No Priors: Artificial Intelligence | Technology | Startups
Meet AlphaEvolve: The Autonomous Agent That Discovers Algorithms Better Than Humans With Google DeepMind’s Pushmeet Kohli and Matej Balog
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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Much of the scientific process involves searching. But rather than continue to rely on the luck of discovery, Google DeepMind has engineered a more efficient AI agent that mines complex spaces to facilitate scientific breakthroughs. Sarah Guo speaks with Pushmeet Kohli, VP of Science and Strategic Initiatives, and research scientist Matej Balog at Google DeepMind about AlphaEvolve, an autonomous coding agent they developed that finds new algorithms through evolutionary search. Pushmeet and Matej talk about how AlphaEvolve tackles the problem of matrix multiplication efficiency, scaling and iteration in problem solving, and whether or not this means we are at self-improving AI. Together, they also explore the implications AlphaEvolve has to other sciences beyond mathematics and computer science.
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Chapters:
Pushmeet Kohli and Matej Balog Introduction
Origin of AlphaEvolve
AlphaEvolve’s Progression from AlphaGo and AlphaTensor
The Open Problem of Matrix Multiplication Efficiency
How AlphaEvolve Evolves Code
Scaling and Predicting Iterations
Implications for Coding Agents
Overcoming Limits of Automated Evaluators
Are We At Self-Improving AI?
Effects on Scientific Discovery and Mathematics
Role of Human Scientists with AlphaEvolve
Making AlphaEvolve Broadly Accessible
Applying AlphaEvolve Within Google
Conclusion
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