A Digital Engineering Approach to Testing Modern AI and Complex Systems
Joseph R. Guerci, Sandeep Gogineni, Robert W. Schutz, Gavin I. McGee, Brian C. Watson, Hoan K. Nguyen, John Don Carlos, Daniel L. Stevens
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Modern AI (i.e., Deep Learning and its variants) is here to stay. However, its enigmatic black box nature presents a fundamental challenge to the traditional methods of test and validation (T&E). Or does it? In this paper we introduce a Digital Engineering (DE) approach to T&E (DE-T&E), combined with generative AI, that can achieve requisite mil spec statistical validation as well as uncover potential deleterious Black Swan events that might otherwise not be uncovered until it is too late. An illustration of these concepts is presented for an advanced modern radar example employing deep learning AI.