Games, Models and evolutionary Algorithms.
ACM OPINION
Open-Endedness and Evolution through Large Models
By The Gradient, September 23, 2022
Joel Lehman is a machine-learning scientist interested in AI safety, reinforcement learning, and creative open-ended search algorithms. He is the co-author of the book Why Greatness Cannot be Planned: The Myth of the Objective.
In an interview, Lehman talks about the move from game development to AI, evolutionary algorithms, neuroevolution through augmenting topologies (NEAT), LLMs as practical thought experiments in disembodied understanding, evolution through large models (ELM), competition in AI, advice for people considering ML research, and more.
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