Hinton remained one of the few who believed he would one day fulfill his promise, providing machines that could not only recognize objects, but also identify spoken words, understand natural language, conduct a conversation, and perhaps even solving problems humans couldn’t solve on their own, providing new and more incisive ways to explore the mysteries of biology, medicine, geology, and other sciences. It was an eccentric position even inside his own university, which spent years denying his permanent request to hire another professor who could work alongside him in this long and winding struggle to build machines that learned. by themselves. “One crazy person working on this was enough,” he imagined. But with a nine-page article that Hinton and his students unveiled in the fall of 2012, detailing their breakthrough, they announced to the world that neural networks are indeed as powerful as Hinton had long claimed.
A few days after the article was published, Hinton received an email from an AI researcher named Kai Yu, who worked for Baidu, the Chinese tech giant. At first glance, Hinton and Yu had little in common. Born in post-war Britain into a family of upper crustal scientists whose influence is matched only by their eccentricity, Hinton had studied at Cambridge, earned a doctorate in artificial intelligence from the University. of Edinburgh and spent most of the next four decades as a teacher. computer science. Yu was 30 years younger than Hinton and raised in Communist China, the son of an automotive 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 initially met in Canada at a university workshop, as part of a grassroots effort to revive this almost inactive area of research in the scientific community and rename the idea to “deep learning”. Yu, a short, bespectacled, round-faced man, was among those who helped spread the gospel. When this nine-page article came out of the University of Toronto, Yu told think tank Baidu that they should recruit Hinton as soon as possible. With his email, Yu introduced Hinton to a vice president at Baidu, who quickly offered $ 12 million to hire Hinton and his students for just a few years of work.
For a while, it looked like Hinton and his suitors in Beijing were on the verge of making a deal. But Hinton paused. Over the past few months, he had cultivated connections at several other companies large and small, including two of Baidu’s big American rivals, and they too were calling his office in Toronto, asking what it would take to get the job done. ‘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 turned the situation around. Pushed by his students and realizing that Baidu and his rivals were much more likely to pay huge sums of money to acquire a business than to shell out the same dollars for a few new hires from academia, he created his little Get Started. . He called it DNNresearch in a nod to the “deep neural networks” they specialize in, and he asked a lawyer in Toronto how he could maximize the price of a startup with three employees, no product, and hardly any history.
According to the lawyer, he had two options: he could hire a professional negotiator and risk angering the companies he hoped to acquire his small business, or he could organize an auction. Hinton chose an auction. In the end, four names joined the auction: Baidu, Google, Microsoft, and a two-year-old London start-up called DeepMind, co-founded by a young neuroscientist named Demis Hassabis, whom most countries in the world had never heard of.