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The Gradient: Perspectives on AI

David Pfau: Manifold Factorization and AI for Science

Jul 11, 2024
David Pfau, a research scientist at Google DeepMind, discusses manifold factorization, deep learning for quantum mechanics, and picking research problems. He explores optimization on manifolds, projective representation theory in physics, and metrics in AI. Pfau also delves into understanding rotations in vision, topology-preserving methods, and scalability in AI development.
02:00:52

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

  • Applications of machine learning to computational physics were discussed, focusing on neural network wave functions for ab initio quantum chemistry.
  • Using machine learning as a model for understanding brain function was emphasized, not just as a data analysis tool.

Deep dives

Research Interests and Applications of Machine Learning in Computational Physics

Applications of machine learning to computational physics and connections between differential geometry and unsupervised learning are discussed. Fascinating topics like ab initio quantum chemistry with neural network wave functions and disentangled representations are explored.

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