Evaluating Model Explainability Methods with Sara Hooker - TWiML Talk #189
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) - Un pódcast de Sam Charrington
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In this, the first episode of the Deep Learning Indaba series, we’re joined by Sara Hooker, AI Resident at Google Brain. I spoke with Sara in the run-up to the Indaba about her work on interpretability in deep neural networks. We discuss what interpretability means and nuances like the distinction between interpreting model decisions vs model function. We also talk about the relationship between Google Brain and the rest of the Google AI landscape and the significance of the Google AI Lab in Accra, Ghana.
