to LaTeX Equations! I’m Simultaneously Impressed by Its Coherence and Amused by Its Brittleness—Watch Me Test the Fundamental Theorem of Calculus.

  69. GPT-3 Does The Work™ on Some Business Analyst SQL Queries given Quite a Few Examples from (Https://techbeamers.com/sql-Query-Questions-Answers-For-Practice/). What’s Wildest Is That It Knows a Few Functions like SUBSTR given No Examples in That Syntax. More to Come Re: GPT-3 for Automating Data Analytics Tasks.

  70. Automating My Job With GPT-3: Using GPT-3 Instruct to Generate Database-Ready SQL to Answer Business Questions

  71. Who Models the Models That Model Models? An Exploration of GPT-3’s In-Context Model Fitting Ability

  72. https://www.autoregex.xyz/

  73. This Changes Everything. :Exploding_head: With GPT-3, I Built a Figma Plugin to Design for You. I Call It ‘Designer’

  74. https://web.archive.org/web/20200727092603/https://spronkoid.github.io/recycling/Recyclingisascam.html

  75. https://bramses.notion.site/ERB-of-History-GPT-3-Bot-784e99b7fea0462f95489d74a568c4ad

  76. Design a Role-Playing Game Using 200 Words or Less.

  77. I Was Thinking of Using GPT-3 to Generate ‘200 Word RPGs’ (Tiny Complete Games) but I’m Getting Quite Distracted Watching It play 200 Word RPG Challenge Entries. It Didn’t Account for the Tokens but It Got the General Idea without Any Example Gameplay in the Prompt.

  78. Recommendations For Anything You Want

  79. Predictability and Surprise in Large Generative Models

  80. Turns out GPT-3 Can Do Vision Too 😉 Built an Ingredient Parser: Take a Pic of Any Nutrition Label (Google to Extract Text), and GPT-3 Will Identify Ingredients, Find an Emoji, Determine If It’s Unhealthy, and Give a Definition 🤯

  81. The Best Kept Secret about OpenAI’s GPT-3 – @AndrewMayne

  82. Image GPT (iGPT): We find that, just as a large transformer model trained on language can generate coherent text, the same exact model trained on pixel sequences can generate coherent image completions and samples

  83. The Scaling Hypothesis

  84. Evolution as Backstop for Reinforcement Learning

  85. Decision Transformer: Reinforcement Learning via Sequence Modeling

  86. The Aleph: Borgean Fantastic Hyperreality Revisited by GPT-3

  87. The Unreasonable Effectiveness of Data

  88. RNN Metadata for Mimicking Author Style

  89. Crowdsourcing The Best GPT-2-1.5b Poetry

  90. Passages from the Life of a Philosopher § Chapter 5: ‘Difference Engine No. 1’

  91. GPT-2 Neural Network Poetry

  92. Mechanical Sympathy: Understanding the Hardware Makes You a Better Developer

  93. scaling-hypothesis#meta-learning

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  94. https://x.com/karpathy/status/1273788774422441984

  95. https://gptprompts.wikidot.com/linguistics:word-in-context

  96. How Many Data Points is a Prompt Worth?

  97. Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm

  98. How Can We Know What Language Models Know?

  99. Prefix-Tuning: Optimizing Continuous Prompts for Generation

  100. Calibrate Before Use: Improving Few-Shot Performance of Language Models

  101. Technology Forecasting: The Garden of Forking Paths

  102. ‘Lizardman survey constant’ directory

  103. Sample #1353

  104. I Asked GPT-3 about Xinjiang and It Broke…The Pro-CCP Responses Seem to Have Worse English, like including ‘the’ in ‘the Stability Maintenance’. Unnecessary Articles Are a Tic of ESL Speakers. The Topic Seems to Prompt GPT to Draw from Either Western or Chinese State Media Sources, With the Politics That Come With It.

  105. Codex: Evaluating Large Language Models Trained on Code: Figure 14: When the Prompt Includes Subtle Bugs, Codex Tends to Produce Worse Code Than It Is Capable of Producing. This Gap Increases With Model Size. Including an Instruction to Write Correct Code Helps a Little but Does Not Fix the Problem. Even With No Examples in the Context, Codex Produces Substantially Worse Code Than It Is Capable Of.

