Machine learning has enabled many advances in processing visual, language, and other digital data signals and, as a result, is quickly becoming integrated in a variety of real-world systems with important societal and business purposes. However, as with any computer technology deployed at scale or in critical domains, ML systems face motivated adversaries who might wish to cause undesired behavior or violate security restrictions. In this session, participants will discuss the security challenges of today\u2019s AI-driven systems and opportunities to mitigate adversarial attacks for more robust systems.<\/p>\n
Session Lead: Emre Kiciman, Microsoft<\/p>\n
Speaker: Aleksander M\u0105dry, Massachusetts Institute of Technology
\nTalk Title: What Do Our Models Learn?<\/p>\n
Speaker: Dawn Song, University of California, Berkeley
\nTalk Title: AI & Security: Challenges, Lessons & Future Directions<\/p>\n
Speaker: Jerry Li, Microsoft
\nTalk Title: Algorithmic Aspects of Secure Machine Learning<\/p>\n
Q&A panel with all 3 speakers<\/p>\n","protected":false},"excerpt":{"rendered":"
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