
Укрощение искусственного интеллекта. Пособие по выживанию в нашем необыкновенном будущем
10
Generative AI Could Raise Global GDP by 7%, Goldman Sachs, April 5, 2023, https://www.goldmansachs.com/intelligence/pages/generative-ai-could-raise-global-gdp-by-7-percent.html.
11
Jeremy Kahn, The Inside Story of ChatGPT: How OpenAI Founder Sam Altman Built the World’s Hottest Technology with Billions from Microsoft.
12
Nick Bostrom, Superintelligence: Paths, Dangers, Strategies (Oxford University Press, 2014); Irving John Good, Speculations Concerning the First Ultraintelligent Machine, Advances in Computers 6 (1966): 31–88.
13
Jon Porter, ChatGPT Continues to Be One of the Fastest-Growing Services Ever, The Verge, November 6, 2023, https://www.theverge.com/2023/11/6/23948386/chatgpt-active-user-count-openai-developer-conference.
14
Gamiel Gran and Navin Chaddha, Generative AI – From Big Vision to Practical Execution, Mayfield, September 22, 2023, https://www.mayfield.com/generative-ai-from-big-vision-to-practical-execution/.
15
Джон Генри – мифологический народный герой США, рабочий, вступивший в соревнование с паровым молотом, но погибший от истощения. – Прим. ред.
16
Tom Brown et al., Language Models Are Few-Shot Learners, Advances in Neural Information Processing Systems 33 (Red Hook, NY: Curran Associates, Inc., 2020), 1877–1901.
17
Brown et al., Language Models Are Few-Shot Learners.
18
Brown et al., Language Models Are Few-Shot Learners; Deepak Narayanan et al., Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM, arXiv.org, 2021, https://arxiv.org/abs/2104.04473.
19
Alan Turing, Intelligent Machinery, National Physical Laboratory, August 1948.
20
Alan Turing, Computing Machinery and Intelligence, MIND: A Quarterly Review of Psychology and Philosophy 59, no. 236 (October 1950): 433–460.
21
Wolfe Mays, Can Machines Think? Philosophy 27, no. 101 (1952): 148–162.
22
John R. Searle, Minds, Brains, and Programs, The Behavioral and Brain Sciences 3, no. 3 (1980): 417–424.
23
Howard Gardner, Frames of Mind: The Theory Of Multiple Intelligences (London: Fontana Press, 1993).
24
Diane Proudfoot, Rethinking Turing’s Test, Journal of Philosophy 110, no. 7 (2013): 391–411; Simone Natale, Deceitful Media: Artificial Intelligence and Social Life After the Turing Test (New York: Oxford University Press, 2021); Luciano Floridi and Josh Cowls, A Unified Framework of Five Principles for AI in Society, in Machine Learning and the City, ed. Silvio Carta (Hoboken: Wiley, 2022), doi:10.1002/9781119815075.ch45.
25
Meta Fundamental AI Research Diplomacy Team (FAIR) et al., Human-Level Play in the Game of Diplomacy by Combining Language Models with Strategic Reasoning, Science 378, no. 6624 (2022): Supplemental Materials, Section A: Ethical Considerations, Evaluation Methods: AI agent disclosure, p. 4; Eva Dou and Olivia Geng, AI Masters the Game of Go, Wall Street Journal, January 6, 2017.
26
Natasha Lomas, Duplex Shows Google Failing at Ethical and Creative AI Design, TechCrunch, May 10, 2018, https://techcrunch.com/2018/05/10/duplex-shows-google-failing-at-ethical-and-creative-ai-design/.
27
John Markoff, Machines of Loving Grace: The Quest For Common Ground Between Humans and Robots (New York: HarperCollins, 2015).
28
J. McCarthy, M. L. Minsky, N. Rochester, C. E. Shannon, A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, Dartmouth, 1955, Ray Solomonoff Digital Archive, Box A, https://raysolomonoff.com/dartmouth/boxa/dart564props.pdf.
29
Pamela McCorduck, Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence, 2nd Edition (New York: Routledge, 2004), 114–115.
30
Pamela McCorduck, Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence, 2nd Edition (New York: Routledge, 2004), 104.
