Season 3 · Episode 6
Nature of Intelligence, Ep. 6: AI’s changing seasons
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.
December 4, 202444m 1s
Show Notes
Guest:
- Melanie Mitchell, Resident Professor, Santa Fe Institute
Hosts: Abha Eli Phoboo
Producer: Katherine Moncure
Podcast theme music by: Mitch Mignano
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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