1980–1986 · 8 key events · 25 related people, organisations and concepts
Expert systems
Rule-based systems reach industry and national programmes fund new work.
1980
First International Workshop on Machine Learning held at Carnegie Mellon
The first International Workshop on Machine Learning, held at Carnegie Mellon University in July 1980, was the meeting that grew into ICML, today one of the three main machine learning conferences.
1982
Japan launches the Fifth Generation Computer Systems project
Japan's Ministry of International Trade and Industry launched a ten-year national project in 1982 to build a logic-programming, massively parallel computer for artificial intelligence. It produced working parallel inference machines but ended in commercial failure.
Neural networks and physical systems with emergent collective computational abilities
A 1982 paper by John J. Hopfield arguing that useful computation can emerge as a collective property of many simple, equivalent units, and describing a model whose collective behaviour gives content-addressable memory, generalisation and error correction.
Neural networks and physical systems with emergent collective computational abilities
A 1982 paper by John J. Hopfield showing that large networks of simple neuron-like units can collectively store and recall memories. It became known as the Hopfield network, and the work was recognised by the 2024 Nobel Prize in Physics.
1983
First CVPR held in Washington, DC
The Conference on Computer Vision and Pattern Recognition (CVPR) held its first meeting in Washington, DC in 1983, organised by Takeo Kanade and Dana H. Ballard. It became one of the central venues where computer vision research is presented and compared.
1985
A Learning Algorithm for Boltzmann Machines
A 1985 Cognitive Science paper by Ackley, Hinton and Sejnowski that presents a general learning rule for connectionist networks and reports simple examples of efficient internal representations.
1986
Connectionism revives with backpropagation and the PDP volumes
In October 1986 Rumelhart, Hinton and Williams published a Nature paper showing that networks with hidden units can learn useful internal representations by back-propagation — with the 1987 PDP volumes, a key publication of connectionism's second wave.
Learning representations by back-propagating errors
In a 1986 Nature paper, Rumelhart, Hinton and Williams showed that backpropagation lets hidden units in a multilayer network learn useful internal features of a task. The method is standard in most neural networks today.
