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Soon We Won’t Program Computers; We’ll Train Them Like Dogs

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Our machines speak a different language now, one that even the best coders can’t fully understand.

Over the past several years, the biggest tech companies in Silicon Valley have aggressively pursued an approach to computing called machine learning. In traditional programming, an engineer writes explicit, step-by-step instructions for the computer to follow. With machine learning, programmers don’t encode computers with instructions. They train them. If you want to teach a neural network to recognize a cat, for instance, you don’t tell it to look for whiskers, ears, fur, and eyes. You simply show it thousands and thousands of photos of cats, and eventually it works things out. If it keeps misclassifying foxes as cats, you don’t rewrite the code. You just keep coaching it.

This approach is not new—it’s been around for decades—but it has recently become immensely more powerful, thanks in part to the rise of deep neural networks, massively distributed computational systems that mimic the multilayered connections of neurons in the brain. And already, whether you realize it or not, machine learning powers large swaths of our online activity. Facebook uses it to determine which stories show up in your News Feed, and Google Photos uses it to identify faces. Machine learning runs Microsoft’s Skype Translator, which converts speech to different languages in real time. Self-driving cars use machine learning to avoid accidents. Even Google’s search engine—for so many years a towering edifice of human-written rules—has begun to rely on these deep neural networks. In February the company replaced its longtime head of search with machine-learning expert John Giannandrea, and it has initiated a major program to retrain its engineers in these new techniques. “By building learning systems,” Giannandrea told reporters this fall, “we don’t have to write these rules anymore.”

But here’s the thing: With machine learning, the engineer never knows precisely how the computer accomplishes its tasks. The neural network’s operations are largely opaque and inscrutable. It is, in other words, a black box. And as these black boxes assume responsibility for more and more of our daily digital tasks, they are not only going to change our relationship to technology—they are going to change how we think about ourselves, our world, and our place within it.

If in the old view programmers were like gods, authoring the laws that govern computer systems, now they’re like parents or dog trainers. And as any parent or dog owner can tell you, that is a much more mysterious relationship to find yourself in.

Just as Newtonian physics wasn’t obviated by quantum mechanics, code will remain a powerful tool set to explore the world.

Andy Rubin is an inveterate tinkerer and coder. The cocreator of the Android operating system, Rubin is notorious in Silicon Valley for filling his workplaces and home with robots. He programs them himself. “I got into computer science when I was very young, and I loved it because I could disappear in the world of the computer. It was a clean slate, a blank canvas, and I could create something from scratch,” he says. “It gave me full control of a world that I played in for many, many years.”

Now, he says, that world is coming to an end. Rubin is excited about the rise of machine learning—his new company, Playground Global, invests in machine-learning startups and is positioning itself to lead the spread of intelligent devices—but it saddens him a little too. Because machine learning changes what it means to be an engineer.

“People don’t linearly write the programs,” Rubin says. “After a neural network learns how to do speech recognition, a programmer can’t go in and look at it and see how that happened. It’s just like your brain. You can’t cut your head off and see what you’re thinking.” When engineers do peer into a deep neural network, what they see is an ocean of math: a massive, multilayer set of calculus problems that—by constantly deriving the relationship between billions of data points—generate guesses about the world.

Artificial intelligence wasn’t supposed to work this way. Until a few years ago, mainstream AI researchers assumed that to create intelligence, we just had to imbue a machine with the right logic. Write enough rules and eventually we’d create a system sophisticated enough to understand the world. They largely ignored, even vilified, early proponents of machine learning, who argued in favor of plying machines with data until they reached their own conclusions. For years computers weren’t powerful enough to really prove the merits of either approach, so the argument became a philosophical one. “Most of these debates were based on fixed beliefs about how the world had to be organized and how the brain worked,” says Sebastian Thrun, the former Stanford AI professor who created Google’s self-driving car. “Neural nets had no symbols or rules, just numbers. That alienated a lot of people.”

The implications of an unparsable machine language aren’t just philosophical. For the past two decades, learning to code has been one of the surest routes to reliable employment—a fact not lost on all those parents enrolling their kids in after-school code academies. But a world run by neurally networked deep-learning machines requires a different workforce. Analysts have already started worrying about the impact of AI on the job market, as machines render old skills irrelevant. Programmers might soon get a taste of what that feels like themselves.

“I was just having a conversation about that this morning,” says tech guru Tim O’Reilly when I ask him about this shift. “I was pointing out how different programming jobs would be by the time all these STEM-educated kids grow up.” Traditional coding won’t disappear completely—indeed, O’Reilly predicts that we’ll still need coders for a long time yet—but there will likely be less of it, and it will become a meta skill, a way of creating what Oren Etzioni, CEO of the Allen Institute for Artificial Intelligence, calls the “scaffolding” within which machine learning can operate. Just as Newtonian physics wasn’t obviated by the discovery of quantum mechanics, code will remain a powerful, if incomplete, tool set to explore the world. But when it comes to powering specific functions, machine learning will do the bulk of the work for us.

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