Artificial intelligence has a strong grasp on probability, but it still can’t compute cause and effect. Artificial intelligence owes a lot of its smarts to Judea Pearl.
In the 1980s he led efforts that allowed machines to reason probabilistically. Now he’s one of the field’s sharpest critics.
In his latest book, The Book of Why: The New Science of Cause and Effect, he argues that artificial intelligence has been handicapped by an incomplete understanding of what intelligence really is. Three decades ago, a prime challenge in artificial-intelligence research was to program machines to associate a potential cause to a set of observable conditions.
Pearl figured out how to do that using a scheme called Bayesian networks. Bayesian networks made it practical for machines to say that, given a patient who returned from Africa with a fever and body aches, the most likely explanation was malaria.
In 2011 Pearl won the Turing Award, computer science’s highest honor, in large part for this work. But as Pearl sees it, the field of AI got mired in probabilistic associations. Read more from theatlantic.com…
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