Home → Magazine Archive → June 2019 (Vol. 62, No. 6) → Lifelong Learning in Artificial Neural Networks → Abstract

Lifelong Learning in Artificial Neural Networks

By Gary Anthes

Communications of the ACM, Vol. 62 No. 6, Pages 13-15

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Over the past decade, artificial intelligence (AI) based on machine learning has reached break-through levels of performance, often approaching and sometimes exceeding the abilities of human experts. Examples include image recognition, language translation, and performance in the game of Go.

These applications employ large artificial neural networks, in which nodes are linked by millions of weighted interconnections. They mimic the structure and workings of living brains, except in one key respect—they don't learn over time, as animals do. Once designed, programmed, and trained by developers, they do not adapt to new data or new tasks without being retrained, often a very time-consuming task.


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