

From Physics to Causal AI & Back | Bernhard Schölkopf Ep 17 | CausalBanditsPodcast.com
Jun 3, 2024
Bernhard Schölkopf, Director at the Max Planck Institute for Intelligent Systems, merges insights from physics, biology, and machine learning. He discusses how evolution might favor causal inference over mere correlation and the intricate ties between differential equations and causal models. Schölkopf emphasizes the importance of understanding biological intelligence to enhance AI development. Plus, he shares his exciting new book project, aiming to bridge gaps in causal inference and its application across disciplines.
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Thinking as Internal Action
- Conrad Lorenz compared thinking to "acting in an imagined space".
- This metaphor highlights the importance of internal models for intelligent behavior.
Beyond Statistical Representations
- Current AI excels at statistical representation learning, focusing on correlations and patterns.
- Shifting towards interventional representations, incorporating actions, is key for true intelligence.
Correlation vs. Causation in Learning
- Humans and animals rely heavily on correlational learning, likely for efficiency.
- Causal models become crucial in situations requiring deeper understanding and generalization.