In 1969 Marvin Minsky and Seymour Papert’s book, titled Perceptrons, attacked Rosenblatt’s neural network model by wrongly claiming that a Perceptron (although a simple single-layer one) could not learn the XOR function and solve classifications in higher dimensions. This recalcitrant book had a devastating impact, also because of Rosenblatt’s premature death in 1971, and blocked funds to neural network research for decades. What is termed as the first ‘winter of Artificial Intelligence’ would be better described as the ‘winter of neural networks,’ which lasted until 1986 when the two volumes Parallel Distributed Processing clarified that (multilayer) Perceptrons can actually learn complex logic functions.

(Pasquinelli, 2017)

Pasquinelli, M. (2017). Machines that Morph Logic: Neural Networks and the Distorted Automation of Intelligence as Statistical Inference. Glass Bead, 1. [link]

Minsky, M. L., & Papert, S. (1988). Perceptrons : an introduction to computational geometry. Cambridge, MA: MIT Press. (Original work published 1969)