Reprint of: Looking to the future: Learning from experience, averting catastrophe.

Clicks: 280
ID: 60933
2019
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Ranked #18 of 55 articles by views in neural networks : the official journal of the international neural network society

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Abstract
As humans go through life sifting vast quantities of complex information, we extract knowledge from settings that are more ambiguous than our early homes and classrooms. Learning from experience in an individual's unique context generally improves expert performance, despite the risks inherent in brain dynamics that can transform previously reliable expectations. Designers of twenty-first century technologies face the challenges and responsibilities posed by fielded systems that continue to learn on their own. The neural model Self-supervised ART, which can acquire significantly new knowledge in unpredictable contexts, is an example of one such system.
Reference Key
carpenter2019reprintneural Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Carpenter, Gail A;
Journal neural networks : the official journal of the international neural network society
Year 2019
DOI
S0893-6080(19)30283-7
URL
Keywords
artificial intelligence (ai) neural networks semi-supervised learning adaptive resonance theory (art) self-supervised (art) self-supervised learning

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