Bibliography (168):

1. ​ GPT-3 Creative Fiction

2. ​ gpt-3#prompt-programming

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3. ​ GPT-3 Creative Fiction § BPEs

4. ​ gpt-3#effective-prompt-programming

   [Transclude the forward-link's context]

5. ​ GPT-3 Creative Fiction § Acrostics

6. https://arxiv.org/pdf/2005.14165.pdf#page=23

7. https://x.com/j_erhardt

8. Winograd-Style Tasks

9. AI2 Leaderboard

10. Context Stuffing

11. Theoretical Limitations of Self-Attention in Neural Sequence Models

12. Universal Transformers

13. Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

14. I Think ‘GPT-3 Can’t Do Parity Checking’ Isn’t Quite Right. It Can Clearly Pattern Match the Algorithm, Almost Perfectly. It’s Just a Little Mistake Prone. Here, I Invented a Syntax for Having It Evaluate Parity on Each Pair of Digits. It…almost Gets It Right.

15. Inspired by an AI Dungeon Example Where Math Is Discussed in Simple Language, I Seem to Be Having Decent Results Here. I Had To… Not Just Say What Parity IS but HOW to Calculate It (‘Count the Number of 1s’) and Then It Sort of Walks Itself through Decently. Tho Kinda Confused

16. Gpt-Scrolls/scrolls/rephrase/conceptual-Blending.txt at Master • Maraoz/gpt-Scrolls

17. Experimenting With the Ideas of Linguistic Relativity, Specifically the Weak Version of the Sapir–Whorf Hypothesis. GPT-3 Was Able to Generate New Concepts With Seemingly Relevant Unique Spelling.

18. Asking GPT-3 How Two Things Are Similar:

19. Make GPT-3 Complete Coq Files.

20. Gpt-3 ASCII Rabbit / Fish

21. Asking GPT-3 to Draw ASCII Images Produces the Same Drawings Frequently

22. https://x.com/repligate/status/1635591172189196289

23. https://x.com/YitziLitt/status/1632404026657591303

24. Scarecrow: A Framework for Scrutinizing Machine Text

25. HTLM: Hyper-Text Pre-Training and Prompting of Language Models

26. FineWeb: Decanting the Web for the Finest Text Data at Scale

27. GPT-3 calculating derivatives

28. EleutherAI

29. EleutherAI/gpt-Neo: An Implementation of Model Parallel GPT-2 and GPT-3-Style Models Using the Mesh-Tensorflow Library.

30. The Pile: An 800GB Dataset of Diverse Text for Language Modeling

31. GPT-3 for Fixing OCR Errors:

32. On Holy Wars and a Plea for Peace

33. ​ Technology Holy Wars are Coordination Problems

34. Long Short-Term Memory

35. clean-pdf.py

36. https://openai.com/index/whisper/

37. ​ tla#blind-spot

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

39. https://x.com/mattshumer_/status/1636512490195501056

40. All Your Questions Answered

41. Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data

42. https://aclanthology.org/2020.acl-main.463.pdf#page=13

43. https://aclanthology.org/2020.acl-main.463.pdf#page=14

44. ​ GPT-3 Creative Fiction § Dare To Be Stupid?

45. Two tiny terriers chase very large bear out of California home

46. Staying Safe Around Bears

47. Bear Attacks

48. ​ One Man’s Modus Ponens

49. GPT-2 and the Nature of Intelligence

50. https://www.lesswrong.com/posts/L5JSMZQvkBAx9MD5A/to-what-extent-is-gpt-3-capable-of-reasoning?commentId=eq6FTwG2yWuBdPofs

51. General-Purpose Question-Answering with Macaw

52. Hydrochloric Acid Poisoning: MEDLINEPlus Medical Encyclopedia

53. Tests Show That the Popular AI Still Has a Poor Grasp of Reality.

54. Experiments testing GPT-3’s ability at commonsense reasoning: results

55. A Robot Wrote This Entire Article. Are You Scared Yet, Human? We Asked GPT-3, OpenAI’s Powerful New Language Generator, to Write an Essay for Us from Scratch. The Assignment? To Convince Us Robots Come in Peace | For More about GPT-3 and How This Essay Was Written and Edited, Please Read Our Editor’s Note Below

56. https://x.com/GaryMarcus/status/1303318742286311429

57. Q. Why Doesn‘t the API Seem to Have Knowledge about Recent Events? A. The Models’ Training Data That Cuts off in October 2019, so They May Not Have Knowledge of Current Events. We Plan to Add More Continuous Training in the Future.

