The Intuition of Novel Research Circumvents Deception

Have suggestions for future podcast guests (or other feedback)? Let us know here!

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. 

Subscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on Twitter

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