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Classical AI research—particularly the branch known as connectionism, the basis for artificial neural networks—adheres to the outside-in, tabula rasa model.

These ideas remained popular, however, among philosophers and psychologists, who called it “connectionism” or “parallel distributed processing.”

He covers the alternate paradigms that gave rise to machine learning in the middle of the 20th century, the “connectionism” field’s fall from grace in the 1960s, and its eventual resurrection and surpassing of traditional A.I. paradigms in the 1980s to the present.

Domingos divides the field into five contemporary machine-learning paradigms—evolutionary algorithms, connectionism and neural networks, symbolism, Bayes networks, and analogical reasoning—which he imagines being unified in one future “master algorithm” capable of learning nearly anything.

The next turn came in more recent decades as a cross-disciplinary group of researchers, including Seung, hit on a new way of thinking that is described as connectionism.