The Gradient: Perspectives on AI

David Pfau: Manifold Factorization and AI for Science

Jul 11, 2024

02:00:52

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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.

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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.

Optimization challenges of spectral decompositions in the deep learning era were highlighted, showcasing convergence using algorithms like power iteration.

Geometric discussions on decomposing manifolds from data and the application of Diram decomposition in computing subspace invariances were explored.

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.

Holonomy Group and Manifold Decomposition

01:31

Turning Intuition into Practical Algorithms through Collaboration

01:23

The Elegance and Universality of Mathematical Precision in Biological Theories

01:20

Uncovering Insights into Dimensionality Reduction Techniques

01:27

Intro

2min

Intersection of Neuroscience and Deep Learning in Scientific Research

17min

Exploring Optimization on Manifolds and Deep Learning Methods

24min

Exploring the Surprising Convergence of Power Iteration in Optimization

3min

Exploring Projective Representation Theory, Lie Groups, and Physics Implications

2min

Understanding Manifold Factorization in AI

30min

Exploring Metrics, Missing Pieces, and Scalability in AI

20min

Comparing Different Methods in Small Language Models

11min

Navigating Research Paths and Pursuing Unseen Connections

12min

Episode 130

I spoke with David Pfau about:

* Spectral learning and ML

* Learning to disentangle manifolds and (projective) representation theory

* Deep learning for computational quantum mechanics

* Picking and pursuing research problems and directions

David’s work is really (times k for some very large value of k) interesting—I’ve been inspired to descend a number of rabbit holes because of it.

(if you listen to this episode, you might become as cool as this guy)

While I’m at it — I’m still hovering around 40 ratings on Apple Podcasts. It’d mean a lot if you’d consider helping me bump that up!

Enjoy—and let me know what you think!

David is a staff research scientist at Google DeepMind. He is also a visiting professor at Imperial College London in the Department of Physics, where he supervises work on applications of deep learning to computational quantum mechanics. His research interests span artificial intelligence, machine learning and scientific computing.

Find me on Twitter for updates on new episodes, and reach me at editor@thegradient.pub for feedback, ideas, guest suggestions.

I spend a lot of time on this podcast—if you like my work, you can support me on Patreon :) You can also support upkeep for the full Gradient team/project through a paid subscription on Substack!

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Outline:

* (00:00) Intro

* (00:52) David Pfau the “critic”

* (02:05) Scientific applications of deep learning — David’s interests

* (04:57) Brain / neural network analogies

* (09:40) Modern ML systems and theories of the brain

* (14:19) Desirable properties of theories

* (18:07) Spectral Inference Networks

* (19:15) Connections to FermiNet / computational physics, a series of papers

* (33:52) Deep slow feature analysis — interpretability and findings on eigenfunctions

* (39:07) Following up on eigenfunctions (there are indeed only so many hours in a day; I have been asking the Substack people if they can ship 40-hour days, but I don’t think they’ve gotten to it yet)

* (42:17) Power iteration and intuitions

* (45:23) Projective representation theory

* (46:00) ???

* (46:54) Geomancer and learning to decompose a manifold from data

* (47:45) we consider the question of whether you will spend 90 more minutes of this podcast episode (there are not 90 more minutes left in this podcast episode, but there could have been)

* (1:08:47) Learning embeddings

* (1:11:12) The “unexpected emergent property” of Geomancer

* (1:14:43) Learned embeddings and disentangling and preservation of topology

* n/b I still haven’t managed to do this in colab because I keep crashing my instance when I use s3o4d :(

* (1:21:07) What’s missing from the ~ current (deep learning) paradigm ~

* (1:29:04) LLMs as swiss-army knives

* (1:32:05) RL and human learning — TD learning in the brain

* (1:37:43) Models that cover the Pareto Front (image below)

* (1:46:54) AI accelerators and doubling down on transformers

* (1:48:27) On Slow Research — chasing big questions and what makes problems attractive

* (1:53:50) Future work on Geomancer

* (1:55:35) Finding balance in pursuing interesting and lucrative work