To the computer scientist Leslie Valiant, “machine learning” is redundant. In his opinion, a toddler fumbling with a rubber ball and a deep-learning networkclassifying 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.