COMPLEXITY

Guest: 
Melanie Mitchell, Resident Professor, Santa Fe Institute
Hosts: Abha Eli Phoboo
Producer: Katherine Moncure
Podcast theme music by: Mitch Mignano
Follow us on:
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More info:
Tutorial: Fundamentals of Machine Learning
Lecture: Artificial Intelligence
SFI programs: Education
Competition: ARC Prize
Books: 
Gödel, Escher, Bach: an Eternal Golden Braid by Douglas Hofstadter
Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell
Complexity: A Guided Tour by Melanie Mitchell
Talks: 
The Future of Artificial Intelligence by Melanie Mitchell
Introduction: AI and the Barrier of Meaning 2 by Melanie Mitchell
Conceptual Abstraction and Analogy in Natural and Artificial Intelligence by Melanie Mitchell
Papers & Articles:
“The metaphors of artificial intelligence,” in Science (November 14, 2024), doi: 10.1126/science.adt6140
“Using counterfactual tasks to evaluate the generality of analogical reasoning in Large Language Models,” in arXiv (February 14, 2024), doi.org/10.48550/arXiv.2402.08955
“Comparing humans, GPT-4, and GPT-4V on abstraction and reasoning tasks, ” (Proceedings of the LLM-CP Workshop, AAAI 2024), arXiv (December 11, 2023), doi.org/10.48550/arXiv.2311.09247
“The debate over understanding in AI’s large language models,” in PNAS (March 21, 2023), doi.org/10.1073/pnas.2215907120
“The ConceptARC benchmark: evaluating understanding and generalization in the ARC domain,” in Transactions on Machine Learning Research (August 2023), arXiv (May 11, 2023), doi.org/10.48550/arXiv.2305.07141

Guests: 
Erica Cartmill, Professor, Anthropology and Cognitive Science, Indiana University Bloomington
Ellie Pavlick, Assistant Professor, Computer Science and Linguistics, Brown University
Hosts: Abha Eli Phoboo & Melanie Mitchell
Producer: Katherine Moncure
Podcast theme music by: Mitch Mignano
Follow us on:
Twitter • YouTube • Facebook • Instagram • LinkedIn  • Bluesky
More info:
Tutorial: Fundamentals of Machine Learning
Lecture: Artificial Intelligence
SFI programs: Education
Diverse Intelligences Summer Institute
Books: 
Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell
Talks: 
How do we know what an animal understands by Erica Cartmill
The Future of Artificial Intelligence by Melanie Mitchell
Papers & Articles:
“Just kidding: the evolutionary roots of playful teasing,” in Biology Letters (September 23, 2020), doi.org/10.1098/rsbl.2020.0370
“Overcoming bias in the comparison of human language and animal communication,” in PNAS (November 13, 2023), doi.org/10.1073/pnas.22187991
“Using the senses in animal communication,” by Erica Cartmill, in A New Companion to Linguistic Anthropology, Chapter 20, Wiley Online Library (March 21, 2023)
“Symbols and grounding in large language models,” in Philosophical Transactions of the Royal Society A (June 5, 2023), doi.org/10.1098/rsta.2022.0041
“Emergence of abstract state representations in embodied sequence modeling,” in arXiv (November 7, 2023), doi.org/10.48550/arXiv.2311.02171
“How do we know how smart AI systems are,” in Science (July 13, 2023), doi: 10.1126/science.adj59

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
Follow us on:
Twitter • YouTube • Facebook • Instagram • LinkedIn  • Bluesky
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

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
Follow us on:
Twitter • YouTube • Facebook • Instagram • LinkedIn  • Bluesky
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: 
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
Follow us on:
Twitter • YouTube • Facebook • Instagram • LinkedIn  • Bluesky
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 live?” in Behavioral and Brain Sciences (September 28, 2023), doi:10.1017/S0140525X23001814
“Can we distinguish machine learning from human learning?” in arXiv (October 8, 2019), doi.org/10.48550/arXiv.1910.03466