Neural Networks and Symbolic AI

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In episode 53 of The Gradient Podcast, Daniel Bashir speaks to Professor Melanie Mitchell.

Professor Mitchell is the Davis Professor at the Santa Fe Institute. Her research focuses on conceptual abstraction, analogy-making, and visual recognition in AI systems. She is the author or editor of six books and her work spans the fields of AI, cognitive science, and complex systems. Her latest book is Artificial Intelligence: A Guide for Thinking Humans. 

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Outline:

* (00:00) Intro

* (02:20) Melanie’s intro to AI

* (04:35) Melanie’s intellectual influences, AI debates over time

* (10:50) We don’t have the right metrics for empirical study in AI

* (15:00) Why AI is Harder than we Think: the four fallacies

* (20:50) Difficulties in understanding what’s difficult for machines vs humans

* (23:30) Roles for humanlike and non-humanlike intelligence

* (27:25) Whether “intelligence” is a useful word

* (31:55) Melanie’s thoughts on modern deep learning advances, brittleness

* (35:35) Abstraction, Analogies, and their role in AI

* (38:40) Concepts as analogical and what that means for cognition

* (41:25) Where does analogy bottom out

* (44:50) Cognitive science approaches to concepts

* (45:20) Understanding how to form and use concepts is one of the key problems in AI

* (46:10) Approaching abstraction and analogy, Melanie’s work / the Copycat architecture

* (49:50) Probabilistic program induction as a promising approach to intelligence

* (52:25) Melanie’s advice for aspiring AI researchers

* (54:40) Outro

Links:

* Melanie’s homepage and Twitter

* Papers

* Difficulties in AI, hype cycles

* Why AI is Harder than we think

* The Debate Over Understanding in AI’s Large Language Models

* What Does It Mean for AI to Understand?

* Abstraction, analogies, and reasoning

* Abstraction and Analogy-Making in Artificial Intelligence

* Evaluating understanding on conceptual abstraction benchmarks