31
Harald Sack, Marvin Minsky and Artificial Neural Networks, SciHi Blog, August 2020, http://scihi.org/marvin-minsky-artificial-neural-networks/; Jeremy Bernstein, Marvin Minsky’s Vision of the Future, The New Yorker, December 6, 1981; Caspar Wylie, The History of Neural Networks and AI: Part I, Open Data Science, April 24, 2018, https://opendatascience.com/the-history-of-neural-networks-and-ai-part-i/; Single Layer Perceptron, Tutorials Point, accessed August 7, 2023, https://www.tutorialspoint.com/tensorflow/tensorflow_single_layer_perceptron.htm; McCorduck, Machines Who Think, 99–104.
32
McCorduck, Machines Who Think, 99–104; Shraddha Goled, Why Did AI Pioneer Marvin Minsky Oppose Neural Networks? Analytics India Magazine, March 2022, https://analyticsindiamag.com/why-did-ai-pioneer-marvin-minsky-oppose-neural-networks/.
33
Ben Tarnoff, Weizenbaum’s Nightmares: How the Inventor of the First Chatbot Turned against AI, The Guardian, July 25, 2023, https://www.theguardian. com/technology/2023/jul/25/joseph-weizenbaum-inventor-eliza-chatbot-turned-against-artificial-intelligence-ai.
34
McCorduck, Machines Who Think, 291–293.
35
McCorduck, Machines Who Think, 295–296.
36
McCorduck, Machines Who Think, 293–294.
37
Joseph Weizenbaum, ELIZA – a Computer Program for the Study of Natural Language Communication between Man and Machine, Communications of the ACM (Association for Computing Machinery) 9, no. 1 (1966): 36–45, https://doi.org/10.1145/365153.365168, 42.
38
McCorduck, Machines Who Think, 296.
39
Joseph Weizenbaum, ELIZA – a Computer Program for the Study of Natural Language Communication between Man and Machine, Communications of the ACM (Association for Computing Machinery) 9, no. 1 (1966): 42.
40
Ben Tarnoff, Weizenbaum’s Nightmares: How the Inventor of the First Chatbot Turned against AI.
41
McCorduck, Machines Who Think, 294.
42
Lawrence Switzky, ELIZA Effects: Pygmalion and the Early Development of Artificial Intelligence, Shaw 40, no. 1 (June 1, 2020): 50–68.
43
McCorduck, Machines Who Think, 293–294.
44
McCorduck, Machines Who Think, 85.
45
McCorduck, Machines Who Think, 361.
46
McCorduck, Machines Who Think, 356.
47
McCorduck, Machines Who Think, 356–357.
48
Tarnoff, Weizenbaum’s Nightmares: How the Inventor of the First Chatbot Turned against AI.
49
Joseph Weizenbaum, Computer Power and Human Reason: From Judgment to Calculation.
50
McCorduck, Machines Who Think, 359.
51
Bruce C. Buchanan, Joshua Lederberg, and John McCarthy, Three Reviews of J. Weizenbaum’s Computer Power and Human Reason, Advanced Research Projects Agency Archive, Stanford University, Computer Science Department, Stanford Artificial Intelligence Laboratory, November 1976, online, Defense Technical Information Center, U.S. Department of Defense, https://apps.dtic.mil/dtic/tr/fulltext/u2/a044713.pdf.
52
Cade Metz, Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World (UK: Penguin Books, 2022), 41–44.
53
Cade Metz, Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World (UK: Penguin Books, 2022), 53–54.
54
Cade Metz, Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World (UK: Penguin Books, 2022), 64–65.
55
Dave Steinkraus, Ian Buck, and Patrice Y. Simard, Using GPUs for Machine Learning Algorithms, in ICDAR ’05: Proceedings of the Eighth International Conference on Document Analysis and Recognition, Eighth International Conference on Document Analysis and Recognition (August 2005), IEEE Computer Society, 1115–1119, doi:10.1109/icdar.2005.251.
56
Kumar Chellapilla, Sidd Puri, and Patrice Simard, High Performance Convolutional Neural Networks for Document Processing, International Workshop on the Frontiers of Handwriting Recognition (IWFHR), October 2006, https://www.researchgate.net/publication/228344387_High_Performance_Convolutional_Neural_ Networks_for_Document_Processing.
57
Metz, Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World, 69–78.
58
Metz, Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World, 1–12, 80–88, 98.
