The dirty secret of machine learning

David Beyer talks about AI adoption challenges, who stands to benefit most from the technology, and what's missing from the conversation.

By Jenn Webb
February 13, 2017
Les Petits Secrets, l'illustration Européenne 1872 no.34 page 272. Les Petits Secrets, l'illustration Européenne 1872 no.34 page 272. (source: loki11 on Wikimedia Commons)

“Too many businesses now are pitching AI almost as though it’s batteries included. I think that’s dangerous because it’s going to potentially lead to over-investment in things that overpromise. Then when they under-deliver, it has a deflationary effect on people’s attitudes toward the space. It almost belittles the problem itself. Not everything requires the latest whiz-bang technology. In fact, the dirty secret of machine learning—and, in a way, venture capital—is so many problems could be solved by just applying simple regression analysis. Yet, very few people, very few industries do the bare minimum.”

“In terms of industry adoption, I think data readiness is a pretty good predictor of how quickly a particular business, and by extension an industry, will adopt machine learning. You can buy all the software that you want; if you still are struggling to pull data from disparate silos, clean it up, make sense of it, you’re not going to get anywhere. In terms of that index of readiness, I’m hoping that I or someone can do research on trying to feed data readiness, data maturity into that equation to help predict how quickly a particular industry will adopt it.”—David Beyer

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Post topics: AI & ML

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