Description: Artificial Intelligence and Machine Learning for Safety-Critical Systems: A Comprehensive Guide provides engineers and system designers who are exploring the application of AI/ML methods for safety-critical systems with a dedicated resource on the challenges and mitigation strategies involved in their design. The book's authors present ML techniques in safety-critical systems across multiple domains, including pattern recognition, image processing, edge computing, Internet of Things (IoT), encryption, hardware accelerators, and many others. These applications help readers understand the many challenges that need to be addressed in order to increase the deployment of ML models in critical systems.
In addition, the book shows how to improve public trust in ML systems by providing explainable model outputs rather than treating the system as a black box for which the outputs are difficult to explain. Finally, the authors demonstrate how to meet legal certification and regulatory requirements for the appropriate ML models. In essence, the goal of this book is to help ensure that AI-based critical systems better utilize resources, avoid failures, and increase system safety and public safety.Brief description:
Dr. Kanishka Tyagi is Director of Artificial Intelligence at UHV Technologies, Ft. Wayne, IN, USA, where he leads the development of Machine Learning in diverse R&D projects,
including the sorting of non-recyclable plastics, metal alloys, pathological samples, and the analysis of Roots CT images, funded by the US Department of Energy. Previously, he has worked as a lead machine learning autonomous driving scientist at Aptiv Corporation in Agoura Hills, California. Prior to Aptiv, he worked at Siemens research, interned in ML groups at The MathWorks and Google Research. He has worked as a visiting researcher at Ajou University and Seoul National University. Dr. Tyagi worked as a Research Associate at the Department of Electrical Engineering, Indian Institute of Technology, Kanpur, with Dr. P.K. Kalra. He received his M.S. and Ph.D. degree with Dr. Michael Manry in the Department of Electrical Engineering at the University of Texas at Arlington. His research interests are optimization theory, music and audio processing, neural networks, hardware machine learning, and radar machine learning. He is a co-editor of Quantum Computing: A Shift from Bits to Qubits from Springer. Dr. Tyagi has filed 15 U.S. patents/trade secrets in the course of his research.