Specialists who interviewed human experts — doctors, geologists, chemists — and hand-translated their know-how into thousands of if-then rules for expert systems like MYCIN and XCON. The job largely vanished when expert systems proved too brittle and expensive to maintain, and statistical machine learning replaced hand-written rules with rules learned automatically from data.
Knowledge 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
1943
McCulloch and Pitts model the neuron mathematically
Warren McCulloch and Walter Pitts publish 'A Logical Calculus of the Ideas Immanent in Nervous Activity,' showing that networks of simplified, all-or-nothing neurons could in principle compute any logical function — a theoretical seed later credited as the ancestor of the artificial neural network.
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.
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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