No Priors Ep. 120 | With Google DeepMind’s Pushmeet Kohli and Matej Balog

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. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @pushmeet | @matejbalog Chapters: 00:00 Pushmeet Kohli and Matej Balog Introduction 0:48 Origin of AlphaEvolve 02:31 AlphaEvolve’s Progression from AlphaGo and AlphaTensor 08:02 The Open Problem of Matrix Multiplication Efficiency 11:18 How AlphaEvolve Evolves Code 14:43 Scaling and Predicting Iterations 16:52 Implications for Coding Agents 19:42 Overcoming Limits of Automated Evaluators 25:21 Are We At Self-Improving AI? 28:10 Effects on Scientific Discovery and Mathematics 31:50 Role of Human Scientists with AlphaEvolve 38:30 Making AlphaEvolve Broadly Accessible 40:18 Applying AlphaEvolve Within Google 41:39 Conclusion