Just about every AI advance you’ve heard of depends on a breakthrough that’s three decades old. Keeping up the pace of progress will require confronting AI’s serious limitations.
I’m standing in what is soon to be the center of the world, or is perhaps just a very large room on the seventh floor of a gleaming tower in downtown Toronto. Showing me around is Jordan Jacobs, who cofounded this place: the nascent Vector Institute, which opens its doors this fall and which is aiming to become the global epicenter of artificial intelligence.
We’re in Toronto because Geoffrey Hinton is in Toronto, and Geoffrey Hinton is the father of “deep learning,” the technique behind the current excitement about AI. “In 30 years we’re going to look back and say Geoff is Einstein—of AI, deep learning, the thing that we’re calling AI,” Jacobs says. Of the researchers at the top of the field of deep learning, Hinton has more citations than the next three combined. His students and postdocs have gone on to run the AI labs at Apple, Facebook, and OpenAI; Hinton himself is a lead scientist on the Google Brain AI team. In fact, nearly every achievement in the last decade of AI—in translation, speech recognition, image recognition, and game playing—traces in some way back to Hinton’s work.
The Vector Institute, this monument to the ascent of Hinton’s ideas, is a research center where companies from around the U.S. and Canada—like Google, and Uber, and Nvidia—will sponsor efforts to commercialize AI technologies. Money has poured in faster than Jacobs could ask for it; two of his cofounders surveyed companies in the Toronto area, and the demand for AI experts ended up being 10 times what Canada produces every year. Vector is in a sense ground zero for the now-worldwide attempt to mobilize around deep learning: to cash in on the technique, to teach it, to refine and apply it. Data centers are being built, towers are being filled with startups, a whole generation of students is going into the field.
The impression you get standing on the Vector floor, bare and echoey and about to be filled, is that you’re at the beginning of something. But the peculiar thing about deep learning is just how old its key ideas are. Hinton’s breakthrough paper, with colleagues David Rumelhart and Ronald Williams, was published in 1986. The paper elaborated on a technique called backpropagation, or backprop for short. Backprop, in the words of Jon Cohen, a computational psychologist at Princeton, is “what all of deep learning is based on—literally everything.”
When you boil it down, AI today is deep learning, and deep learning is backprop—which is amazing, considering that backprop is more than 30 years old. It’s worth understanding how that happened—how a technique could lie in wait for so long and then cause such an explosion—because once you understand the story of backprop, you’ll start to understand the current moment in AI, and in particular the fact that maybe we’re not actually at the beginning of a revolution. Maybe we’re at the end of one.
The walk from the Vector Institute to Hinton’s office at Google, where he spends most of his time (he is now an emeritus professor at the University of Toronto), is a kind of living advertisement for the city, at least in the summertime. You can understand why Hinton, who is originally from the U.K., moved here in the 1980s after working at Carnegie Mellon University in Pittsburgh.
When you step outside, even downtown near the financial district, you feel as though you’ve actually gone into nature. It’s the smell, I think: wet loam in the air. Toronto was built on top of forested ravines, and it’s said to be “a city within a park”; as it’s been urbanized, the local government has set strict restrictions to maintain the tree canopy. As you’re flying in, the outer parts of the city look almost cartoonishly lush.
Toronto is the fourth-largest city in North America (after Mexico City, New York, and L.A.), and its most diverse: more than half the population was born outside Canada. You can see that walking around. The crowd in the tech corridor looks less San Francisco—young white guys in hoodies—and more international. There’s free health care and good public schools, the people are friendly, and the political order is relatively left-leaning and stable; and this stuff draws people like Hinton, who says he left the U.S. because of the Iran-Contra affair. It’s one of the first things we talk about when I go to meet him, just before lunch....MORE
Thursday, October 26, 2017
Questions America Wants Answered: "Is AI Riding a One-Trick Pony?"
From MIT's Technology Review: