Perceptrons (book)
| Author | Marvin Minsky, Seymour Papert |
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Publication date
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1969 |
Perceptrons: an introduction to computational geometry is a book written by Marvin Minsky and Seymour Papert and published in 1969. An edition with handwritten corrections and additions was released in the early 1970s. An expanded edition was further published in 1987, containing a chapter dedicated to counter the criticisms made of it in the 1980s.
The main subject of the book is the perceptron, an important kind of artificial neural network developed in the late 1950s and early 1960s. The main researcher on perceptrons was Frank Rosenblatt, author of the book Principles of Neurodynamics. Rosenblatt and Minsky knew each other since adolescence, having studied with a one-year difference at the Bronx High School of Science. They became at one point central figures of a debate inside the AI research community, and are known to have promoted loud discussions in conferences. Despite the dispute, the corrected version of the book published after Rosenblatt's death contains a dedication to him.
This book is the center of a long-standing controversy in the study of artificial intelligence. It is claimed that pessimistic predictions made by the authors were responsible for an erroneous change in the direction of research in AI, concentrating efforts on so-called "symbolic" systems, and contributing to the so-called AI winter. This decision, supposedly, proved to be unfortunate in the 1980s, when new discoveries showed that the prognostics in the book were wrong.
The book contains a number of mathematical proofs regarding perceptrons, and while it highlights some of perceptrons' strengths, it also shows some previously unknown limitations. The most important one is related to the computation of some predicates, as the XOR function, and also the important connectedness predicate. The problem of connectedness is illustrated at the awkwardly colored cover of the book, intended to show how humans themselves have difficulties in computing this predicate.
Some critics of the book state that the authors imply that, since a single artificial neuron is incapable of implementing some functions such as the XOR logical function, larger networks also have similar limitations, and therefore should be dropped. Later research on three-layered perceptrons showed how to implement such functions, therefore saving the technique from obliteration.
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