Beyond Curve Fitting

Last week I attended the AAAI spring symposium on “Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI”, held at Stanford University. Since Judea Pearl and Dana Mackenzie published “The Book of Why”, the topic of causal inference gains increasing momentum in the machine learning and artificial intelligence community. If we want to build truly intelligent machines, which are able to interact with us in a meaningful way, we have to teach them the concept of causality. Otherwise, our future robots will never be able to understand that forcing the rooster to crow at 3am in the morning won’t make the sun appear. Continue reading Beyond Curve Fitting