  106. Surprisingly Turing-Complete

  107. Adversarial Reprogramming of Neural Networks

  108. Adversarial Reprogramming of Text Classification Neural Networks

  109. Deep Learning: Classics and Trends: Language Models Are Few-Shot Learners

  110. A Systematic Characterization of Sampling Algorithms for Open-ended Language Generation

  111. Trading Off Diversity and Quality in Natural Language Generation

  112. Scarecrow: A Framework for Scrutinizing Machine Text

  113. The Curious Case of Neural Text Degeneration

  114. Towards a Human-like Open-Domain Chatbot

  115. Language GANs Falling Short

  116. Six Challenges for Neural Machine Translation

  117. Analyzing Uncertainty in Neural Machine Translation

  118. Mamba: Linear-Time Sequence Modeling with Selective State Spaces

  119. https://x.com/mayfer/status/1732269798934106133

  120. https://web.media.mit.edu/~minsky/papers/Why%20programming%20is--.html

  121. ‘inner monologue (AI)’ directory

  122. Seems to work

  123. Teaching GPT-3 to do a brute force 'for loop' checking answers also seems to work

  124. Program Synthesis with Large Language Models

  125. I found that getting GPT-3 to add its own "internal monologue" in parentheses to be a helpful strategy…

  126. How to Dramatically Improve the Reasoning Ability of GPT-3

  127. Teaching GPT-3 to Identify Nonsense

  128. GPT-J-6B: 6B JAX-Based Transformer

  129. https://www.reddit.com/r/AIDungeon/comments/i1qhg0/the_dragon_ai_just_got_worse/

  130. I’ve Noticed a Number of People Using AI Dungeon to Test GPT-3’s Abilities. While It’s a Great Way to See How GPT-3 Can Power an Interesting Application, It’s a Poor Test of GPT-3’s Abilities in General. The First Generation of Any Custom Prompt Is Actually GPT-2.

  131. https://x.com/nickwalton00/status/1289970219855708160

  132. Controlling GPT-3 With Logit Bias

  133. Evaluating Different Fewshot Description Prompts on GPT-3

  134. The ‘AI Dungeons’ Dragon Model Is Heavily Path Dependent (Testing GPT-3 on Ethics)

  135. Aurora / AuroraPurgatio

  136. gpt-2#improvements

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  137. ‘self-attention’ directory

  138. T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

  139. GPT-2 Preference Learning for Music Generation § Optimization by Backprop, Not Blackbox

  140. Progressive Generation of Long Text

  141. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

  142. Co-Writing Screenplays and Theatre Scripts with Language Models (Dramatron): An Evaluation by Industry Professionals

  143. Scaling Language Models: Methods, Analysis & Insights from Training Gopher § Table A40: Conversations Can Create the Illusion of Creativity

  144. Announcing GPT-NeoX-20B

  145. https://gist.github.com/moyix/ca4091f16f0b5011bfa8f3f97f705a0d

  146. LaMDA: Language Models for Dialog Applications

  147. https://wordcraft-writers-workshop.appspot.com/stories/diana-hamilton

  148. Most Language Models can be Poets too: An AI Writing Assistant and Constrained Text Generation Studio

  149. Help me write a poem: Instruction Tuning as a Vehicle for Collaborative Poetry Writing (CoPoet)

  150. I Have a Joke but It’s GPT-3 Generated.

  151. I Think I Have Had Enough of These Jokes. Dear GPT-3 I Command You to Generate All Possible Jokes of This Type. GPT-3: Your Wish Is My Command:

  152. https://x.com/wowitsmrinal/status/1287175391040290816

  153. Models In a Spelling Bee: Language Models Implicitly Learn the Character Composition of Tokens

  154. CLIP: Connecting Text and Images: We’re introducing a neural network called CLIP which efficiently learns visual concepts from natural language supervision. CLIP can be applied to any visual classification benchmark by simply providing the names of the visual categories to be recognized, similar to the ‘zero-shot’ capabilities of GPT-2 and GPT-3

