Melanie Mitchell: Abstraction and Analogy in AI

Melanie Mitchell: Abstraction and Analogy in AI

Author: Daniel Bashir December 15, 2022 Duration: 54:47

Have suggestions for future podcast guests (or other feedback)? Let us know here!

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

Subscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on Twitter

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



Get full access to The Gradient at thegradientpub.substack.com/subscribe

Hosted by Daniel Bashir, The Gradient: Perspectives on AI moves beyond surface-level headlines to explore the intricate machinery and human ideas shaping artificial intelligence. Each episode is built on a foundation of deep research, leading to conversations that are both technically substantive and broadly accessible. You'll hear from researchers, engineers, and philosophers who are actively building and critiquing our technological future, discussing not just how AI systems work, but the larger implications of their integration into society. This isn't about speculative hype; it's a grounded examination of real progress, persistent challenges, and ethical considerations from those on the front lines. The discussions peel back layers on topics like model architecture, policy, and the fundamental science behind the algorithms becoming part of our daily lives. For anyone curious about the substance behind the buzz-whether you have a technical background or are simply keen to understand a defining technology of our age-this podcast offers a crucial and thoughtful resource. Tune in for a consistently detailed and nuanced take that treats artificial intelligence with the complexity it deserves.
Author: Language: English Episodes: 100

The Gradient: Perspectives on AI
Podcast Episodes
Steve Miller: Will AI Take Your Job? It's Not So Simple. [not-audio_url] [/not-audio_url]

Duration: 1:10:25
In episode 58 of The Gradient Podcast, Daniel Bashir speaks to Professor Steve Miller.Steve is a Professor Emeritus of Information Systems at Singapore Management University. Steve served as Founding Dean for the SMU Sch…
Linus Lee: At the Boundary of Machine and Mind [not-audio_url] [/not-audio_url]

Duration: 2:28:46
In episode 56 of The Gradient Podcast, Daniel Bashir speaks to Linus Lee. Linus is an independent researcher interested in the future of knowledge representation and creative work aided by machine understanding of langua…
Suresh Venkatasubramanian: An AI Bill of Rights [not-audio_url] [/not-audio_url]

Duration: 1:40:58
In episode 55 of The Gradient Podcast, Daniel Bashir speaks to Professor Suresh Venkatasubramanian. Professor Venkatasubramanian is a Professor of Computer Science and Data Science at Brown University, where his research…
Marc Bellemare: Distributional Reinforcement Learning [not-audio_url] [/not-audio_url]

Duration: 1:12:22
Have suggestions for future podcast guests (or other feedback)? Let us know here!In episode 52 of The Gradient Podcast, Daniel Bashir speaks to Professor Marc Bellemare. Professor Bellemare leads the reinforcement learni…
François Chollet: Keras and Measures of Intelligence [not-audio_url] [/not-audio_url]

Duration: 1:28:50
In episode 51 of The Gradient Podcast, Daniel Bashir speaks to François Chollet.François is a Senior Staff Software Engineer at Google and creator of the Keras deep learning library, which has enabled many people (includ…
Yoshua Bengio: The Past, Present, and Future of Deep Learning [not-audio_url] [/not-audio_url]

Duration: 1:14:09
Happy episode 50! This week’s episode is being released on Monday to avoid Thanksgiving. Have suggestions for future podcast guests (or other feedback)? Let us know here!In episode 50 of The Gradient Podcast, Daniel Bash…
Kanjun Qiu and Josh Albrecht: Generally Intelligent [not-audio_url] [/not-audio_url]

Duration: 47:21
In episode 49 of The Gradient Podcast, Daniel Bashir speaks to Kanjun Qiu and Josh Albrecht. Kanjun and Josh are CEO and CTO of Generally Intelligent, an AI startup aiming to develop general-purpose agents with human-lik…

«1...678910