Towards Secure and Interpretable AI: Scalable Methods, Interactive Visualizations, and Practical Tools

Chau, Duen Horng

Title:

Towards Secure and Interpretable AI: Scalable Methods, Interactive Visualizations, and Practical Tools

dc.contributor.author	Chau, Duen Horng
dc.contributor.corporatename	Georgia Institute of Technology. GVU Center	en_US
dc.contributor.corporatename	Georgia Institute of Technology. College of Computing	en_US
dc.contributor.corporatename	Georgia Institute of Technology. School of Computational Science and Engineering	en_US
dc.date.accessioned	2019-09-10T17:00:03Z
dc.date.available	2019-09-10T17:00:03Z
dc.date.issued	2019-08-29
dc.description	Presented on August 29, 2019 at 11:30 a.m.-1:00 p.m. in the Technology Square Research Building (TSRB), 1st Floor Auditorium, Georgia Institute of Technology.	en_US
dc.description	Polo Chau is an Associate Professor of Computing at Georgia Tech. He co-directs Georgia Tech's MS Analytics program. 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 CMU's Computer Science Dissertation Award, Honorable Mention. He received awards and grants from NSF, NIH, NASA, DARPA, Intel (Intel Outstanding Researcher), Symantec, Google, Nvidia, IBM, Yahoo, Amazon, Microsoft, eBay, LexisNexis; Raytheon Faculty Fellowship; Edenfield Faculty Fellowship; Outstanding Junior Faculty Award; The Lester Endowment Award; Symantec fellowship (twice); Best student papers at SDM'14 and KDD'16 (runner-up); Best demo at SIGMOD'17 (runner-up); Chinese CHI'18 Best paper. His research led to open-sourced or deployed technologies by Intel (for ISTC-ARSA: ShapeShifter, SHIELD, ADAGIO, MLsploit), Google, Facebook, Symantec (Polonium, AESOP protect 120M people from malware), and Atlanta Fire Rescue Department. His security and fraud detection research made headlines.	en_US
dc.description	Runtime: 59:01 minutes	en_US
dc.description.abstract	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 successful deployment of AI in security settings. We present our joint works with Intel which include the first targeted physical adversarial attack (ShapeShifter) that fools state-of-the-art object detectors; a fast defense (SHIELD) that removes digital adversarial noise by stochastic data compression; and interactive systems (ADAGIO and MLsploit) that further democratize the study of adversarial machine learning and facilitate real-time experimentation for deep learning practitioners. Finally, we also present how scalable interactive visualization can be used to amplify people’s ability to understand and interact with large-scale data and complex models. We sample from projects where interactive visualization has 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; went viral!), 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.	en_US
dc.format.extent	59:01 minutes
dc.identifier.uri	http://hdl.handle.net/1853/61842
dc.language.iso	en_US	en_US
dc.publisher	Georgia Institute of Technology	en_US
dc.relation.ispartofseries	GVU Brown Bag
dc.subject	AI	en_US
dc.subject	Cyber security	en_US
dc.subject	Deep learning	en_US
dc.subject	Visualization	en_US
dc.title	Towards Secure and Interpretable AI: Scalable Methods, Interactive Visualizations, and Practical Tools	en_US
dc.title.alternative	Towards Secure and Interpretable AI...	en_US
dc.type	Moving Image
dc.type.genre	Lecture
dspace.entity.type	Publication
local.contributor.author	Chau, Duen Horng
local.contributor.corporatename	GVU Center
local.relation.ispartofseries	GVU Brown Bag Seminars
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