AI for physics & physics for AI

A central goal of physics is to discover mathematical
patterns in data. For example, after four years of analyzing data
tables on planetary orbits, Johannes Kepler started a scientific
revolution in 1605 by discovering that Mars' orbit was an ellipse. I
describe how we can automate such tasks with machine learning and not
only discover symbolic formulas accurately matching datasets
(so-called symbolic regression), equations of motion and conserved
quantities, but also auto-discover which degrees of freedom are most
useful for predicting time evolution (for example, optimal generalized
coordinates extracted from video data). The methods I present exploit
numerous ideas from physics to recursively simplify neural networks,
ranging from symmetries to differentiable manifolds, curvature and
topological defects, and also take advantage of mathematical insights
from knot theory and graph modularity.
BIO: Max Tegmark is a professor doing AI and physics research at MIT
as part of the Institute for Artificial Intelligence & Fundamental
Interactions and the Center for Brains, Minds and Machines. He
advocates for positive use of technology as president of the Future of
Life Institute. He is the author of over 250 publications as well as
the New York Times bestsellers “Life 3.0: Being Human in the Age of
Artificial Intelligence” and "Our Mathematical Universe: My Quest
for the  Ultimate Nature of Reality". His AI research focuses on
intelligible intelligence.