Large-scale machine learning and AI on multi-modal data

The Data Exchange Podcast: Bob Friday, Chief AI Officer of Juniper, on incorporating machine learning and AI into large-scale, real-time applications.

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This week’s guest is Bob Friday1, Chief AI Officer at Mist Systems a Juniper Company.  Bob is a serial entrepreneur and seasoned technologist, and at Mist  his team uses data technologies, machine learning , and AI to “optimize user experiences and simplify operations across the wireless access, wired access, and SD-WAN domains”. This means having foundational data infrastructure in place to ingest, process, store, and analyze hundreds of terabytes of data daily.

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Bob and his team build models from structured, semi-structured, and unstructured data. They have deployed anomaly detection models that rely on deep learning (LSTMs) and have begun exploring the use of graph neural networks for a variety of use cases. They have also built and deployed systems that use recent advances in natural language models. Their virtual assistant provides insight and guidance to IT staff via a natural language conversational interface.

Bob Friday:

Machine Learning has been around for a long time, we’ve solved a lot of problems. I think the big transition is “are you really doing something on par with  humans”? … We’re basically trying to take domain expertise, and stick it into Marvis. We’re trying to build something that actually does something on par with a network domain expert.

… I’m a big fan of what they call human-assisted AI. …  you’re talking about an AI assistant, where the human domain expert still solves the corner cases. You use AI to solve 80% of the problem, but there’s still 20% of the problem that requires some domain expert. And to some extent, that’s what we’re doing. Marvis solves about 70% of the problem;  30% of the time we still need a domain expert to come in and help push it over the finish line.

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[1] This post and episode are part of a collaboration between Gradient Flow and Juniper. See our statement of editorial independence.

[Image by Oleg Gamulinskiy from Pixabay.]