Artificial Intelligence has suddenly gone from the fringes of science to being everywhere. So how did we get here? And where's this all heading? In this new series of Science Friction, we're finding out.
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[14] Been Kim - Interactive and Interpretable Machine Learning Models
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Manage episode 302418431 series 2982803
Content provided by The Thesis Review and Sean Welleck. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by The Thesis Review and Sean Welleck or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Been Kim is a Research Scientist at Google Brain. Her research focuses on designing high-performance machine learning methods that make sense to humans. Been's PhD thesis is titled "Interactive and Interpretable Machine Learning Models for Human Machine Collaboration", which she completed in 2015 at MIT. We discuss her work on interpretability, including her work in the thesis on the Bayesian Case Model and its interactive version, as well as connections with her subsequent work on black-box interpretability methods that are used in many real-world applications. Episode notes: https://cs.nyu.edu/~welleck/episode14.html Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter, and find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html Support The Thesis Review at www.buymeacoffee.com/thesisreview
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47 episodes
MP3•Episode home
Manage episode 302418431 series 2982803
Content provided by The Thesis Review and Sean Welleck. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by The Thesis Review and Sean Welleck or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Been Kim is a Research Scientist at Google Brain. Her research focuses on designing high-performance machine learning methods that make sense to humans. Been's PhD thesis is titled "Interactive and Interpretable Machine Learning Models for Human Machine Collaboration", which she completed in 2015 at MIT. We discuss her work on interpretability, including her work in the thesis on the Bayesian Case Model and its interactive version, as well as connections with her subsequent work on black-box interpretability methods that are used in many real-world applications. Episode notes: https://cs.nyu.edu/~welleck/episode14.html Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter, and find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html Support The Thesis Review at www.buymeacoffee.com/thesisreview
…
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47 episodes
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