Publication Date
3-1-2021
Abstract
Decades ago, machine learning was not as good as human learning, so many machine learning techniques were borrowed from how we humans learn -- be it on the level of concepts or on the level of biological neurons, cells responsible for mental activities such as learning. Lately, however, machine learning techniques such as deep learning have started outperforming humans. It is therefore time to start borrowing the other way around, i.e., using machine learning experience to improve our human teaching and learning. In this paper, we describe several relevant ideas -- and explain how some of these ideas are related to fuzzy logic and fuzzy techniques.
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Technical Report: UTEP-CS-21-19a
Published in Julia Rayz, Victor Raskin, Scott Dick, and Vladik Kreinovich (eds.), Explainable AI and Other Applications of Fuzzy Techniques, Proceedings of the Annual Conference of the North American Fuzzy Information Processing Society NAFIPS'2021, West Lafayette, Indiana, June 7-9, 2021, Springer, Cham, Switzerland, 2022, pp. 400-405.