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OCS 2020 Breakout: Kelly Davis

jj profile image JJ ・2 min read

Kelly Davis combines interest/expertise in theoretical physics, higher mathematics, and computer software to solve interesting problems in one or more of these disciplines. Specialties: M-theory, C++, superstring theory, Java, differential topology, JavaScript, Kirby calculus, distributed systems, smooth 4-manifolds, rendering API design, TQFT , CUDA, quantum gravity, NLP, renormalization, server side programming...

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Kelly Davis ran the Machine Learning group at Mozilla -- learn about how open ML and AI technologies are shaping the future of data and business models.

You have a fascinating background in open-source machine learning. Can you tell us about your background academically, and then going into what you do now? - 0:25

Dig into Mozilla more. Can you explain what Mozilla has done with machine learning over the last 5 years, and what interesting work comes from that? - 5:40

How does machine learning play into software source code evolution? What about source code as a data asset to run into machine learning models? Is that a thing? - 8:52

We’ve talked a little bit about proprietary vs open source machine learning models. Can you talk about the importance of having open source machine learning models and what type of collaboration happens there? - 10:29

Tell me about the advancements your seeing in democratizing/simplifying machine learning for non-experts. - 13:05

Tell us about what you’re working on these days. What’s been exciting for you and capturing your attention? - 14:34

I’d like to ask you a final thing related to open core. You’ve shared how in opening up the machine learning code, you create opportunities for people to explore open core businesses. Could you elaborate on that? - 15:57

(JJ elaborates on Kelly’s response, about relationship between machine learning and open-source product development) - 17:24

Concluding remarks - 18:22


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