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AI Canonical Architecture and Robust AI

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Keynote: AI Canonical Architecture and Robust AI

David R. Martinez

∗

Abstract

This presentation addresses an AI canonical architecture suit- able for a number of different classes of applications. Several examples will be shown focused on cyber security and poten- tial vulnerabilities to adversarial attacks. One critical element of the end-to-end AI architecture is the need for robust AI.

Significant advances have been made in AI algorithms and high performance computing. However, additional advance- ments in science and technology (S & T) are needed to vali- date the performance of AI systems. This performance assess- ment is very critical because AI systems are very brittle to ad- versarial modifications to the system. The AI canonical archi- tecture starts with data conditioning, followed by classes of machine learning algorithms, human-machine teaming, mod- ern computing, and robust AI. We will briefly address each of these areas. The presentation concludes with a summary of S

& T challenges and recommendations.

∗D. Martinez is with the Lincoln Laboratories, Massachusetts Institute of Technology, MA, USA. e-mail: dmartinez@ll.mit.edu Copyright c⃝by the paper’s authors. Copying permitted for private and academic purposes. In: Joseph Collins, Prithviraj Dasgupta, Ranjeev Mittu (eds.): Proceedings of the AAAI Fall 2018 Sympo- sium on Adversary-Aware Learning Techniques and Trends in Cy- bersecurity, Arlington, VA, USA, 18-19 October, 2018, published at http://ceur-ws.org

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