In the final episode of the season, Abha sits down with Melanie to hear her perspective. They chat about Melanie’s career and research with Douglas Hofstadter, the author of Gödel, Escher, Bach. They also discuss her opinions on LLMs’ current capabilities, what she thinks of existential questions like the alignment problem, how sustainable the industry is, the difficulty of making claims about concepts like “intelligence” and “understanding,” and what she thinks future technological development should focus on.

COMPLEXITY

[email protected] (Santa Fe Institute)

Nature of Intelligence, Ep. 6: AI’s changing seasons

DEC 4, 202444 MIN
COMPLEXITY

Nature of Intelligence, Ep. 6: AI’s changing seasons

DEC 4, 202444 MIN

Description

Guest: Melanie Mitchell, Resident Professor, Santa Fe InstituteHosts: Abha Eli PhobooProducer: Katherine MoncurePodcast theme music by: Mitch MignanoFollow us on:Twitter • YouTube • Facebook • Instagram • LinkedIn  • BlueskyMore info:Tutorial: Fundamentals of Machine LearningLecture: Artificial IntelligenceSFI programs: EducationCompetition: ARC PrizeBooks: Gödel, Escher, Bach: an Eternal Golden Braid by Douglas HofstadterArtificial Intelligence: A Guide for Thinking Humans by Melanie MitchellComplexity: A Guided Tour by Melanie MitchellTalks: The Future of Artificial Intelligence by Melanie MitchellIntroduction: AI and the Barrier of Meaning 2 by Melanie MitchellConceptual Abstraction and Analogy in Natural and Artificial Intelligence by Melanie MitchellPapers & 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