  155. DALL·E 2: Hierarchical Text-Conditional Image Generation with CLIP Latents § 7. Limitations and Risks

  156. Character-Aware Models Improve Visual Text Rendering

  157. What’s AGI, and Why Are AI Experts Skeptical? ChatGPT and other bots have revived conversations on artificial general intelligence. Scientists say algorithms won’t surpass you any time soon

  158. GPT-3 vs Water Cooler Trivia participants: A Human vs Robot Showdown

  159. There Once Was a Really Bad Poet, It Was Automated but You Didn’t Know It

  160. https://x.com/zswitten/status/1390045960663797764

  161. https://www.reddit.com/r/slatestarcodex/comments/1201v68/10word_quote_a_short_and_simple_failure_mode_of/jdjsx43/

  162. LMentry: A Language Model Benchmark of Elementary Language Tasks

  163. https://amistrongeryet.substack.com/p/can-ai-do-my-job

  164. https://amistrongeryet.substack.com/p/gpt-4-capabilities

  165. BPE Blues

  166. BPE Blues+

  167. GPT-2 Folk Music § Spaceless Model

  168. Commas vs Integers

  169. Math: OpenAI API Can Do Some Math out of the Gate, but Most Math It Seems It Has to Learn. Many Times, the Numbers That It Spits out Are Just Random. However, including Different Priming Prompts Can Result in Decent Results.

  170. Analysing Mathematical Reasoning Abilities of Neural Models

  171. Vincent-163/transformer-Arithmetic

  172. Generative Language Modeling for Automated Theorem Proving

  173. Investigating the Limitations of the Transformers with Simple Arithmetic Tasks

  174. Deep-speare: A Joint Neural Model of Poetic Language, Meter and Rhyme

  175. You’re Right, Spaces Make All the Difference! Copycat Is Toast! (Except for the Last One :-) (GPT-3 Output in Red).

  176. Can GPT-3 Make Analogies?

  177. https://x.com/SteveMoraco/status/1293302692832411649

  178. https://x.com/nutanc/status/1293387692755939331

  179. It Just so Happens I Am Watching a 5-Year-Old Right Now. Here Are the Results! / / Q: If Abc Goes to Abd, What Does Pqr Go To? / A: S / / Q: If Abc Goes to Abd, What Does Ppqqrr Go To? / A: Ss / / Q: If Abc Goes to Abd, What Does Mrrjjj Go To? / A: Kkk / Q: If Abc Goes to Abd, What Does Xyz Go To? / A: Now I Know My ABCs, next Time Won’t You Sing With Me! / / Q: If Axbxcx Goes to Abc, What Does Xpxqxr Go To? / A: S / / Hope This Enlightens Someone

  180. Generative Language Modeling for Automated Theorem Proving § Experiments

  181. BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance

  182. On Seeing Through and Unseeing: The Hacker Mindset

  183. Tokens Are Definitely Shorter Than English, but the Performance Even Worse. Getting It to Explain Its Thinking, It Clearly Can’t Tell at All Which Sentences/words Sound the Same, Which Is Odd, Since Homonyms Tend to Have the Same Letters in Russian…On the Other Hand Strength of the Model Definitely Not As Good outside of English.

  184. Human: Did You Know That There Is No Country in Africa That Starts With the Lett...

  185. The Bitter Lesson

  186. BPE-Dropout: Simple and Effective Subword Regularization

  187. Unigram LM: Byte Pair Encoding is Suboptimal for Language Model Pretraining

  188. https://ndingwall.github.io/blog/tokenization

  189. CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation

  190. Charformer: Fast Character Transformers via Gradient-based Subword Tokenization

  191. ByT5: Towards a token-free future with pre-trained byte-to-byte models

  192. MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers

  193. Towards End-to-End In-Image Neural Machine Translation

  194. PIXEL: Language Modeling with Pixels

  195. Perceiver: General Perception with Iterative Attention

  196. One Big Net For Everything

  197. face#faq

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  198. The Value Equivalence Principle for Model-Based Reinforcement Learning

