ELM in a Quality Diversity Algorithm

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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

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