Hilary Mason on taking big data from theory to reality

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If you’re interested in assessing how and when a given data technology — deep learning, machine intelligence, natural language generation — can move from the theoretical to commercial use,  Hilary Mason may have the best job around. This week’s guest, the CEO and Founder of Fast Forward Labs, talks about how that startup taps into a wide array of expertise sources– from academic and commercial research, the open source world to “outsider art” in the realms of spam and malware, to come up with new ideas for applications.

One natural language generation (NLG) project, for example, lets a person who wants to sell her house, enter the parameters — square footage, number of rooms etc — then step back to let the system write up the ad for that property. (As a person who makes her living from writing words, all I can say is: “ick.”)

She’s also got an interesting take on opportunities in the internet of things — a term she dislikes — and why the much-maligned title of data scientist has validity. Mason is really interesting so if you’re pressed for time, check out at least the second half of this podcast. And to hear more from her, be sure to sign up for Structure Data in March, where she will return to speak in March.

Shivon Zilis, VC, Bloomberg Beta; Sven Strohband, Partner and CTO, Khosla Ventures; Hilary Mason, Data Scientist in Residence, Accel Partners; Jalak Jobanputra, Managing Partner, FuturePerfect Ventures.

Shivon Zilis, VC, Bloomberg Beta; Sven Strohband, Partner and CTO, Khosla Ventures; Hilary Mason, Data Scientist in Residence, Accel Partners; Jalak Jobanputra, Managing Partner, FuturePerfect Ventures.

As for segment one, Derrick and I discuss Datapipe’s acquisition of GoGrid, the first cloud consolidation move of the new year; the long-awaited Box IPO; and an itty bit on Microsoft’s foray into augmented reality.

So get cozy and take a listen.

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Hosts: Barb Darrow and Derrick Harris.

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Peter Fretty

For most organizations this step is where its crucial to have data scientists working in conjunction with the lines of business so that the models are developed in a manner that putting them into action is a realistic venture. I would suggest as organizations hit this point, its a good time to bring their tool providers into the equation to help with the next step. Also, starting with smaller projects can help build momentum and confidence needed to venture into the unknown.

Peter Fretty, IDG blogger working on behalf of SAS

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