  199. RL agents Implicitly Learning Human Preferences

  200. What Is The Morning Writing Effect?

  201. Revealing Persona Biases in Dialogue Systems

  202. The Basic AI Drives

  203. The Scaling Hypothesis § It From Byte

  204. EleutherAI Discord Server

  205. https://platform.openai.com/terms-of-use

  206. https://openai.com/index/gpt-4v-system-card/

  207. https://openai.com/index/hello-gpt-4o/

  208. Inverse Scaling Prize: Second Round Winners

  209. How ‘Honest’ Is GPT-3?

  210. epigram#tom-swifties

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  211. Humans Who Are Not Concentrating Are Not General Intelligences

  212. Better Babblers

  213. Using GPT-3 to Explain Jokes

  214. Computing Machinery And Intelligence

  215. GPT-3: Its Nature, Scope, Limits, and Consequences

  216. https://www.theintrinsicperspective.com/p/the-banality-of-chatgpt

  217. Prothalamion by Edmund Spenser

  218. Shakespeare's Sonnets

  219. Playing #chess With GPT-3. Built Using Chess.js, Chessboard.js and @OpenAI’s GPT-3. White Is Me, Black Is GPT-3. GPT-3 Went for the Capture First and Did a Castling Move. Amazing!

  220. On the Sizes of OpenAI API Models: ...Ada, Babbage, Curie and Davinci Line up Closely With 350M, 1.3B, 6.7B, and 175B Respectively.

  221. Swifties 3: The Race Is Not To The Swifty

  222. Navy Seal Copypasta

  223. TIFU by trying to make a salad in the microwave

  224. https://www.reddit.com/r/NavySealCopypasta/

  225. https://www.reddit.com/r/GPT3/comments/ukbba5/the_rickrollian_language_of_william_shakespeare/

  226. https://www.reddit.com/r/mlscaling/comments/pa4h0c/ai_can_write_in_english_now_its_learning_other/ha36d60/

  227. https://www.reddit.com/r/GPT3/comments/v8xsy9/artificial_neural_networks_are_making_strides/ibv9nhm/

  228. https://x.com/MagicRealismBot/status/1273659023926022144

  229. 410 Deleted by Author

  230. ChatGPT is fun, but it is not funny! Humor is still challenging Large Language Models

  231. https://slatestarscratchpad.tumblr.com/post/621298010168705024/slatestarscratchpad-the-ai-projects-ive-found

  232. Delivering Real-Time AI in the Palm of Your Hand

  233. Politeness Transfer: A Tag and Generate Approach

  234. rnn-metadata#success

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  235. https://x.com/MalenaOhl/status/1298816889569914881

  236. CorentinJ/Real-Time-Voice-Cloning: Clone a Voice in 5 Seconds to Generate Arbitrary Speech in Real-Time

  237. Rosebud AI: Build Games at the Speed of Thought. AI Powered Game Development

  238. I Used @OpenAI GPT-3 to Convert Sentences to a Gentler and Non-Confrontational Tone. The Initial Four Input/output Pairs Are Training Examples, and Then I Tested It With Three New Inputs:

  239. Apparently ‘what ho’ is a corruption of…

  240. https://x.com/balzarot/status/1278213982663426048

  241. But for Me, It Was Tuesday

  242. To Be Fair, You Have To Have a Very High IQ to Understand Rick and Morty

  243. Tendies Stories

  244. https://www.reddit.com/r/rational/comments/poixjd/review_the_fall_of_doc_future/hcy7owh/

  245. https://x.com/allgebrah/status/1282438217401339907

  246. https://x.com/allgebrah/status/1282483394484502534

  247. Taking the Hobbits to Isengard

  248. They'Re Taking the Hobbits to Isengard

  249. The Ents’ Marching Song

  250. Back From Yet Another Globetrotting Adventure, Indiana Jones Checks His Mail And Discovers That His Bid For Tenure Has Been Denied

  251. epigram#less-known-mi6-licenses

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  252. Jukebox: We’re introducing Jukebox, a neural net that generates music, including rudimentary singing, as raw audio in a variety of genres and artist styles. We’re releasing the model weights and code, along with a tool to explore the generated samples.

