Originally published on February 7, 2016 on Wired.com by John Pavlus.
TO THE COMPUTER scientist Leslie Valiant, “machine learning” is redundant. In his opinion, a toddler fumbling with a rubber ball and a deep-learning network classifying cat photos are both learning; calling the latter system a “machine” is a distinction without a difference.
Valiant, a computer scientist at Harvard University, is hardly the only scientist to assume a fundamental equivalence between the capabilities of brains and computers. But he was one of the first to formalize what that relationship might look like in practice: In 1984, his “probably approximately correct” (PAC) model mathematically defined the conditions under which a mechanistic system could be said to “learn” information. Valiant won the A.M. Turing Award—often called the Nobel Prize of computing—for this contribution, which helped spawn the field of computational learning theory.
Valiant’s conceptual leaps didn’t stop there. In a 2013 book, also entitled “Probably Approximately Correct,” Valiant generalized his PAC learning framework to encompass biological evolution as well.
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