Before deep learning, getting a computer-vision or speech system to work well depended on researchers hand-designing the right input features — edge detectors like SIFT and HOG for images, cepstral coefficients for audio — a specialized craft in itself. Networks that learn their own features directly from raw data made most of this design work obsolete within a few years of 2012.
Hand-crafted feature engineer no longer exists as a living trade. Here is what erased it, and which profession took on the work.
1943–1956
The idea and the name
Warren McCulloch and Walter Pitts' 1943 paper modeling neurons as simple logical switches gave later researchers a mathematical starting point, and Alan Turing's 1950 proposal of a practical test for machine intelligence gave the question a public shape. Both ideas fed into the 1956 Dartmouth workshop, where John McCarthy and three colleagues coined 'artificial intelligence' for a summer meeting that produced far less technical progress than its organizers hoped, but permanently named the field they had just founded.
What else was happening then
1950
Turing proposes the imitation game
In 'Computing Machinery and Intelligence,' published in the journal Mind, Alan Turing sidesteps the question 'can machines think' and proposes a practical test instead, predicting that within fifty years a machine with about a billion bits of storage would fool an average interrogator at least 30% of the time.
1956
The Dartmouth workshop names the field
John McCarthy, Marvin Minsky, Nathaniel Rochester and Claude Shannon's eight-week Dartmouth summer workshop coins the term 'artificial intelligence' and brings together roughly ten researchers who would go on to found much of the field's early research agenda.
1958
Rosenblatt builds the Perceptron
Frank Rosenblatt's Perceptron, first implemented in software and then in custom hardware at Cornell, learns to classify simple images by adjusting its own connection weights — the first working demonstration of a trainable artificial neural network.
1969–1974
The first AI winter begins
Marvin Minsky and Seymour Papert's 1969 book 'Perceptrons' proves mathematically that single-layer networks cannot solve simple problems like XOR, and Britain's 1973 Lighthill Report concludes AI has failed to deliver on its promises; funding for neural-network and general AI research collapses on both sides of the Atlantic.
Where that work lives now
Designs and tests the algorithms behind machine intelligence, in a field now racing to automate a growing share of its own research process.
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