What happens when you apply the scaling laws of large language models to the physical work of atoms? Elad Gil sits down with Liam Fedus, co-founder at Periodic Labs, which is pioneering an AI foundation lab for atoms. Liam discusses how he pivoted from dark matter physics research to the front lines of artificial intelligence, including stints at Google Brain and working on ChatGPT at OpenAI. He talks about how Periodic is connecting massive language models to the physical world to overcome data bottlenecks in material science. Liam also shares how they use language models as an orchestration layer operating alongside specialized neural nets to run closed-loop physical experiments. They also explore the future of AGI and ASI, as well as the role of robotics in lab automation.
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Chapters:
00:00 – Cold Open
00:05 – Liam Fedus Introduction
00:39 – Liam’s Background at Google Brain, OpenAI
05:14 – From ChatGPT to Materials and Atoms
06:34 – Training Data in the Physical World
09:52 – Generalization Across Domains
11:31 – Models as an Orchestration Layer
12:48 – Commercialization and Business Model
16:10 – How Periodic’s Success May Shape the Future
17:45 – Multidisciplinary Scaling
19:41 – Capital and Compute
21:12 – Hiring at Periodic
21:44 – Thoughts on AGI and ASI
23:30 – Timeline for Machine-Directed Self-Improvement
25:39 – Automation and Data Generation
27:59 – Why Liam is Excited About the Future of Robotics
29:25 – Conclusion
