Milestones timeline
What were the first big milestones in AI?
The field was named at a 1956 workshop, got its first trainable neural network with Rosenblatt's perceptron in 1958, and learned how to train deeper networks with the 1986 backpropagation paper.
The story of today’s AI runs through a handful of dated turning points. This first page covers the early ones, before computers were fast enough to make neural networks pay off. The term itself came a little before our timeline starts: the field was named at the 1956 Dartmouth Summer Research Project, the workshop widely treated as the founding event of artificial intelligence.[1]
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1958 — The perceptron. Frank Rosenblatt built the perceptron at the Cornell Aeronautical Laboratory and demonstrated it publicly in July 1958. It was the first trainable neural network, a machine that adjusted itself from examples rather than being programmed by hand.[2] The underlying algorithm dates to 1957, and the physical Mark I machine was demonstrated in 1960.[3]
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1986 — Backpropagation. David Rumelhart, Geoffrey Hinton, and Ronald Williams published “Learning representations by back-propagating errors” in the journal Nature. The method gave a practical way to train networks with hidden layers, which a single perceptron could not handle.[4]
Between these dates the field went through long stretches of disappointment, sometimes called AI winters, when funding and interest dried up. The ideas were sound, but the hardware and the data needed to make them work had not arrived yet. That changed in the years covered on the next page.
References
- Dartmouth workshop — Wikipedia
- Professor's perceptron paved the way for AI - 60 years too soon — Cornell Chronicle
- Perceptron — Wikipedia
- Backpropagation — Wikipedia