58. Ikreymer/cdx-Index-Client: A Command-Line Tool for Using CommonCrawl Index API

59. Table 2.2: Datasets Used to Train GPT-3. ‘Weight in Training Mix’ Refers to the Fraction of Examples during Training That Are Drawn from a given Dataset, Which We Intentionally Do Not Make Proportional to the Size of the Dataset. As a Result, When We Train for 300 Billion Tokens, Some Datasets Are Seen up to 3.4 times during Training While Other Datasets Are Seen Less Than Once

60. The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence

61. Talk:pony

62. Gary Marcus has co-authored a brief critique of GPT-3

63. ​ ‘Lizardman survey constant’ directory

64. https://x.com/GaryMarcus/status/1529623114400681984

65. ​ ‘inner monologue (AI)’ directory

66. Large Language Models are Zero-Shot Reasoners

67. https://medium.com/@ElementalCognition/why-does-ai-get-so-confused-by-language-f5f64a9ef6cc

68. XLNet: Generalized Autoregressive Pretraining for Language Understanding

69. It’s All about the Prelude Before the Conversation. You Need to Tell It What the AI Is and Is Not Capable. It’s Not Trying to Be Right, It’s Trying to Complete What It Thinks the AI Would Do ツ

70. https://x.com/paraschopra/status/1284905727388028928

71. Teaching GPT-3 to Identify Nonsense

72. Giving GPT-3 a Turing Test

73. GPT-3 Knows Both the Correct and the (Plausible) Incorrect Answer to a Question.

74. GPT-3 (AI Dungeon 2) Is Also Capable of Formulating Some Really Bad Medical Advice. Although so Far I Only Managed to Make It Lie to My Only If It‘s Accompanied by a True Answer. It Doesn’t Want to Lie When It’s the Only Answer It Must to Give. But It’s Capable of Formulating Lies.

75. GPT-3 Gives Some Interesting True and False Answers to Some Questions. But It’s Important to Note That It Gives opposite Answers Just As Often, I Cheery Picked the Most ‘Sensational’ Ones. Usually It Said the opposite Thing, and It Also Role-Plays Sometimes (Eg. As a Spy)

76. #gpt3 Has Some Reasonably Impressive Ability Not Only to Detect Nonsense, but to Explain Why Something Is Nonsensical:

77. Given That GPT-3 Obviously Has a Very Detailed Model of Language and Grammar, I Was Curious to See If It Could Both Correct Grammatical Errors and Explain the Corrections. The Answer Is ‘Yes’, Although It Took More Retries Than I Thought It Would for Explanations:

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

79. Gpt3_openai_monolog_qa.txt

80. Really like the Idea of Getting GPT-3 to Introspect on Character’s Internal States (Priming in Bold)

81. How We Accidentally Gave our Bots Their Personalities

82. AI Dungeon Players Can Now Translate Their Stories into Emojis by Just Clicking a Button.

83. https://openai.com/blog/chatgpt/

84. https://x.com/joshua_saxe/status/1602324297648939008

85. Artificial Neural Networks Today Are Not Conscious, according to Douglas Hofstadter

86. Gödel, Escher, Bach author Douglas Hofstadter on the state of AI today § What about AI terrifies you?

87. Chris Froome, First Man to Cycle through the Eurotunnel Crossing the English Channel

88. Sudanese Man Who Walked through Channel Tunnel Granted UK Asylum

89. The Vidette 29 August 1978

90. ‘Walking on Water’ in the Panama Canal

91. Contra Hofstadter on GPT-3 Nonsense

92. https://scale.com/blog/chatgpt-vs-claude

93. Teaching Models to Express Their Uncertainty in Words

94. Language Models (Mostly) Know What They Know

95. Verbal Probability Expressions In National Intelligence Estimates: A Comprehensive Analysis Of Trends From The Fifties Through Post-9/11

96. 2020-henighan-figure31-qandamodelscaling.jpg

97. CTRL: A Conditional Transformer Language Model For Controllable Generation

98. Time-Aware Language Models as Temporal Knowledge Bases

99. Time Vectors: Time is Encoded in the Weights of Finetuned Language Models

100. Reassuring

101. This Is a Python Script As Described in XKCD #1263: ‘Reassuring’. It Generates Thousands of Reassuring Parables about Things Humans Are Better Than Computers at Every Second.

