No Priors: Artificial Intelligence | Technology | Startups
AI for Atoms: How Periodic Labs is Revolutionizing Materials Engineering with Co-Founder Liam Fedus
At this moment of inflection in technology, co-hosts Elad Gil and Sarah Guo talk to the world's leading AI engineers, researchers and founders about the biggest questions: How far away is AGI? What markets are at risk for disruption? How will commerce, culture, and society change? What’s happening in state-of-the-art in research? “No Priors” is your guide to the AI revolution. Email feedback to show@no-priors.com.
Sarah Guo is a startup investor and the founder of Conviction, an investment firm purpose-built to serve intelligent software, or "Software 3.0" companies. She spent nearly a decade incubating and investing at venture firm Greylock Partners.
Elad Gil is a serial entrepreneur and a startup investor. He was co-founder of Color Health, Mixer Labs (which was acquired by Twitter). He has invested in over 40 companies now worth $1B or more each, and is also author of the High Growth Handbook.
Show Notes
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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:
– Cold Open
– Liam Fedus Introduction
– Liam’s Background at Google Brain, OpenAI
– From ChatGPT to Materials and Atoms
– Training Data in the Physical World
– Generalization Across Domains
– Models as an Orchestration Layer
– Commercialization and Business Model
– How Periodic’s Success May Shape the Future
– Multidisciplinary Scaling
– Capital and Compute
– Hiring at Periodic
– Thoughts on AGI and ASI
– Timeline for Machine-Directed Self-Improvement
– Automation and Data Generation
– Why Liam is Excited About the Future of Robotics
– Conclusion
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