Nature of Intelligence, Ep. 3: What kind of intelligence is an LLM?

10/23/24 • 45 min

2 Listeners

COMPLEXITY

Guests:

Tomer Ullman, Assistant Professor, Department of Psychology, Harvard University
Murray Shanahan, Professor of Cognitive Robotics, Department of Computing, Imperial College London; Principal Research Scientist, Google DeepMind

Hosts: Abha Eli Phoboo & Melanie Mitchell

Producer: Katherine Moncure

Podcast theme music by: Mitch Mignano

More info:

Tutorial: Fundamentals of Machine Learning
Lecture: Artificial Intelligence
SFI programs: Education

Books:

Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell
The Technological Singularity by Murray Shanahan
Embodiment and the inner life: Cognition and Consciousness in the Space of Possible Minds by Murray Shanahan
Solving the Frame Problem by Murray Shanahan
Search, Inference and Dependencies in Artificial Intelligence by Murray Shanahan and Richard Southwick

Talks:

The Future of Artificial Intelligence by Melanie Mitchell
Artificial intelligence: A brief introduction to AI by Murray Shanahan

Papers & Articles:

“A Conversation With Bing’s Chatbot Left Me Deeply Unsettled,” in New York Times (Feb 16, 2023)
“Bayesian Models of Conceptual Development: Learning as Building Models of the World,” in Annual Review of Developmental Psychology Volume 2 (Oct 26, 2020), doi.org/10.1146/annurev-devpsych-121318-084833
“Comparing the Evaluation and Production of Loophole Behavior in Humans and Large Language Models,” in Findings of the Association for Computational Linguistics (December 2023), doi.org/10.18653/v1/2023.findings-emnlp.264
“Role play with large language models,” in Nature (Nov 8, 2023), doi.org/10.1038/s41586-023-06647-8
“Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks,” arXiv (v5, March 14, 2023), doi.org/10.48550/arXiv.2302.08399
“Talking about Large Language Models,” in Communications of the ACM (Feb 12, 2024),
“Simulacra as Conscious Exotica,” in arXiv (v2, July 11, 2024), doi.org/10.48550/arXiv.2402.12422

Guests:

Tomer Ullman, Assistant Professor, Department of Psychology, Harvard University
Murray Shanahan, Professor of Cognitive Robotics, Department of Computing, Imperial College London; Principal Research Scientist, Google DeepMind

Hosts: Abha Eli Phoboo & Melanie Mitchell

Producer: Katherine Moncure

Podcast theme music by: Mitch Mignano

More info:

Tutorial: Fundamentals of Machine Learning
Lecture: Artificial Intelligence
SFI programs: Education

Books:

Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell
The Technological Singularity by Murray Shanahan
Embodiment and the inner life: Cognition and Consciousness in the Space of Possible Minds by Murray Shanahan
Solving the Frame Problem by Murray Shanahan
Search, Inference and Dependencies in Artificial Intelligence by Murray Shanahan and Richard Southwick

Talks:

The Future of Artificial Intelligence by Melanie Mitchell
Artificial intelligence: A brief introduction to AI by Murray Shanahan

Papers & Articles:

“A Conversation With Bing’s Chatbot Left Me Deeply Unsettled,” in New York Times (Feb 16, 2023)
“Bayesian Models of Conceptual Development: Learning as Building Models of the World,” in Annual Review of Developmental Psychology Volume 2 (Oct 26, 2020), doi.org/10.1146/annurev-devpsych-121318-084833
“Comparing the Evaluation and Production of Loophole Behavior in Humans and Large Language Models,” in Findings of the Association for Computational Linguistics (December 2023), doi.org/10.18653/v1/2023.findings-emnlp.264
“Role play with large language models,” in Nature (Nov 8, 2023), doi.org/10.1038/s41586-023-06647-8
“Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks,” arXiv (v5, March 14, 2023), doi.org/10.48550/arXiv.2302.08399
“Talking about Large Language Models,” in Communications of the ACM (Feb 12, 2024),
“Simulacra as Conscious Exotica,” in arXiv (v2, July 11, 2024), doi.org/10.48550/arXiv.2402.12422

Previous Episode

Nature of Intelligence, Ep. 2: The relationship between language and thought

Guests:

Evelina Fedorenko, Associate Professor, Department of Brain and Cognitive Sciences, and Investigator, McGovern Institute for Brain Research, MIT
Steve Piantadosi, Professor of Psychology and Neuroscience, and Head of Computation and Language Lab, UC Berkeley
Gary Lupyan, Professor of Psychology, University of Wisconsin-Madison

Hosts: Abha Eli Phoboo & Melanie Mitchell

Producer: Katherine Moncure

Podcast theme music by: Mitch Mignano

More info:

