Published Sep 22, 2022
Joel Lehman: Open-Endedness and Evolution through Large Models
Joel Lehman delves into the revolutionary use of large language models as mutation operators in evolutionary algorithms, highlighting the potential for continuous evolution in AI and the philosophical implications of open-ended systems. His discussion offers fresh perspectives on AI, evolutionary theory, and personal growth by challenging traditional boundaries and emphasizing novelty and diversity.
Topics covered
Popular Clips
Episode Highlights
Related Episodes
Jeff Clune: Genetic Algorithms, Quality-Diversity, Curiosity
Answers 383 questions
Hugo Larochelle: Deep Learning as Science
Answers 383 questions
Michael Levin & Adam Goldstein: Intelligence and its Many Scales
Answers 383 questions
Kyunghyun Cho: Neural Machine Translation, Language, and Doing Good Science
Answers 383 questions
Joel Simon on AI art and Artbreeder
Answers 383 questions
Luis Voloch: AI and Biology
Answers 383 questions
Melanie Mitchell: Abstraction and Analogy in AI
Answers 383 questions
Ben Wellington: ML for Finance and Storytelling through Data
Answers 383 questions
Soumith Chintala: PyTorch
Answers 383 questions
Zachary Lipton: Where Machine Learning Falls Short
Answers 383 questions
Sasha Rush: Building Better NLP Systems
Answers 383 questions
Ed Grefenstette: Language, Semantics, Cohere
Answers 383 questions
François Chollet: Keras and Measures of Intelligence
Answers 383 questions
Joe Edelman: Meaning-Aligned AI
Answers 383 questions
Some Changes at The Gradient
Answers 383 questions