Scalable Interactive and Practical Tools for Secure and Interpretable AI

Aug 23, 07:00PM PDT(02:00AM GMT).
  • Free 134 Attendees
This seminar is hosted by SF Bay ACM

We have witnessed tremendous growth in Artificial intelligence (AI) and machine learning (ML) recently. However, research shows that AI and ML models are often vulnerable to adversarial attacks, and their predictions can be difficult to understand, evaluate and ultimately act upon.

Discovering real-world vulnerabilities of deep neural networks and countermeasures to mitigate such threats has become essential to the successful deployment of AI in security settings. We present the first targeted physical adversarial attack (ShapeShifter) that fools state-of-the-art object detectors; a fast defence that counters adversarial noise by data compression; and interactive systems that further democratize the study of adversarial machine learning and facilitate real-time experimentation for deep learning practitioners.

To amplify people’s ability to interpret AI models, we present scalable interactive visualizations that have provided key leaps of insight, from increased model interpretability (Gamut with Microsoft Research), to model explorability with models trained on millions of instances (ActiVis deployed with Facebook), increased usability for non-experts about state-of-the-art AI (GAN Lab open-sourced with Google Brain), and our latest work Summit, an interactive system that scalably summarizes and visualizes what features a deep learning model has learned and how those features interact to make predictions. We conclude by highlighting the next visual analytics research frontiers in AI.

Duen Chau(Georgia Tech)

ACM Distinguished Speaker, Duen Horng (Polo) Chau is an Associate Professor of Computing at Georgia Tech. He co-directs Georgia Tech MS Analytics program. He is the Director of Industry Relations of The Institute for Data Engineering and Science (IDEaS), and the Associate Director of Corporate Relations of The Center for Machine Learning. His research group bridges machine learning and visualization to synthesize scalable interactive tools for making sense of massive datasets, interpreting complex AI models, and solving real-world problems in cybersecurity, human-centered AI, graph visualization and mining, and social good. His Ph.D. in Machine Learning from Carnegie Mellon University won CMUs Computer Science Dissertation Award, Honorable Mention.
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