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.
Days after the paper was published, Hinton received an email from a fellow AI researcher named Kai Yu, who worked for Baidu, the Chinese tech giant. On the surface, Hinton and Yu had little in common. Born in postwar Britain to an upper-crust family of scientists whose influence was matched only by their eccentricity, Hinton had studied at Cambridge, earned a PhD in artificial intelligence from the University of Edinburgh, and spent most of the next four decades as a professor of computer science. Yu was 30 years younger than Hinton and grew up in Communist China, the son of an automobile engineer, and studied in Nanjing and then Munich before moving to Silicon Valley for a job in a corporate research lab. The two were separated by class, age, culture, language, and geography, but they shared a faith in neural networks. They had originally met in Canada at an academic workshop, part of a grassroots effort to revive this nearly dormant area of research across the scientific community and rebrand the idea as “deep learning.” Yu, a small, bespectacled, round-faced man, was among those who helped spread the gospel. When that nine-page paper emerged from the University of Toronto, Yu told the Baidu brain trust they should recruit Hinton as quickly as possible. With his email, Yu introduced Hinton to a Baidu vice president, who promptly offered $12 million to hire Hinton and his students for just a few years of work.
For a moment, it seemed like Hinton and his suitors in Beijing were on the verge of sealing an agreement. But Hinton paused. In recent months, he’d cultivated relationships inside several other companies, both small and large, including two of Baidu’s big American rivals, and they, too, were calling his office in Toronto, asking what it would take to hire him and his students.
Seeing a much wider opportunity, he asked Baidu if he could solicit other offers before accepting the $12 million, and when Baidu agreed, he flipped the situation upside down. Spurred on by his students and realizing that Baidu and its rivals were much more likely to pay enormous sums of money to acquire a company than they were to shell out the same dollars for a few new hires from the world of academia, he created his tiny startup. He called it DNNresearch in a nod to the “deep neural networks” they specialized in, and he asked a Toronto lawyer how he could maximize the price of a startup with three employees, no products, and virtually no history.
As the lawyer saw it, he had two options: He could hire a professional negotiator and risk angering the companies he hoped would acquire his tiny venture, or he could set up an auction. Hinton chose an auction. In the end, four names joined the bidding: Baidu, Google, Microsoft, and a two-year-old London startup called DeepMind, cofounded by a young neuroscientist named Demis Hassabis, that most of the world had never heard of.