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In episode 34 of The Gradient Podcast, Daniel Bashir speaks to Sara Hooker.
Sara (@sarahookr) leads Cohere for AI and is a former Research Scientist at Google. Sara founded a Bay Area non-profit called Delta Analytics, which works with non-profits and communities to build technical capacity. She is also one of the co-founders of the Trustworthy ML Initiative, an active participant of the ML Collective research group, and a host of the underrated ML podcast.
Subscribe to The Gradient Podcast: Apple Podcasts | Spotify | Pocket Casts | RSSFollow The Gradient on Twitter
Sections:
(00:00) Intro(02:20) Podcasting gripe-fest(06:00) Sara’s journey: from economics to AI(09:15) Economics vs. AI research(12:45) The Hardware Lottery(19:15) Towards better hardware benchmarks(26:00) Getting away from the hardware lottery(32:30) The myth of compact, interpretable, robust, performant DNNs(35:15) Top-line metrics vs. disaggregated metrics(39:20) Solving memorization in the data pipeline, noisy examples(45:35) Cohere For AI
Episode Links:
* Cohere for AI
* Sara’s Homepage
4.7
4747 ratings
In episode 34 of The Gradient Podcast, Daniel Bashir speaks to Sara Hooker.
Sara (@sarahookr) leads Cohere for AI and is a former Research Scientist at Google. Sara founded a Bay Area non-profit called Delta Analytics, which works with non-profits and communities to build technical capacity. She is also one of the co-founders of the Trustworthy ML Initiative, an active participant of the ML Collective research group, and a host of the underrated ML podcast.
Subscribe to The Gradient Podcast: Apple Podcasts | Spotify | Pocket Casts | RSSFollow The Gradient on Twitter
Sections:
(00:00) Intro(02:20) Podcasting gripe-fest(06:00) Sara’s journey: from economics to AI(09:15) Economics vs. AI research(12:45) The Hardware Lottery(19:15) Towards better hardware benchmarks(26:00) Getting away from the hardware lottery(32:30) The myth of compact, interpretable, robust, performant DNNs(35:15) Top-line metrics vs. disaggregated metrics(39:20) Solving memorization in the data pipeline, noisy examples(45:35) Cohere For AI
Episode Links:
* Cohere for AI
* Sara’s Homepage
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