Tutorial: Fundamentals of Machine Learning
Lecture: Artificial Intelligence
SFI programs: Education

Books:

Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell
Developing Object Concepts in Infancy: An Associative Learning Perspective by Rakison, D.H., and G. Lupyan
Language and Mind by Noam Chomsky
On Language by Noam Chomsky

Talks:

The Future of Artificial Intelligence by Melanie Mitchell
The language system in the human brain: Parallels & Differences with LLMs by Evelina Federenko

Papers & Articles:

“Dissociating language and thought in large language models,” in Trends in Cognitive Science (March 19, 2024), doi: 10.1016/j.tics.2024.01.011
“The language network as a natural kind within the broader landscape of the human brain,” in Nature Reviews Neuroscience (April 12, 2024), doi.org/10.1038/s41583-024-00802-4
“Visual grounding helps learn word meanings in low-data regimes,” in arXiv (v2 revised on 25 March 2024), doi.org/10.48550/arXiv.2310.13257
“No evidence of theory of mind reasoning in the human language network,” in Cerebral Cortex (December 28, 2022), doi.org/10.1093/cercor/bhac505
“Chapter 1: Modern language models refute Chomsky’s approach to language,” by Steve T. Piantadosi (v7, November 2023), lingbuzz/007180
“Uniquely human intelligence arose from expanded information capacity,” in Nature Reviews Psychology (April 2, 2024), doi.org/10.1038/s44159-024-00283-3
“Understanding the allure and pitfalls of Chomsky's acience,” Review by Gary Lupyan, in The American Journal of Psychology (Spring 2018), doi.org/10.5406/amerjpsyc.131.1.0112
“Language is more abstract than you think, or, why aren’t languages more iconic?” in Philosophical Transactions of the Royal Society B (June 18, 2018),
Published:18 June 2018, doi.org/10.1098/rstb.2017.0137
“Does vocabulary help structure the mind?” in Minnesota Symposia on Child Psychology: Human Communication: Origins, Mechanisms, and Functions (February 27, 2021), doi.org/10.1002/9781119684527.ch6
“Use of superordinate labels yields more robust and human-like visual representations in convolutional neural networks,” in Journal of Vision (December 2021), doi.org/10.1167/jov.21.13.13
“Appeals to ‘Theory of Mind’ no longer explain much in language evolution,” by Justin Sulik and Gary Lupyan
“Effects of language on visual perception,” in Trends in Cognitive Sciences (October 1, 2020), doi.org/10.1016/j.tics.2020.08.005
“Is language-of-thought the best game in the town we li...

Next Episode

Nature of Intelligence, Ep. 4: Babies vs Machines

Guests:

Linda Smith, Distinguished Professor and Chancellor's Professor, Psychological and Brain Sciences, Department of Psychological and Brain Sciences, Indiana University Bloomington
Michael Frank, Benjamin Scott Crocker Professor of Human Biology, Department of Psychology, Stanford University

Hosts: Abha Eli Phoboo & Melanie Mitchell

Producer: Katherine Moncure

Podcast theme music by: Mitch Mignano

More info:

Tutorial: Fundamentals of Machine Learning
Lecture: Artificial Intelligence
SFI programs: Education

Books:

Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell

Talks:

Why "Self-Generated Learning” May Be More Radical and Consequential Than First Appears by Linda Smith
Children’s Early Language Learning: An Inspiration for Social AI, by Michael Frank at Stanford HAI
The Future of Artificial Intelligence by Melanie Mitchell

Papers & Articles:

“Curriculum Learning With Infant Egocentric Videos,” in NeurIPS 2023 (September 21)
“The Infant’s Visual World The Everyday Statistics for Visual Learning,” by Swapnaa Jayaraman and Linda B. Smith, in The Cambridge Handbook of Infant Development: Brain, Behavior, and Cultural Context, Chapter 20, Cambridge University Press (September 26, 2020)
“Can lessons from infants solve the problems of data-greedy AI?” in Nature (March 18, 2024), doi.org/10.1038/d41586-024-00713-5
“Episodes of experience and generative intelligence,” in Trends in Cognitive Sciences (October 19, 2022), doi.org/10.1016/j.tics.2022.09.012
“Baby steps in evaluating the capacities of large language models,” in Nature Reviews Psychology (June 27, 2023), doi.org/10.1038/s44159-023-00211-x
“Auxiliary task demands mask the capabilities of smaller language models,” in COLM (July 10, 2024)
“Learning the Meanings of Function Words From Grounded Language Using a Visual Question Answering Model,” in Cognitive Science (First published: 14 May 2024), doi.org/10.1111/cogs.13448