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Causal AI: The Melting Pot. Can Physics, Math & Biology Help Us?

What is the relationship between physics and causal models?

What can science of non-human animal behavior teach causal AI researchers?

Bernhard Schölkopf's rich background and experience allow him to combine perspectives from computation, physics, mathematics, biology, theory of evolution, psychology and ethology to build a deep understanding of underlying principles that govern complex systems and intelligent behavior.

His pioneering work in causal machine learning has revolutionized the field, providing new insights that enhance our ability to understand causal relationships and mechanisms in both natural and artificial systems.

In the episode we discuss:

Does evolution favor causal inference over correlation-based learning?
Can differential equations help us generalize structural causal models?
What new book is Bernhard working on?
Can ethology inspire causal AI researchers?

Ready to dive in?

About The Guest
Bernhard Schölkopf, PhD is a Director at Max Planck Institute for Intelligent Systems. He's one of the cofounders of European Lab for Learning & Intelligent Systems (ELLIS) and a recepient of the ACM Allen Newell Award, BBVA Foundation Frontiers of Knowledge Award, and more. His contributions to modern machine learning are hard to overestimate. He's a an affiliated professor at ETH Zürich, honorary professor at the University of Tübingen and the Technical University Berlin. His pioneering work on causal inference and causal machine learning inspired thousands of researchers and practitioners worldwide.

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