Читать книгу - "Эти странные новые разумы: Как ИИ научился говорить и что это значит - Кристофер Саммерфилд"

Kim, G., Baldi, P., and McAleer, S. (2023), ‘Language Models Can Solve Computer Tasks’. arXiv. Available at http://arxiv.org/abs/2303.17491 (accessed 11 December 2023).

Klessinger, N., Szczerbinski, M., and Varley, R. (2007), ‘Algebra in a Man with Severe Aphasia’, Neuropsychologia, 45(8), pp. 1642–8. Available at https://doi.org/10.1016/j.neuropsychologia.2007.01.005.

Kocijan, V. et al. (2023), ‘The Defeat of the Winograd Schema Challenge’. arXiv. Available at http://arxiv.org/abs/2201.02387 (accessed 17 February 2024).

Kojima, T. et al. (2023), ‘Large Language Models Are Zero-Shot Reasoners’. arXiv. Available at http://arxiv.org/abs/2205.11916 (accessed 11 December 2023).

Krueger, D., Maharaj, T., and Leike, J. (2020), ‘Hidden Incentives for Auto-Induced Distributional Shift’. arXiv. Available at http://arxiv.org/abs/2009.09153 (accessed 24 November 2023).

Lenat, D. (2022), ‘Creating a 30-Million-Rule System: MCC and Cycorp’, IEEE Annals of the History of Computing, 44(1), pp. 44–56. Available at https://doi.org/10.1109/MAHC.2022.3149468.

Lewis, P. et al. (2021), ‘Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks’. arXiv. Available at http://arxiv.org/abs/2005.11401 (accessed 9 December 2023).

Li, Y. et al. (2022), ‘Competition-Level Code Generation With AlphaCode’, Science, 378(6624), pp. 1092–7. Available at https://doi.org/10.1126/science.abq1158.

Lin, S., Hilton, J., and Evans, O. (2022), ‘TruthfulQA: Measuring How Models Mimic Human Falsehoods’. arXiv. Available at http://arxiv.org/abs/2109.07958 (accessed 7 October 2023).

Linzen, T., Dupoux, E., and Goldberg, Y. (2016), ‘Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies’, Transactions of the Association for Computational Linguistics, 4, pp. 521–35. Available at https://doi.org/10.1162/tacl_a_00115.

Liu, T. and Low, B. K. H. (2023), ‘Goat: Fine-Tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks’. Available at https://doi.org/10.48550/arXiv.2305.14201.

Lobina, D. (2023), ‘Artificial Intelligence [sic: Machine Learning] and the Best Game in Town; or How Some Philosophers, and the Bbs, Missed a Step’, 3 Quarks Daily, 13 February. Available at https://3quarksdaily.com/3quarksdaily/2023/02/artificial-intelligence-sic-machine-learning-and-the-best-game-in-town-or-how-some-philosophers-and-the-bbs-missed-a-step.html.

Lu, Y., Yu, J., and Huang, S.-H. S. (2023), ‘Illuminating the Black Box: A Psychometric Investigation into the Multifaceted Nature of Large Language Models’. arXiv. Available at http://arxiv.org/abs/2312.14202 (accessed 17 February 2024).

Luccioni, A. S. and Viviano, J. D. (2021), ‘What’s in the Box? A Preliminary Analysis of Undesirable Content in the Common Crawl Corpus’. arXiv. Available at http://arxiv.org/abs/2105.02732 (accessed 6 October 2023).

Luria, A. R., Tsvetkova, L. S., and Futer, D. S. (1965), ‘Aphasia in a Composer’, Journal of the Neurological Sciences, 2(3), pp. 288–92. Available at https://doi.org/10.1016/0022-510X(65)90113-9.

Madaan, A. et al. (2022), ‘Language Models of Code Are Few-Shot Commonsense Learners’. arXiv. Available at https://doi.org/10.48550/arXiv.2210.07128.

Mahowald, K. et al. (2023), ‘Dissociating Language and Thought in Large Language Models: A Cognitive Perspective’. arXiv. Available at http://arxiv.org/abs/2301.06627 (accessed 16 September 2023).

Marcus, G. (2020), ‘The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence’. Preprint. arXiv. Available at http://arxiv.org/abs/2002.06177 (accessed 8 April 2021).

Matz, S. et al. (2023), ‘The Potential of Generative AI for Personalized Persuasion at Scale’. Preprint. PsyArXiv. Available at https://doi.org/10.31234/osf.io/rn97c.

McCulloch, W. S. and Pitts, W. (1943), ‘A Logical Calculus of the Ideas Immanent in Nervous Activity’, Bulletin of Mathematical Biophysics, 5, pp. 115–33. Available at https://doi.org/10.1007/BF02478259.

