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Thanks to rapid progress in artificial intelligence, we have entered an era when technology and philosophy intersect in interesting ways.
Philosophical Investigations. (Translated by Anscombe, G.E.M.)
L. Wittgenstein · 1953
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Rational animals
D. Davidson · 1982
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The symbol grounding problem
S. Harnad · 1990
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Intentional systems theory
D. Dennett · 2009
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The unreasonable effectiveness of data
A. Y. Halevy, P. Norvig, and F. Pereira · 2009
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Embodiment and the Inner Life: Cognition and Consciousness in the Space of Possible Minds
M. Shanahan · 2010
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
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BERT: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2018
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ViLBERT: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
J. Lu, D. Batra, D. Parikh, and S. Lee · 2019
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Language models are unsupervised multitask learners
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever · 2019
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Conversational AI: Social and ethical considerations
E. Ruane, A. Birhane, and A. Ventresque · 2019
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The Promise of Artificial Intelligence: Reckoning and Judgment
B. C. Smith · 2019
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Climbing towards NLU: On meaning, form, and understanding in the age of data
E. Bender and A. Koller · 2020
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Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, et al · 2020
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GPT-3, bloviator: OpenAI’s language generator has no idea what it’s talking about
G. Marcus and E. Davis · 2020
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Learning to summarize from human feedback
N. Stiennon, L. Ouyang, J. Wu, D. M. Ziegler, R. Lowe, et al · 2020
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On the dangers of stochastic parrots: Can language models be too big?
E. Bender, T. Gebru, A. McMillan-Major, and S. Shmitchell · 2021
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A mathematical framework for transformer circuits
N. Elhage, N. Nanda, C. Olsson, T. Henighan, N. Joseph, et al · 2021
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Implicit representations of meaning in neural language models
B. Z. Li, M. Nye, and J. Andreas · 2021
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PaLM: Scaling language modeling with pathways
A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, et al · 2022
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Faithful reasoning using large language models
A. Creswell and M. Shanahan · 2022
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Improving alignment of dialogue agents via targeted human judgements
A. Glaese, N. McAleese, M. Trȩbacz, J. Aslanides, V. Firoiu, et al · 2022
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Large language models are zero-shot reasoners
T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa · 2022
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Locating and editing factual associations in GPT
K. Meng, D. Bau, A. J. Andonian, and Y. Belinkov · 2022
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Show your work: Scratchpads for intermediate computation with language models
M. Nye, A. J. Andreassen, G. Gur-Ari, H. Michalewski, et al · 2021
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Scaling language models: Methods, analysis & insights from training Gopher
J. W. Rae, S. Borgeaud, T. Cai, K. Millican, J. Hoffmann, et al · 2021
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Ethical and social risks of harm from language models
L. Weidinger, J. Mellor, M. Rauh, C. Griffin, J. Uesato, et al · 2021
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Do as I can, not as I say: Grounding language in robotic affordances
M. Ahn, A. Brohan, N. Brown, Y. Chebotar, O. Cortes, et al · 2022
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Flamingo: a visual language model for few-shot learning
J.-B. Alayrac, J. Donahue, P. Luc, A. Miech, I. Barr, et al · 2022
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Data distributional properties drive emergent in-context learning in transformers
S. C. Chan, A. Santoro, A. K. Lampinen, J. X. Wang, A. K. Singh, et al · 2022
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In-context learning and induction heads
C. Olsson, N. Elhage, N. Nanda, N. Joseph, N. DasSarma, et al · 2022
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Training language models to follow instructions with human feedback
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, et al · 2022
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Meaning without reference in large language models
S. T. Piantadosi and F. Hill · 2022
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Abstraction for deep reinforcement learning
M. Shanahan and M. Mitchell · 2022
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LaMDA: Language models for dialog applications
R. Thoppilan, D. De Freitas, J. Hall, N. Shazeer, A. Kulshreshtha, et al · 2022
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Selection-inference: Exploiting large language models for interpretable logical reasoning
A. Creswell, M. Shanahan, and I. Higgins · 2023
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Toolformer: Language models can teach themselves to use tools
T. Schick, J. Dwivedi-Yu, R. Dessì, R. Raileanu, M. Lomeli, et al · 2023
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