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Google, 'machine learning' and the future of coding

Google, 'machine learning' and the future of coding
Published 24 Jun 2016   Follow @BrendanTN_

If you are not already, it's worth following the work of Steven Levy, a long-time technology journalist that has been covering Silicon Valley and the American technology sector for over 20 years (his work and criticism of Apple are particularly noteworthy). He now writes for Medium on its Backchannel site and recently published a long-form piece on Google and its attempts to integrate 'machine learning' throughout all aspects of the company. The progress on artificial intelligence has been getting more attention recently, primarily because of comments from figures like Elon Musk and Stephen Hawking (some other great pieces on this broad area can be found here and here). But a lot of the work is taking place in massive companies like Google. Below are a few extracts from Levy's recent piece.

First, what exactly is 'machine learning'?:

The example Giannandrea cites to demonstrate machine learning power is Google Photos, a product whose definitive feature is an uncanny?—?maybe even disturbing?—?ability to locate an image of something specified by the user. Show me pictures of border collies. “When people see that for the first time they think something different is happening because the computer is not just computing a preference for you or suggesting a video for you to watch,” says Giannandrea. “It’s actually understanding what’s in the picture.” He explains that through the learning process, the computer “knows” what a border collie looks like, and it will find pictures of it when it’s a puppy, when its old, when it’s long-haired, and when it’s been shorn. A person could do that, of course. But no human could sort through a million examples and simultaneously identify ten thousand dog breeds. But a machine learning system can. If it learns one breed, it can use the same technique to identify the other 9999 using the same technique. “That’s really what’s new here,” says Giannandrea. “For those narrow domains, you’re seeing what some people call super human performance in these learned systems.”

Google seems to be championing a sort-of 'democratisation' of machine-learning skills among its engineers, as well as throughout the wider tech community:

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...

...“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.

Also, it seems that machine learning responses follow human nature more closely than first thought:

When the team began testing Smart Reply, though, users noted a weird quirk: it would often suggest inappropriate romantic responses. “One of the failure modes was this really hysterical tendency for it to say, ‘I love you’ whenever it got confused,” says Corrado. “It wasn’t a software bug?—?it was an error in what we asked it to do.” The program had somehow learned a subtle aspect of human behavior: “If you’re cornered, saying, ‘I love you’ is a good defensive strategy.” Corrado was able to help the team tamp down the ardor.

Lastly, this takeaway likely foreshadows what will be written in terms of the history of the evolution of coding:

“It was significant to the company that we were successful in making search better with machine learning,” says Giannandrea. “That caused a lot of people to pay attention.” Pedro Domingos, the University of Washington professor who wrote The Master Algorithm, puts it a different way: “There was always this battle between the retrievers and the machine learning people,” he says. “The machine learners have finally won the battle.”

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