Fetching the paper…
Reading the bibliography…
We present this article as a small gesture in an attempt to counter what appears to be exponentially growing hype around Artificial Intelligence (AI) and its capabilities, and the distraction provided by the associated talk of science-fiction scenarios that might arise if AI should become sentient and super-intelligent.
McCulloch W.S., Pitts W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5:115-133
1943
Earlier work this paper cites.
Rumelhart D.E., McClelland J.L. (1986). Parallel distributed processing: exploration in the microstructure of cognition (Vols. 1 and 2). MIT Press
1986
Earlier work this paper cites.
Mitchell T.M. (1997). Machine Learning. McGraw-Hill
1997
Earlier work this paper cites.
Christiansen M.H., Kirby S. (eds.) (2003). Language evolution. Oxford; New York: Oxford University Press
2003
Earlier work this paper cites.
OpenAI (2020). Language Models are Few-Shot Learners. https://arxiv.org/abs/2005.14165
2005
Earlier work this paper cites.
Lungarella M, Iida F., Bongard J., Pfeifer R. (Ed’s). (2007). 50 Years of Artificial Intelligence: Essays Dedicated to the 50th Anniversary of Artificial Intelligence. Lecture Notes in Computer Science 4850, Springer
2007
Earlier work this paper cites.
McKay R.I., Nguyen X.H., Whigham P.A., Shan Y., O’Neill M. (2010). Grammar-based Genetic Programming - A Survey. Genetic Programming and Evolvable Machines, pp.365-396 Vol.11 No.3-4
2010
Earlier work this paper cites.
Kahneman, D. (2011). Thinking, Fast and Slow. Penguin Books
2011
Earlier work this paper cites.
Brabazon A., O’Neill M., McGarraghy S. (2015). Natural Computing Algorithms. Springer
2015
Earlier work this paper cites.
EU GDPR (2016). Regulation (EU) 2016/679 of the European Parliament and of the Council. Official Journal of the European Union https://eur-lex.europa.eu/eli/reg/2016/679/oj
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Vaswani et al. (2017). Attention Is All You Need. arXiv:1706.03762 [cs.CL]
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
Pearl J. (2019). The seven tools of causal inference, with reflections on machine learning. Communications of the ACM, 62(3):54–60
2019
Earlier work this paper cites.
High-Level Expert Group on AI (2019). Ethics guidelines for trustworthy AI. European Commission
2019
Earlier work this paper cites.
Mitchell M. (2020). Artificial Intelligence: A Guide for Thinking Humans. Pelican
2020
Earlier work this paper cites.
Bender E.M., Koller A. (2020). Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5185–5198, Online. Association for Computational Linguistics
2020
Cited alongside, same era.
Tamari et al. (2020) Language (Re)modelling: Towards Embodied Language Understanding. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. https://aclanthology.org/2020.acl-main.559/
2020
Cited alongside, same era.
Carlini, N., Tramèr, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T.B., Song, D.X., Erlingsson, Ú., Oprea, A., & Raffel, C. (2020). Extracting Training Data from Large Language Models. USENIX Security Symposium
2020
Cited alongside, same era.
Dillon S. (2020). The Eliza effect and its dangers: from demystification to gender critique. Journal for Cultural Research, 24(1):1-15
2020
Cited alongside, same era.
McCormack C. (2023). Concern over ’inconsistent’ Govt dept policy around ChatGPT. RTE News, 15 May 2023. https://www.rte.ie/news/2023/0515/1383644-ireland-chatgpt-security/
2023
Closest in time.
Mitchell M., Krakauer D.C. (2023). The debate over understanding in AI’s large language models. Proceedings of the National Academy of Science 120(13)
2023
Closest in time.
Mitchell M. (2023). On Analogy-Making in Large Language Models. Blog, 3 January 2023. https://open.substack.com/pub/aiguide/p/on-analogy-making-in-large-language?utm_campaign=post&utm_medium=web
2023
Closest in time.
Brooks R. (2023). What Will Transformers Transform? Blog, 23 March 2023. rodneybrooks.com/what-will-transformers-transform/
2023
Closest in time.
Armstrong K. (2023). ChatGPT: US lawyer admits using AI for case research, BBC News, 28 May 2023. https://www.bbc.com/news/world-us-canada-65735769
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Bender E.M., Gebru T., McMillan-Major A., and Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’21). Association for Computing Machinery, New York, NY, USA, pp. 610–623
2021
Cited alongside, same era.
Embodied Cognition. (2021). Stanford Encyclopedia of Philosophy https://plato.stanford.edu/entries/embodied-cognition/
2021
Cited alongside, same era.
Luccioni A., Viviano J. (2021). What’s in the Box? An Analysis of Undesirable Content in the Common Crawl Corpus. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pp.182–189, Online. Association for Computational Linguistics https://aclanthology.org/2021.acl-short.24/
2021
Cited alongside, same era.
Xie, S. M., Raghunathan, A., Liang, P., & Ma, T. (2021). An Explanation of In-context Learning as Implicit Bayesian Inference. https://doi.org/10.48550/ARXIV.2111.02080
2021
Cited alongside, same era.
Birhane A. (2022). Automating Ambiguity: Challenges and Pitfalls of Artificial Intelligence. PhD Thesis, University College Dublin
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Open AI. (2022). Dall-E 2 https://openai.com/dall-e-2
2022
Cited alongside, same era.
Rombach R., Blattmann A., Lorenz D., Esser P., Ommer B. (2022). High-Resolution Image Synthesis with Latent Diffusion Models. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022
2022
Cited alongside, same era.
2023
Closest in time.
Birhane, A., Kasirzadeh, A., Leslie, D. et al. Science in the age of large language models. Nat Rev Phys 5, 277–280 (2023). https://doi.org/10.1038/s42254-023-00581-4
2023
Closest in time.
Morley, Jessica and Floridi, Luciano, Foundation Models Are Exciting, but They Should Not Disrupt the Foundations of Caring (April 20, 2023). Available at SSRN: https://ssrn.com/abstract=4424821 or http://dx.doi.org/10.2139/ssrn.4424821
2023
Closest in time.
Gros D., Devanbu P., Yu Z. (2023). AI Safety Subproblems for Software Engineering Researchers. https://doi.org/10.48550/arXiv.2304.14597
2023
Closest in time.
Make-A-Video. (2023). Meta AI. https://makeavideo.studio/
2023
Closest in time.
Agostinelli et al. (2023) MusicLM. Google Research. https://google-research.github.io/seanet/musiclm/examples/
2023
Closest in time.
Kidd C., Birhane A. (2023). How AI can distort human beliefs. Science 380, pp.1222-1223
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Wang, X., Zhu, W., Saxon, M., Steyvers, M., & Wang, W. Y. (2023). Large Language Models Are Implicitly Topic Models: Explaining and Finding Good Demonstrations for In-Context Learning. https://doi.org/10.48550/ARXIV.2301.11916
2023
Closest in time.
Samuelson P. (2023). Legal Challenges to Generative AI, Part I. Communications of the ACM 66(7):20-23
2023
Closest in time.
European Parliament (2023) MEPs ready to negotiate first-ever rules for safe and transparent AI. Press Release, 14 June 2023, https://www.europarl.europa.eu/news/en/press-room/20230609IPR96212/meps-ready-to-negotiate-first-ever-rules-for-safe-and-transparent-ai
2023
Closest in time.