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Adversarial Examples Are Not Bugs, They Are Features with Aleksander Madry - #369
FromThe TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Adversarial Examples Are Not Bugs, They Are Features with Aleksander Madry - #369
FromThe TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
ratings:
Length:
41 minutes
Released:
Apr 27, 2020
Format:
Podcast episode
Description
Today we’re joined by Aleksander Madry, Faculty in the MIT EECS Department, a member of CSAIL and of the Theory of Computation group. Aleksander, whose work is more on the theoretical side of machine learning research, walks us through his paper “Adversarial Examples Are Not Bugs, They Are Features,” which was published previously presented at last year’s NeurIPS conference. In our conversation, we explore the idea of adversarial examples in machine learning systems being features, with results that might be undesirable, but still working as designed. We talk through what we expect these systems to do, vs what they’re actually doing, if we’re able to characterize these patterns, and what makes them compelling, and if the insights from the paper will inform opinions on either side of the deep learning debate. The complete show notes for this can be found at twimlai.com/talk/369.
Released:
Apr 27, 2020
Format:
Podcast episode
Titles in the series (100)
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