Looking to the future: Learning from experience, averting catastrophe.
Journal:
Neural networks : the official journal of the International Neural Network Society
Published Date:
Dec 1, 2019
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.