Publication Date
6-1-2024
Abstract
One of the reasons why the results of the current AI methods (especially deep-learning-based methods) are not absolutely reliable is that, in contrast to more traditional data processing techniques which are based on solid mathematical and statistical foundations, modern AI techniques use a lot of semi-heuristic methods. These methods have been, in many cases, empirically successful, but the absence of solid justification makes us less certain that these methods will work in other cases as well. To make AI more reliable, it is therefore necessary to provide mathematical foundations for the current semi-heuristic techniques. In this paper, we show that two related approaches can lead to such a foundation: the approach based on computational complexity and the symmetry-based approach. As a result, we get an explanation of why, in general, fuzzy and neural techniques are so successful, and why specific version of these techniques are empirically the most successful, such as ReLU activation function and the use of piecewise linear membership functions in fuzzy approach.
Comments
Technical Report: UTEP-CS-24-28