Metzinger, T. (2021), ‘Artificial Suffering: An Argument for a Global Moratorium on Synthetic Phenomenology’, Journal of Artificial Intelligence and Consciousness, 08(01), pp. 43–66. Available at https://doi.org/10.1142/S270507852150003X.

Mialon, G. et al. (2023), ‘Augmented Language Models: A Survey’. arXiv. Available at http://arxiv.org/abs/2302.07842 (accessed 11 December 2023).

Michel, J.-B. et al. (2011), ‘Quantitative Analysis of Culture Using Millions of Digitized Books’, Science, 331(6014), pp. 176–82. Available at https://doi.org/10.1126/science.1199644.

Mikolov, T. et al. (2013), ‘Distributed Representations of Words and Phrases and Their Compositionality’. arXiv. Available at http://arxiv.org/abs/1310.4546 (accessed 18 February 2024).

Miller, B. A. P. (2015), ‘Automatic Detection of Comment Propaganda in Chinese Media’. Preprint. SSRN Electronic Journal. Available at https://doi.org/10.2139/ssrn.2738325.

Minsky, M. and Papert, S. (1969), Perceptrons: An Introduction to Computational Geometry. Cambridge, Ma: MIT Press.

Moskal, S. et al. (2023), ‘LLMs Killed the Script Kiddie: How Agents Supported by Large Language Models Change the Landscape of Network Threat Testing’. arXiv. Available at http://arxiv.org/abs/2310.06936 (accessed 17 December 2023).

Mosteller, F. and Wallace, D. L. (1963), ‘Inference in an Authorship Problem’, Journal of the American Statistical Association, 58(302), p. 275. Available at https://doi.org/10.2307/2283270.

Nakano, R. et al. (2022), ‘WebGPT: Browser-Assisted Question-Answering with Human Feedback’. arXiv. Available at http://arxiv.org/abs/2112.09332 (accessed 9 December 2023).

Newell, A., Shaw, J. C., and Simon, H. A. (1959), ‘Report on a General Problem-Solving Program’. Available at http://bitsavers.informatik.uni-stuttgart.de/pdf/rand/ipl/P-1584_Report_On_A_General_Problem-Solving_Program_Feb59.pdf.

Noy, S. and Zhang, W. (2023), ‘Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence’, Science, 381(6654), pp. 187–92. Available at https://doi.org/10.1126/science.adh2586.

OpenAI (2023), ‘GPT-4 Technical Report’. arXiv. Available at http://arxiv.org/abs/2303.08774 (accessed 7 October 2023).

Ord, Toby (2020), The Precipice: Existential Risk and the Future of Humanity. London: Bloomsbury.

Ouyang, L. et al. (2022), ‘Training Language Models to Follow Instructions with Human Feedback’. arXiv. Available at http://arxiv.org/abs/2203.02155 (accessed 26 November 2022).

Owen, C. M., Howard, A., and Binder, D. K. (2009), ‘Hippocampus Minor, Calcar Avis, and the Huxley–Owen Debate’, Neurosurgery, 65(6), pp. 1098–105. Available at https://doi.org/10.1227/01.NEU.0000359535.84445.0B.

Pan, Y. et al. (2023), ‘On the Risk of Misinformation Pollution with Large Language Models’. arXiv. Available at http://arxiv.org/abs/2305.13661 (accessed 26 October 2023).

Pariser, Eli (2011), The Filter Bubble: What the Internet is Hiding from You. London: Viking.

Patterson, F. G. (1978), ‘The Gestures of a Gorilla: Language Acquisition in Another Pongid’, Brain and Language, 5(1), pp. 72–97. Available at https://doi.org/10.1016/0093-934X(78)90008-1.

Perez, E. et al. (2022), ‘Discovering Language Model Behaviors with Model-Written Evaluations’. arXiv. Available at http://arxiv.org/abs/2212.09251 (accessed 22 October 2023).

Phuong, M. and Hutter, M. (2022), ‘Formal Algorithms for Transformers’. Available at https://doi.org/10.48550/arXiv.2207.09238.

Piantadosi, S. T. (2023), ‘Modern Language Models Refute Chomsky’s Approach to Language’. LingBuzz. Available at https://lingbuzz.net/lingbuzz/007180.

Piantadosi, S. T. and Hill, F. (2022), ‘Meaning Without Reference in Large Language Models’. Available at https://doi.org/10.48550/arXiv.2208.02957.

Press, O. et al. (2023), ‘Measuring and Narrowing the Compositionality Gap in Language Models’. arXiv. Available at http://arxiv.org/abs/2210.03350 (accessed 13 December 2023).

Ravuri, S. et al. (2021), ‘Skilful Precipitation Nowcasting Using Deep Generative Models of