Uploaded February 2024 | Updated September 2026, 2 days ago
Speaker: Konrad Kording
Abstract: Causality is key to many branches of science, engineering, and the alignment of AI systems. I will start by highlighting the difficulties of causal inference in the real world, and build some intuition about why in the real world causality is difficult while it seems easy in our mind. I will continue by reviewing the mechanisms that people use to figure out causality and emphasize that they usually do it differently than, say, a specialist in causal inference would, and that most of the time the do operator does not overly help. Starting from this, I will highlight how causal inference can be seen as a meta-learning problem.
Bio: en.wikipedia.org/wiki/Konrad_K%C3%B6rding
Speaker: Konrad Kording
Abstract: Causality is key to many branches of science, engineering, and the alignment of AI systems. I will start by highlighting the difficulties of causal inference in the real world, and build some intuition about why in the real world causality is difficult while it seems easy in our mind. I will continue by reviewing the mechanisms that people use to figure out causality and emphasize that they usually do it differently than, say, a specialist in causal inference would, and that most of the time the do operator does not overly help. Starting from this, I will highlight how causal inference can be seen as a meta-learning problem.
Bio: en.wikipedia.org/wiki/Konrad_K%C3%B6rding
