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1958Primary source · 2 sourcesDebated

Rosenblatt builds the Perceptron

The first trainable artificial neuron learns from examples instead of being programmed by hand

On the timeline · around 1958 · Symbolic AI, Booms and WintersFoundationsSymbolic AI, Booms and WintersRosenblatt builds the Perceptron1955196019651970

Quick facts

Inventor
Frank Rosenblatt
Institution
Cornell Aeronautical Laboratory
Published in
Psychological Review, Vol. 65, No. 6

What happened

Frank Rosenblatt, working at the Cornell Aeronautical Laboratory under Office of Naval Research funding, published 'The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain' in Psychological Review. Unlike McCulloch and Pitts' fixed logical networks, the perceptron adjusted the weights on its connections automatically based on whether its output was right or wrong, so it learned to classify simple patterns, such as letters, from labeled examples rather than from a programmer's rules. The device drew heavy press attention, with some coverage suggesting it might one day walk, talk, and reproduce itself. In 1969, Marvin Minsky and Seymour Papert's book 'Perceptrons' proved mathematically that a single-layer perceptron could not compute certain simple functions, including logical XOR, a limitation that was true of that specific architecture but was widely read as a verdict on neural networks in general.

Why it matters

The perceptron introduced the learning rule, adjusting weights from labeled data, that underlies every neural network trained since, but the backlash to its limitations helped starve neural-network research of funding for over a decade, a setback historians still debate the fairness of.

How we know

Rosenblatt's original Psychological Review paper describes the model and its Office of Naval Research funding directly; Cornell's own retrospective documents both the initial enthusiasm and the 1969 critique that followed.

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