Rise and fall of the perceptron

In the 50s, Frank Rosenblatt came up with the perceptron, a machine learning algorithm inspired by neurons and the underlying block of the first neural networks (The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain, American Psychological Association, 1958). With the proper learning procedure, this method was already able to recognize characters. However, the hype was short-lived. Marvin Minsky (one of the fathers of AI) and Seymor Papert quickly demonstrated that the perceptron could not learn a function as simple as XOR (exclusive OR, the function that, given two binary input values, returns 1 if one, and only one, input is 1, and returns 0 otherwise). This makes sense to us nowadays—as the perceptron back then was modeled with a linear function while XOR is a non-linear one—but, at that time, it simply discouraged any further research for years.