Looking to the future: Learning from experience, averting catastrophe.
Where this comes from
- Record sourced from PubMed, PMID 31295692.
- Also identified by DOI 10.1016/j.neunet.2019.05.025.
- No licence information is recorded for this record.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
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.
Medical subject headings
- Neural Networks, Computer
- Supervised Machine Learning