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The making of a robot

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A cyclist passes an autonomous grocery delivery robot in Singapore earlier this month (Reuters image)A cyclist passes an autonomous grocery delivery robot in Singapore earlier this month (Reuters image)A cyclist passes an autonomous grocery delivery robot in Singapore earlier this month (Reuters image)

By the time he stepped onto the bus in downtown Toronto, bound for Lake Tahoe, Geoff Hinton hadn’t sat down for seven years. “I last sat down in 2005,” he often said, “and it was a mistake.” He first injured his back as a teenager, while lifting a space heater for his mother. As he reached his late fifties, he couldn’t sit down without risking a slipped disk, and if it slipped, the pain could put him in bed for weeks. So he stopped sitting down. He used a standing desk inside his office at the University of Toronto. When eating meals, he put a small foam pad on the floor and knelt at the table, poised like a monk at the altar. He lay down when riding in cars, stretching across the back seat. And when traveling longer distances, he took the train. He couldn’t fly, at least not with the commercial airlines, because they made him sit during takeoff and landing. “It got to the point where I thought I might be crippled—that I wouldn’t be able to make it through the day—so I took it seriously,” he says. “If you let it completely control your life, it doesn’t give you any problems.”

That fall, before lying down at the back of the bus for the trip to New York, taking the train all the way to Truckee, California, at the crest of the Sierra Nevadas, and then stretching across the back seat of a taxi for the thirty-minute drive up the mountain to Lake Tahoe, he created a new company. It included only two other people, both young graduate students in his lab at the university. It made no products. It had no plans to make a product. And its website offered nothing but a name, DNNresearch, which was even less appealing than the website. The sixty-four-year-old Hinton—who seemed so at home in academia, with his tousled gray hair, wool sweaters, and two-steps-ahead-of-you sense of humor—wasn’t even sure he wanted to start a company until his two students talked him into it. But as he arrived in Lake Tahoe, one of the largest companies in China had already offered $12 million for his newborn start-up, and soon three other companies would join the bidding, including two of the largest in the United States.

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He was headed for Harrah’s and Harvey’s, the two towering casinos at the foot of the ski mountains on the south side of the lake. Rising up over the Nevada pines, these twin slabs of glass, steel, and stone also served as convention centers, offering hundreds of hotel rooms, dozens of meeting spaces, and a wide variety of (second-rate) restaurants. That December, they hosted an annual gathering of computer scientists called NIPS. Short for Neural Information Processing Systems—a name that looked deep into the future of computing—NIPS was a conference dedicated to artificial intelligence. A London-born academic who had explored the frontiers of AI at universities in Britain, the United States, and Canada since the early 1970s, Hinton made the trip to NIPS nearly every year. But this was different. Although Chinese interest in his company was already locked in, he knew that others were interested, too, and NIPS seemed like the ideal venue for an auction. Two months earlier, Hinton and his students had changed the way machines saw the world. They had built what was called a neural network, a mathematical system modeled on the web of neurons in the brain, and it could identify common objects—like flowers, dogs, and cars—with an accuracy that had previously seemed impossible. As Hinton and his students showed, a neural network could learn this very human skill by analyzing vast amounts of data. He called this “deep learning,” and its potential was enormous. It promised to transform not just computer vision but everything from talking digital assistants to driverless cars to drug discovery.

The idea of a neural network dated back to the 1950s, but the early pioneers had never gotten it working as well as they had hoped. By the new millennium, most researchers had given up on the idea, convinced it was a technological dead end and bewildered by the fifty-year-old conceit that these mathematical systems somehow mimicked the human brain. When submitting research papers to academic journals, those who still explored the technology would often disguise it as something else, replacing the words “neural network” with language less likely to offend their fellow scientists. Hinton remained one of the few who believed it would one day fulfill its promise, delivering machines that could not only recognize objects but identify spoken words, understand natural language, carry on a conversation, and maybe even solve problems humans couldn’t solve on their own, providing new and more incisive ways of exploring the mysteries of biology, medicine, geology, and other sciences. It was an eccentric stance even inside his own university, which spent years denying his standing request to hire another professor who could work alongside him in this long and winding struggle to build machines that learned on their own.

“One crazy person working on this was enough,” he says. But in the spring and summer of 2012, Hinton and his two students made a breakthrough: They showed that a neural network could recognize common objects with an accuracy beyond any other technology. With the nine-page paper they unveiled that fall, they announced to the world that this idea was as powerful as Hinton had long claimed it would be.

Excerpted with permission from Penguin Random House

Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World
Cade Metz
Penguin Random House
Pp 384, Rs 799

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