Here is a brief excerpt from an article by Steven Levy for Wired magazine. To read the complete article, check out others, and obtain information about deep-discount subscriptions, please click here.

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Carson Holgate is training to become a ninja.

Not in the martial arts — she’s already done that. Holgate, 26, holds a second degree black belt in Tae Kwon Do. This time it’s algorithmic. Holgate is several weeks into a program that will inculcate her in an even more powerful practice than physical combat: machine learning, or ML. A Google engineer in the Android division, Holgate is one of 18 programmers in this year’s Machine Learning Ninja Program, which pulls talented coders from their teams to participate, Ender’s Game-style, in a regimen that teaches them the artificial intelligence techniques that will make their products smarter. Even if it makes the software they create harder to understand.

“The tagline is, Do you want to be a machine learning ninja?” says Christine Robson, a product manager for Google’s internal machine learning efforts, who helps administer the program. “So we invite folks from around Google to come and spend six months embedded with the machine learning team, sitting right next to a mentor, working on machine learning for six months, doing some project, getting it launched and learning a lot.”

For Holgate, who came to Google almost four years ago with a degree in computer science and math, it’s a chance to master the hottest paradigm of the software world: using learning algorithms (“learners”) and tons of data to “teach” software to accomplish its tasks. For many years, machine learning was considered a specialty, limited to an elite few. That era is over, as recent results indicate that machine learning, powered by “neural nets” that emulate the way a biological brain operates, is the true path towards imbuing computers with the powers of humans, and in some cases, super humans.

Google is committed to expanding that elite within its walls, with the hope of making it the norm. For engineers like Holgate, the ninja program is a chance to leap to the forefront of the effort, learning from the best of the best. “These people are building ridiculous models and have PhD’s,” she says, unable to mask the awe in her voice. She’s even gotten over the fact that she is actually in a program that calls its students “ninjas.” “At first, I cringed, but I learned to accept it,” she says.

Considering the vast size of Google’s workforce — probably almost half of its 60,000 headcount are engineers — this is a tiny project. But the program symbolizes a cognitive shift in the company. Though machine learning has long been part of Google’s technology — and Google has been a leader in hiring experts in the field — the company circa 2016 is obsessed with it. In an earnings call late last year, CEO Sundar Pichai laid out the corporate mindset: “Machine learning is a core, transformative way by which we’re rethinking how we’re doing everything. We are thoughtfully applying it across all our products, be it search, ads, YouTube, or Play. And we’re in early days, but you will see us — in a systematic way — apply machine learning in all these areas.”

Obviously, if Google is to build machine learning in all its products, it needs engineers who have mastery of those techniques, which represents a sharp fork from traditional coding. As Pedro Domingos, author of the popular ML manifesto The Master Algorithm, writes, “Machine learning is something new under the sun: a technology that builds itself.” Writing such systems involves identifying the right data, choosing the right algorithmic approach, and making sure you build the right conditions for success. And then (this is hard for coders) trusting the systems to do the work.

“The more people who think about solving problems in this way, the better we’ll be,” says a leader in the firm’s ML effort, Jeff Dean, who is to software at Google as Tom Brady is to quarterbacking in the NFL. Today, he estimates that of Google’s 25,000 engineers, only a “few thousand” are proficient in machine learning. Maybe ten percent. He’d like that to be closer to a hundred percent. “It would be great to have every engineer have at least some amount of knowledge of machine learning,” he says.

Does he think that will happen?

“We’re going to try,” he says.

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Here is a direct link to the complete article.

Steven Levy covers the gamut of tech subjects for WIRED, in print and online, and has been contributing to the magazine since its inception. His newest column, Plaintext, will soon only be available to subscribers; sign up here. He has been writing about technology for more than 30 years, writing columns for Rolling Stone and Macworld, leading technology coverage for Newsweek, and cocreating a tech publication, Backchannel, on Medium. (Backchannel was integrated into WIRED in 2017.) He has written seven books, including Hackers, Crypto, Artificial Life, Insanely Great (a history of the Macintosh), and, most recently, In the Plex, “the definitive story of Google.” He attended Temple University and has a master’s degree in literature from Penn State. He works from the New York office.