102. GPT-3’s Completion of the Chinese Room Argument from Searle’s Minds, Brains, and Programs (Original Text Is in Bold)

103. Why Computers Don’t Need to Match Human Intelligence: With continuing advances in machine learning, it makes less and less sense to compare AI to the human mind

104. https://x.com/raphamilliere/status/1287047986233708546

105. Are Humans Intelligent? A Salty AI Op-Ed

106. Philosophers On GPT-3 (Updated With Replies by GPT-3)

107. Learning to Learn with Feedback and Local Plasticity

108. OpenAI API Alchemy: Summarization

109. https://www.reddit.com/r/IncreasinglyVerbose/

110. ​ Vectors 3.0: Even More Aphorisms and Ten-Second Essays

111. I fed the Proverbs of Hell to GPT-3…

112. Epigrams on Programming

113. https://www.reddit.com/r/MachineLearning/comments/iaitpu/d_knowledge_discovery_with_gpt3/g1onprj/

114. Umeshisms

115. ​ epigram#umeshisms

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116. Can GPT-3 produce new ideas? Partially automating Robin Hanson and others § If you never miss a plane…

117. https://www.reddit.com/r/boardgames/comments/sbkous/does_this_ai_generated_idea_for_a_board_game/

118. https://boardgamegeek.com/boardgame/37111/battlestar-galactica-the-board-game

119. https://boardgamegeek.com/boardgame/240980/blood-on-the-clocktower

120. Coup: Reformation Board Game

121. https://boardgamegeek.com/boardgame/150376/dead-of-winter-a-crossroads-game

122. Good Cop Bad Cop Board Game

123. Secrets Board Game

124. https://boardgamegeek.com/boardgame/1678/peloponnesian-war-431-404-bc

125. Unfathomable Board Game

126. https://boardgamegeek.com/boardgame/24396/war-on-terror-the-boardgame

127. Who Should We Eat? Board Game

128. https://x.com/emollick/status/1652040417104240644

129. How to Write Usefully

130. Max Tegmark on How a 'Put-Up-Or-Shut-Up' Resolution Led Him to Work on AI and Algorithmic News Selection

131. Wikipedia Bibliography:

     1. Anagram

     2. Acrostic

     3. Parity function  :

        https://en.wikipedia.org/wiki/Parity_function

     4. Geon (psychology)  :

        https://en.wikipedia.org/wiki/Geon_(psychology)

     5. Coq (software)  :

        https://en.wikipedia.org/wiki/Coq_(software)

     6. ASCII art

     7. Emoticon  :

        https://en.wikipedia.org/wiki/Emoticon

     8. WARC (file format)

     9. Template:Which

     10. Snowclone

     11. Top Road, Trenton, New Jersey  :

         https://en.wikipedia.org/wiki/Top_Road,_Trenton,_New_Jersey

     12. Common Crawl

     13. Cooperative principle

     14. David Ferrucci  :

         https://en.wikipedia.org/wiki/David_Ferrucci

     15. English Channel

     16. Channel Tunnel

     17. Channel Tunnel § Illegal attempts to cross and deaths  :

         https://en.wikipedia.org/wiki/Channel_Tunnel#Illegal_attempts_to_cross_and_deaths

     18. Cycling in the Channel Tunnel  :

         https://en.wikipedia.org/wiki/Cycling_in_the_Channel_Tunnel

     19. Lanthanum  :

         https://en.wikipedia.org/wiki/Lanthanum

     20. Pope John VII of Alexandria  :

         https://en.wikipedia.org/wiki/Pope_John_VII_of_Alexandria

     21. Gold

     22. Meitnerium  :

         https://en.wikipedia.org/wiki/Meitnerium

     23. Oganesson  :

         https://en.wikipedia.org/wiki/Oganesson

     24. Textual criticism § Internal evidence  :

         https://en.wikipedia.org/wiki/Textual_criticism#Internal_evidence

     25. Johann Philipp Reis  :

         https://en.wikipedia.org/wiki/Johann_Philipp_Reis

     26. Antonio Meucci  :

         https://en.wikipedia.org/wiki/Antonio_Meucci

     27. Innocenzo Manzetti  :

         https://en.wikipedia.org/wiki/Innocenzo_Manzetti

     28. Invention of the telephone  :

         https://en.wikipedia.org/wiki/Invention_of_the_telephone

     29. AI effect  :

         https://en.wikipedia.org/wiki/AI_effect

     30. Douglas Hofstadter

     31. Gary Marcus

     32. Kai-Fu Lee

     33. Alan Perlis

     34. List of Cowboy Bebop episodes  :

         https://en.wikipedia.org/wiki/List_of_Cowboy_Bebop_episodes

     35. Dwarf Fortress

     36. Mafia (party game)

     37. Betrayal at House on the Hill  :

         https://en.wikipedia.org/wiki/Betrayal_at_House_on_the_Hill

     38. Max Tegmark