# Feynman on Artificial Intelligence and Machine Learning, with Updates

arXiv: https://arxiv.org/abs/2209.00083 · HTML: https://mlsanity.com/paper/2209.00083/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 31 Aug 2022.

## Builds on (most cited first, 11 of 11)

- [AI Feynman: a Physics-Inspired Method for Symbolic Regression](https://mlsanity.com/paper/1905.11481/) (2019)
- [Generative Language Modeling for Automated Theorem Proving](https://mlsanity.com/paper/2009.03393/) (2020)
- [Deep Learning for Symbolic Mathematics](https://mlsanity.com/paper/1912.01412/) (2019)
- [Ab-Initio Solution of the Many-Electron Schr"odinger Equation with Deep Neural Networks](https://mlsanity.com/paper/1909.02487/) (2019)
- [TacticToe: Learning to Prove with Tactics](https://mlsanity.com/paper/1804.00596/) (2018)
- [A formal proof of the Kepler conjecture](https://mlsanity.com/paper/1501.02155/) (2015)
- [ENIGMA Anonymous: Symbol-Independent Inference Guiding Machine (system description)](https://mlsanity.com/paper/2002.05406/) (2020)
- [HOList: An Environment for Machine Learning of Higher-Order Theorem Proving](https://mlsanity.com/paper/1904.03241/) (2019)
- [The Use of Deep Learning for Symbolic Integration: A Review of (Lample and Charton, 2019)](https://mlsanity.com/paper/1912.05752/) (2019)
- [Automated Theorem Proving in Intuitionistic Propositional Logic by Deep Reinforcement Learning](https://mlsanity.com/paper/1811.00796/) (2018)
- [Prospects for Declarative Mathematical Modeling of Complex Biological Systems](https://mlsanity.com/paper/1804.11044/) (2018)

## 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.