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 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.
An amazing account of the events that shaped deep learning as we know it. https://t.co/6LMWRmwCgW
— Georgios Voulgaris (@GeorgiosVoulga1) March 17, 2021