How to Enrich a Diagram in an Automated Scientist?

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We strive to obtain a state of deep understanding. Deep understanding is having the ability to answer questions in all three level of the hierarchy. That gives us a sense of being in ol and having an understanding of e domain, of crossing the street,. And i am not, i don't have any er fish in this pant or whateverhe metaphor is here, dogs in this fight. But i'm betting that the reaction to that philosophy from a lot of people who ware working in contemporary deep learning is, no, that's not what we do. You know? We just let the computer learn everything it can... but even if you're going to win, it still leaves us with hard problems about

To say that event A causes event B is to not only make a claim about our actual world, but about other possible worlds — in worlds where A didn’t happen but everything else was the same, B would not have happened. This leads to an obvious difficulty if we want to infer causes from sets of data — we generally only have data about the actual world. Happily, there are ways around this difficulty, and the study of causal relations is of central importance in modern social science and artificial intelligence research. Judea Pearl has been the leader of the “causal revolution,” and we talk about what that means and what questions remain unanswered.

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Judea Pearl received a Ph.D. in electrical engineering from the Polytechnic Institute of Brooklyn. He is currently a professor of computer science and statistics and director of the Cognitive Systems Laboratory at UCLA. He is a founding editor of the Journal of Causal Inference. Among his awards are the Lakatos Award in the philosophy of science, The Allen Newell Award from the Association for Computing Machinery, the Benjamin Franklin Medal, the Rumelhart Prize from the Cognitive Science Society, the ACM Turing Award, and the Grenander Prize from the American Mathematical Society. He is the co-author (with Dana MacKenzie) of The Book of Why: The New Science of Cause and Effect.

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