Let's Learn Everything From Data

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The controversy in dat science is whether we should build an einstein fom an amiba by simulating the stream of data that our ancestors had from the time they were amibas until they became einstein. Both sides have arguments on ther sides, because, after all, everything we know originally came some sense data. Let's it. Lets learn everything from data. That's all we have o the alternative is we are ready ahead. Our ancestor works for us and have compiled a bunch of knowledge that we call a plausible plausibility. Well, who listens to whom ae have already compiled? And we know that the rooster, that the son does not listen to the rooster

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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