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Podcast: The Gradient Podcast
Episode:

Joel Lehman: Open-Endedness and Evolution through Large Models
Category: Technology
Duration: 01:38:53
Publish Date: 2022-09-22 15:30:24
Description: Have suggestions for future podcast guests (or other feedback)? Let us know here!

In episode 44 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

Links:

Joel’s webpage

Evolution through Large Models: The Tweet

Papers:

Abandoning Objectives: Evolution through the search for novelty alone

Evolving a diversity of virtual creatures through novelty search and local competition

Designing neural networks through neuroevolution

Evolution through Large Models

Resources for (aspiring) ML researchers!

Cohere for AI

ML Collective

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