  253. ‘The Universe Is a Glitch’ (AI-Driven Music Video)

  254. Artbreeder

  255. Meditations on Moloch

  256. [All in green went my love riding]

  257. R-P-O-P-H-E-S-S-A-G-R (Poem + Analysis)

  258. https://x.com/flantz/status/1286380760585375744

  259. Greece, IV

  260. A Psalm of Life by Henry Wadsworth Longfellow

  261. Ode on Intimations of Immortality from Recollections of Early Childhood by William Wordsworth

  262. Don Juan (Byron, Unsourced)/Canto the Third

  263. https://papergains.co/pdfs/Transformer_Poetry-978-1-7341647-0-1.pdf#page=3

  264. https://x.com/OwainEvans_UK/status/1292190171237175298

  265. Poetry Will Not Optimize, or What Is Literature to AI? § Pg7

  266. Nevermore. / Made With @midjourney / @images_ai ✨ / #AIart #aiartcommunity #artwork #Artists / #artist #AIartwork #generativeart #art

  267. https://x.com/midjourney

  268. ‘CLIP’ directory

  269. ‘diffusion model’ directory

  270. https://x.com/zoink/status/1289076947629125632

  271. Part 1: AI that writes—GPT-3: a big step forward

  272. https://sevensecularsermons.org/about/

  273. https://openai.com/blog/gpt-3-edit-insert/

  274. https://www.reddit.com/r/promptengineers/comments/thxnsx/from_gpt3s_new_edit_mode_it_can_fill_in_acrostic/

  275. Acrostic Poem Examples: Learn to Make Your Own Name or Word Poetry With These Acrostic Poem Examples and a Handy Template

  276. https://www.lesswrong.com/posts/W3DbNmuMJLWRtE5ny/predictions-for-gpt-n?commentId=J22o3qPeYSpc2M2ib

  277. 21st Century Chinese Poetry

  278. https://www.reddit.com/r/MachineLearning/comments/1135tir/d_glm_130b_chineseenglish_bilingual_model/

  279. ‘instruct-tuning LLMs’ directory

  280. Deep reinforcement learning from human preferences

  281. The First Sally (A), Or, Trurl’s Electronic Bard

  282. The First Sally (A), Or, Trurl’s Electronic Bard § Love And Tensor Algebra

  283. Seduced, Shaggy Samson Snored: The Fictional Machine That Generated Poems, and the Real People Who Had to Translate Them

  284. https://x.com/emollick/status/1626316207229132800

  285. Looking for Grammar in All the Right Places

  286. Interpreting GPT: the Logit Lens

  287. Steve Omohundro on GPT-3

  288. Dare To Be Stupid

  289. https://tvtropes.org/pmwiki/pmwiki.php/Platform/FimfictionDotNet

  290. Friendship Is Optimal

  291. AI Writes My Little Pony Fanfiction (GPT-3)

  292. Harry Potter and the Methods of Rationality

  293. https://hpmor.com/chapter/16

  294. http://www.simpsoncrazy.com/scripts/last-exit

  295. This Waifu Does Not Exist § GPT-3

  296. On the New Forcers of Conscience under the Long Parliament

  297. https://www.reddit.com/r/GPT3/comments/ith31k/have_bad_analogies_been_tried_with_gpt3_some/

  298. Why GPT-3 Matters

  299. Building AGI Using Language Models

  300. https://towardsdatascience.com/gpt-3-creative-potential-of-nlp-d5ccae16c1ab

  301. https://www.lesswrong.com/posts/Mzrs4MSi58ujBLbBG/you-can-probably-amplify-gpt3-directly

  302. Machinamenta: Regarding GPT-3's Faculties

  303. Are we in an AI overhang?

  304. OpenAI’s Latest Breakthrough Is Astonishingly Powerful, but Still Fighting Its Flaws

  305. Computers Are Getting Closer to Passing the Turing Test

  306. https://www.reddit.com/r/slatestarcodex/comments/hrx2id/a_collection_of_amazing_things_gpt3_has_done/fy7jl0y/

