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

Clicks: 193
ID: 2961
2019
Article Quality & Performance Metrics
Overall Quality Improving Quality
0.0 /100
Combines engagement data with AI-assessed academic quality
AI Quality Assessment
Not analyzed
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
carpenter2019lookingneural 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)30165-0
URL
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.