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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
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