Feynman on Artificial Intelligence and Machine Learning, with Updates

TL;DR

This paper reviews Richard Feynman's early views on AI and neural networks, comparing them with modern developments, highlighting achieved milestones and open challenges especially in computational science and symbolic methods.

Contribution

It provides a historical perspective on Feynman's ideas and evaluates their relevance and progress in the context of current AI and neural network research.

Findings

Some of Feynman's ideas have been realized in modern AI.

Certain aspects of his interests remain open and promising.

The paper suggests potential revival of symbolic methods in computational science.

Abstract

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.