AI became real in 2012 when a tiny group of coders finally succeeded in building a neural net that actually worked. For years, this tech remained illusive but no longer. In the wonderful
Wired article titled
The Secret Auction That Set Off the Race for AI Supremacy, the high level stakes auction as to what entity would gain access to this transformative technology would change computing and society forever.
That fall—before lying down in the back of the bus from Toronto to New York, taking the train 2,700 miles to Truckee, California, at the crest of the Sierra Nevadas, and then stretching across the back seat of a taxi for the hour-long drive to South Lake Tahoe—Hinton had 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, DNN-research, which was even less inviting than the sparse page. The 64-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 South Lake Tahoe, some of the biggest tech companies in the world were gearing up for a contest to acquire his newborn startup.
TWO MONTHS EARLIER, Hinton and his students had changed the way machines saw the world. They 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’d hoped. By the new millennium, most researchers had given up on the idea, convinced it was a technological dead end and bewildered by the 50-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, however, never quit pursuing his dream.
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 imagined their thinking went. But with a nine-page paper that Hinton and his students unveiled in the fall of 2012, detailing their breakthrough, they announced to the world that neural networks were indeed as powerful as Hinton had long claimed they would be.
Read Wired's long & detailed piece on the start of AI, a story reminding one of the intensity of a high stakes poker game finally resulting as to what entity got the gold ring, thus cementing their stake as the major player in an open ended tech fraught with potential and danger of the third kind.
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