The perceptron learns

On 7 July 1958 the Office of Naval Research showed reporters a program running on an IBM 704 that could tell a card marked on the left from one marked on the right, after fifty tries. The New York Times reported the next day that the Navy had revealed "the embryo of an electronic computer that it expects will be able to walk, talk, see, write, reproduce itself and be conscious of its existence." The psychologist behind it, Frank Rosenblatt, had said something close to that, and spent the rest of his short life paying for it.

What Rosenblatt had actually built was the first machine that learned from its mistakes. A perceptron is a McCulloch–Pitts neuron whose input weights are not fixed. Show it an example, let it guess, and if the guess is wrong nudge each weight in the direction that would have made it right. He proved that if a straight line can separate the two classes, this procedure will find one in a finite number of steps. The Mark I Perceptron, built in 1960 with a 20-by-20 grid of photocells, learned to recognise letters.

The perceptron convergence theorem is the first guarantee in machine learning. Its limitation, that it can only draw straight lines, became the most consequential footnote in the field's history when Minsky and Papert made it a book in 1969.