The first ever computer learning program was written in 1952 by Arthur Samuel. The program was the game of checkers, and the IBM computer improved at the game the more it played, studying which moves made up winning strategies and incorporating those moves into its program.
Then in 1957 Frank Rosenblatt designed the first neural network for computers – the perceptron – which simulated the thought processes of the human brain.
The next significant step forward in ML wasn’t until 1967 when the “nearest neighbor” algorithm was written, allowing computers to begin using very basic pattern recognition. This could be used to map a route for traveling salesmen, starting at a random city but ensuring they visit all cities during a short tour.
Twelve years later, in 1979 students at Stanford University invent the ‘Stanford Cart’ which could navigate obstacles in a room on its own. And in 1981, Gerald Dejong introduced the concept of Explanation Based Learning (EBL), where a computer analyses training data and creates a general rule it can follow by discarding unimportant data.
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