Security and Safety in AI: Adversarial Examples, Bias and Trust with Moustapha Cissé

    800 800 This Week in Machine Learning & AI

    In this episode I’m joined by Moustapha Cissé, Research Scientist at Facebook AI Research Lab (or FAIR) Paris.

    Moustapha’s broad research interests include the security and safety of AI systems, and we spend some time discussing his work on adversarial examples and systems that are robust to adversarial attacks. More broadly, we discuss the role of bias in datasets, and explore his vision for models that can identify these biases and adjust the way they train themselves in order to avoid taking on those biases.

    Enter Our #MyAI Contest!

    In last week’s interviews, our AI in Consumer Electronics series, you heard from some great people who helped me explore some of the very cool ways that machine learning and AI are being used to enhance our everyday lives.

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    Conference Update

    Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you’ll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI’s latest developments, separate what’s hype and what’s really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML. Early price ends February 2!

    About Moustapha

    Mentioned in the Interview

    “More On That Later” by Lee Rosevere licensed under CC By 4.0

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