  307. https://x.com/nikillinit/status/1289281944421711878

  308. Starting a Business Around GPT-3 Is a Bad Idea

  309. Laws of Tech: Commoditize Your Complement

  310. https://www.patreon.com/posts/39864473

  311. GPT-3: Using Fiction to Demonstrate How Prompts Impact Output Quality

  312. https://medium.com/@marcinkraszewski/gpt-3-project-ideas-with-code-5940c275bc41

  313. Context Stuffing

  314. How I Used GPT-3 to Hit Hacker News Front Page 5 times in 3 Weeks

  315. TLDR: I Go from Wanting a Machine Learning Model to Getting That Trained Model, without Actually Having a Dataset.

  316. Want To Reduce Labeling Cost? GPT-3 Can Help

  317. https://www.lesswrong.com/posts/4JeAoTrAuByXGw6zm/updated-how-does-gpt2-s-training-corpus-capture-internet

  318. Thefirstaibook

  319. The Arcadian Cantos- A Poem without an Author- 1st Draft

  320. Generative Models are Unsupervised Predictors of Page Quality: A Colossal-Scale Study

  321. MMLU: Measuring Massive Multitask Language Understanding

  322. What Grades Can AI Get In College?

  323. Musings on Typicality

  324. Can GPT-3 Pass a Writer’s Turing Test?

  325. https://x.com/julianharris/status/1421008325785890825

  326. Computers Learning Humor Is No Joke

  327. Extrapolating to Unnatural Language Processing With GPT-3’s In-Context Learning: The Good, the Bad, and the Mysterious

  328. Pen.el

  329. Exploring GPT-3

  330. Post-History Is Written by the Martyrs

  331. https://x.com/goodside

  332. https://www.reddit.com/r/slatestarcodex/comments/hfouw5/gpt3_for_creative_fiction_poetry_dialogue_puns/

  333. https://www.reddit.com/r/MediaSynthesis/comments/hfoulh/gpt3_for_creative_fiction_poetry_dialogue_puns/

  334. https://www.reddit.com/r/HPMOR/comments/hgw2zq/gpt3_neural_net_completions_of_mor_chapter_16/

  335. https://www.reddit.com/r/SubSimulatorGPT2Meta/comments/hl0x18/gwerns_post_on_gpt3_has_some_gold/

  336. GPT-3 Fiction Samples

  337. https://news.ycombinator.com/item?id=23722635

  338. https://news.ycombinator.com/item?id=35633316

  339. Wikipedia Bibliography:

    1. Moravec’s paradox

    2. Ryan North

    3. Long-term nuclear waste warning messages

    4. Waste Isolation Pilot Plant

    5. Six Degrees of Kevin Bacon  :

    6. Kevin Bacon  :

    7. Web colors  :

    8. JSX (JavaScript)  :

    9. The 7 Basic Plots  :

    10. E.T. the Extra-Terrestrial  :

    11. The Library of Babel

    12. The Book of Sand  :

    13. Markov chain

    14. Charles Babbage

    15. Tacit knowledge

    16. Peter Watts (author)

    17. Cooperative principle

    18. Chekhov's gun

    19. Interactive fiction

    20. Bag-of-words model

    21. Common Crawl

    22. EPUB  :

    23. XHTML  :

    24. Jabberwocky

    25. Witch Doctor (song)  :

    26. Marvin Minsky

    27. Spice and Wolf

    28. Beam search

    29. Confabulation

    30. Byte pair encoding

    31. Synesthesia

    32. Analytic language  :

    33. Synthetic language  :

    34. Scientific notation

    35. 2012 phenomenon  :

