Feynman on Artificial Intelligence and Machine Learning, with Updates
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Abstract (arXiv)
I present my recollections of Richard Feynman's mid-1980s interest in artificial intelligence and neural networks, set in the technical context of the physics-related approaches to neural networks of that time. I attempt to evaluate his ideas in the light of the substantial advances in the field since then, and vice versa. There are aspects of Feynman's interests that I think have been largely achieved and others that remain excitingly open, notably in computational science, and potentially including the revival of symbolic methods therein.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 26 references that are in the map. First posted on arXiv 31 Aug 2022.
Builds on
11 of 26 references.
References · 11
- 2019AI Feynman: a Physics-Inspired Method for Symbolic Regressioncited by 536
- 2020Generative Language Modeling for Automated Theorem Provingcited by 379
- 2019Deep Learning for Symbolic Mathematicscited by 266
- 2019Ab-Initio Solution of the Many-Electron Schr"odinger Equation with Deep Neural Networkscited by 195
- 2018TacticToe: Learning to Prove with Tacticscited by 43
- 2015A formal proof of the Kepler conjecturecited by 42
- 2020ENIGMA Anonymous: Symbol-Independent Inference Guiding Machine (system description)cited by 26
- 2019HOList: An Environment for Machine Learning of Higher-Order Theorem Provingcited by 19
- 2019The Use of Deep Learning for Symbolic Integration: A Review of (Lample and Charton, 2019)cited by 11
- 2018Automated Theorem Proving in Intuitionistic Propositional Logic by Deep Reinforcement Learningcited by 7
- 2018Prospects for Declarative Mathematical Modeling of Complex Biological Systemscited by 1
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