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Fortune's Jonathan Vanian with Lookout's Kevin Mahaffey, CloudPassage's Carson Sweet and Mark Terenzoni of Sqrrl at Structure Security Conference on September 28, 2016 in San Francisco. Courtesy of Structure Security

What Companies Get Wrong About Machine Learning

Sep 27, 2016

If you had to pick a tech industry buzzword for 2016, "machine learning" would be a good choice. Every other company, it seems, is packing the phrase into their pitches, and it's having an effect.

According to Carson Sweet of cloud security firm CloudPassage, many companies are asking for machine learning tools to solve problems—even if they don't have a clear idea of what these tools can do.

Speaking at the Structure Security conference on Tuesday in San Francisco, Sweet and executives from two other cyber-security firms explained some common misconceptions about machine learning. One of these is that machine learning is the same thing as "artificial intelligence" (another top candidate for buzzword of the year).

As Mark Terenzoni of threat detection firm Sqrrl explained, AI is like building a brain, but one that is unable to produce deterministic outcomes (ones that will produce a predictable outcome) — that's why mischief makers were able to manipulate Microsoft's AI chat bot into spewing racist comments.

Machine learning, on the other hand, results in predictable responses and useful predictions. It can detect patterns in giant amounts of data and even present the results in visual graphics that highlight the most salient information.

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But there are important limits to machine learning, and the biggest of these is that it still requires humans to frame the right question.

"Machine learning is the tip of spear, but you have to do a lot of curating to create a model that makes sense to a security analyst," said Terenzoni.

Kevin Mahaffey of mobile security firm Lookout (which helped expose that notorious iPhone bug) likewise noted that firms need "clean data" to feed machine learning algorithms. Simply shoveling random stacks of information, he said, will produce a "garbage in, garbage out" result.

Mahaffey, in response to a question from moderator Jonathan Vanian of Fortune, also clarified the difference between "machine learning" and "deep learning." It turns to be a question of scale: deep learning describes the recent breakthroughs in computer power and cost that makes it possible for machine learning tools to explore millions of parameters.

Mahaffey, however, cautioned that while deep learning represents a remarkable technology, many firms still need to learn the basics of machine learning.

"We’re asking ' how many grams of Kale do you want in your smoothies this morning'—while most organizations are still smoking a pack of cigarettes a day," joked Mahaffey.

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