David Martinez discusses an AI canonical architecture suitable for a number of different classes of applications and shares examples focused on cybersecurity to illustrate an application area that benefits from an end-to-end AI architecture. The examples include protecting enterprise systems, implementing automated detection of counterfeit parts, and using machine learning to reduce cyberanalysts’ workloads. The AI canonical architecture starts with data conditioning, followed by classes of machine learning algorithms, human-machine teaming, modern computing, and robust AI. David concludes with a summary of science and technology challenges and recommendations.
David Martinez is associate division head in the Cyber Security and Information Sciences Division at the MIT Lincoln Laboratory. His areas of expertise include cybersecurity, analytics, artificial intelligence, and high-performance computing. David was elected IEEE Fellow “for technical leadership in the development of high performance embedded computing for real-time defense systems,” was awarded the Eminent Engineer Award from the College Engineering at NMSU, and was elected to the NMSU Klipsch Electrical and Computer Engineering Academy. He is a member of the Deans of Engineering Council at NMSU and the advisory board in the School of Computing and Information Sciences at the Florida International University as well as a member of MIT/LL steering committee. Previously, he served on the Army Science Board. David coauthored High Performance Embedded Computing, A Systems Perspective. He holds a BS from New Mexico State University (NMSU), an MS from MIT, an EE degree in electrical and oceanographic engineering jointly from MIT and the Woods Hole Oceanographic Institution, and an MBA from SMU. He was born in El Paso, TX, and is fluent in Spanish. In his free time, he’s an avid golfer, saltwater fisherman, and outdoorsman.
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