59
Jeremy Kahn, Inside Big Tech’s Quest for Human-Level A.I., Fortune, January 20, 2020, https://fortune.com/longform/ai-artificial-intelligence-big-tech-microsoft-alphabet-openai/.
60
Maxime Godfroid, A Critical Appraisal of Deep Learning, Towards Data Science, January 17, 2021, https://towardsdatascience.com/a-critical-appraisal-of-deep-learning-1b154695dddf.
61
Metz, Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World, 105–111; Kahn, Inside Big Tech’s Quest for Human-Level A.I.; Shane Legg, interview by Jeremy Kahn, August 22, 2023.
62
Kahn, Inside Big Tech’s Quest for Human-Level A.I.
63
Metz, Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World, 112.
64
Metz, Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World, 112–116.
65
Peter Holley, Elon Musk’s Nightmare: A Google Robot Army Annihilating Mankind, Washington Post, May 13, 2015, https://www.washingtonpost.com/news/innovations/wp/2015/05/13/elon-musks-nightmare-a-google-robot-army-annihilating-mankind/.
66
Kahn, Inside Big Tech’s Quest for Human-Level A.I.
67
Andrej Karpathy, Introducing OpenAI, accessed January 6, 2024, https://openai.com/blog/introducing-openai.
68
Andrej Karpathy, Introducing OpenAI, accessed January 6, 2024; Jeremy Kahn, ChatGPT Creates an A.I. Frenzy, Fortune, February/March 2023, 44–53.
69
Cade Metz, The Rise of AI – What the AI Behind AlphaGo Can Teach Us about Being Human, Wired, May 17, 2016, https://www.wired.com/2016/05/google-alpha-go-ai/.
70
Metz, Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World, 280–283; Kahn, Inside Big Tech’s Quest for Human-Level A.I.
71
Madhumita Murgia, Transformers: The Google Scientists Who Pioneered an AI Revolution, Financial Times, July 23, 2023, https://www.ft.com/content/37bb01af-ee46-4483-982f-ef3921436a50; Madhumita Murgia and FT Visual Story-Telling Team, Generative AI Exists because of the Transformer, Financial Times, September 12, 2023, https://ig.ft.com/generative-ai/; Jeremy Kahn, A.I. Breakthroughs in Natural-Language Processing Are Big for Business, Fortune, January 20, 2020, https://fortune.com/2020/01/20/natural-language-processing-business/.
72
Kahn, A.I. Breakthroughs in Natural-Language Processing Are Big for Business.
73
Steven Levy, What OpenAI Really Wants, Wired, September 5, 2023, https://www.wired.com/story/what-openai-really-wants/.
74
Jeremy Kahn, Move Over, Photoshop: OpenAI Just Revolutionized Digital Image Making, Fortune, April 6, 2022, https://fortune.com/2022/04/06/openai-dall-e-2-photorealistic-images-from-text-descriptions/.
75
Harry McCracken, Adobe Is Diving – Carefully! – into Generative AI, Fast Company, March 21, 2023, https://www.fastcompany.com/90868402/adobe-firefly-generative-ai-photoshop-express-illustrator.
76
Steven Levy, OpenAI’s Sora Turns AI Prompts Into Photorealistic Videos, Wired, February 15, 2024, https://www.wired.com/story/openai-sora-generative-ai-video/.
77
Kristin Yim, Turn Ideas into Music with MusicLM, Google, May 10, 2023, https://blog.google/technology/ai/musiclm-google-ai-test-kitchen/; ElevenLabs – Generative AI Text to Speech & Voice Cloning, accessed October 1, 2023, https://elevenlabs.io/.
78
Anna Tong and Jeffrey Dastin, Insight: Race towards ‘Autonomous’ AI Agents Grips Silicon Valley, Reuters, July 18, 2023, https://www.reuters.com/technology/race-towards-autonomous-ai-agents-grips-silicon-valley-2023-07-17/.
79
Kahn, Inside Big Tech’s Quest for Human-Level A.I.; Jared Kaplan et al., Scaling Laws for Neural Language Models, arXiv.org, January 23, 2020, http://arxiv.org/abs/2001.08361.
80
Kahn, Inside Big Tech’s Quest for Human-Level A.I.; Ilya Sutskever, interview by Jeremy Kahn, July 14, 2023.
Приобретайте полный текст книги у нашего партнера: