Sep 22 • 1HR 39M

Joel Lehman: Open-Endedness and Evolution through Large Models

A conversation with Joel Lehman, machine learning scientist formerly of OpenAI and Uber AI Labs.

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In episode 42 of The Gradient Podcast, Daniel Bashir speaks to Joel Lehman.

Joel is a machine learning scientist interested in AI safety, reinforcement learning, and creative open-ended search algorithms. Joel has spent time at Uber AI Labs and OpenAI and is the co-author of the book Why Greatness Cannot be Planned: The Myth of the Objective

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Outline:

  • (00:00) Intro

  • (01:40) From game development to AI

  • (03:20) Why evolutionary algorithms

  • (10:00) Abandoning Objectives: Evolution Through the Search for Novelty Alone

  • (24:10) Measuring a desired behavior post-hoc vs optimizing for that behavior

  • (27:30) Neuroevolution through Augmenting Topologies (NEAT), Evolving a Diversity of Virtual Creatures

  • (35:00) Humans are an inefficient solution to evolution’s objectives

  • (47:30) Is embodiment required for understanding? Today’s LLMs as practical thought experiments in disembodied understanding

  • (51:15) Evolution through Large Models (ELM)

  • (1:01:07) ELM: Quality Diversity Algorithms, MAP-Elites, bootstrapping training data

  • (1:05:25) Dimensions of Diversity in MAP-Elites, what is “interesting”?

  • (1:12:30) ELM: Fine-tuning the language model

  • (1:18:00) Results of invention in ELM, complexity in creatures

  • (1:20:20) Future work building on ELM, key challenges in open-endedness

  • (1:24:30) How Joel’s research affects his approach to life and work

  • (1:28:30) Balancing novelty and exploitation in work

  • (1:34:10) Intense competition in AI, Joel’s advice for people considering ML research

  • (1:38:45) Daniel isn’t the worst interviewer ever

  • (1:38:50) Outro

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