    36. International Phonetic Alphabet

    37. ROT13  :

    38. Copycat (software)

    39. Cyrillic script

    40. UTF-8  :

    41. Language game (philosophy)  :

    42. Memento (film)

    43. Mimetic desire  :

    44. Tulpa § 21st century  :

    45. Kevin Roose

    46. Concrete poetry

    47. Green Eggs and Ham § Writing and release  :

    48. Wisdom literature

    49. Shoggoth  :

    50. Turing test

    51. Forth Bridge  :

    52. Elegy Written in a Country Churchyard

    53. To His Coy Mistress  :

    54. Shakespeare's sonnets  :

    55. Ode on a Grecian Urn  :

    56. The Waste Land

    57. Tom Swifty

    58. Copypasta  :

    59. Ravindra Jadeja  :

    60. Dad joke  :

    61. Sun bear  :

    62. Neural style transfer  :

    63. H. P. Lovecraft

    64. Kurt Vonnegut

    65. Franz Kafka

    66. Anne McCaffrey  :

    67. Steve Jobs

    68. Job (biblical figure)  :

    69. Book of Job

    70. Apple Inc

    71. Book of Job § Two speeches by God  :

    72. Declare  :

    73. King James Version  :

    74. Tim Cook

    75. IPhone 7 § Headphone jack controversy  :

    76. MacBook Pro § Keyboard reliability  :

    77. IPhone

    78. IPhone 4 § Antenna  :

    79. Bruce Tognazzini  :

    80. David Pogue  :

    81. Hacker news

    82. MacBook  :

    83. IMac  :

    84. IPad  :

    85. App Store (Apple)  :

    86. Human interface guidelines  :

    87. Apple Industrial Design Group  :

    88. Home screen  :

    89. Messages (Apple)  :

    90. Apple Watch  :

    91. FaceTime

    92. IOS

    93. Apple IIe  :

    94. Mackintosh  :

    95. Zilog Z80  :

    96. Dropbox

    97. Fake news

    98. Hackintosh  :

    99. Blue screen of death

    100. Screen of death § Known screens of death  :

    101. Xbox 360 technical problems

    102. Windows Registry  :

    103. ICloud  :

    104. Flash memory

    105. Apple TV+

    106. History of podcasting § Apple adds podcasts to iTunes  :

    107. Pegatron  :

    108. Steve Wozniak

    109. Homebrew Computer Club  :

    110. Foxconn  :

    111. Apple Store  :

    112. Fiscal year

    113. Cupertino, California  :

    114. Apple Inc. § Logo  :

    115. Instagram

    116. KakaoTalk  :

    117. Ambrose Bierce  :

    118. The Devil's Dictionary  :

    119. Street Fighter (1994 film)  :

    120. Bill & Ted's Excellent Adventure

    121. Snowclone

    122. M. Bison  :

    123. Chun-Li  :

    124. Jason Voorhees  :

    125. Wednesday § Etymology  :

    126. Rick and Morty

    127. Narodnaya Volya  :

    128. Ivan Turgenev  :

    129. Fathers and Sons (novel)  :

    130. The Last of Us Part II  :

    131. Anita Sarkeesian  :

    132. Neil Druckmann  :

    133. Worm (web serial)

    134. Portal 2  :

    135. My Life as a Teenage Robot  :

    136. Dan DiDio  :

    137. Law & Order: Special Victims Unit  :

    138. The Avengers (2012 film)  :

    139. Mr. Robot  :

    140. Cuphead  :

    141. Red Orchestra (espionage)  :

    142. Half-Life 2  :

    143. Jerry Sandusky  :

    144. Greg Egan

    145. Permutation City

    146. Major-General's Song  :

    147. Frank Lantz  :

    148. Universal Paperclips  :

    149. Allen Ginsberg

    150. E. E. Cummings  :

    151. Lord Byron

    152. Henry Wadsworth Longfellow  :

    153. William Wordsworth

    154. Don Juan (poem)  :

    155. Oranges and Lemons  :

    156. Poetry Foundation

    157. Jeff Hawkins  :

    158. The Lorax  :

    159. Dr. Seuss  :

    160. Common metre  :

    161. Acrostic

    162. Robert Browning

    163. Caliban upon Setebos

    164. Tanka  :

    165. Fujiwara no Teika

    166. Stanisław Lem

    167. The Cyberiad

    168. Steve Omohundro  :

    169. Donna Dubinsky  :

    170. Archive of Our Own

    171. Eliezer Yudkowsky

    172. The Illuminatus! Trilogy

    173. Robert Shea  :

    174. Robert Anton Wilson  :

    175. Infinite monkey theorem  :

    176. Last Exit to Springfield  :

    177. Crossover (fiction)  :

    178. Diamond Sutra  :