“Your interaction with the digital world, your phone, your computer, is going to be transformed,” LeCun told BuzzFeed News of what may be.
FAIR is improving computers’ ability to see, hear, and communicate on their own, and its findings now permeate Facebook’s products, touching everything from the News Feed ranking to cameras and photo filters. And Facebook is investing, big time — not simply because artificial intelligence is interesting, but because it’s necessary. In all corners of tech today, companies are competing on the basis of their AI. Uber’s AI-powered autonomous cars are core to its ride-hailing strategy. Google’s AI-reliant Google Home smart speaker is answering queries users once typed in the search bar (and, long before that, looked up in the encyclopedia). Amazon is building convenience stores with artificially intelligent cashiers in an effort to crack the $674 billion edible grocery market.
And at Facebook, AI is everywhere. Its AI-powered photo filters, for instance, are helping it fend off a challenge from Snapchat. Its AI’s ability to look at pictures, see what’s inside them, and decide what to show you in its feeds that is helping the company provide a compelling experience keeps you coming back. And similar technology is monitoring harassing, terroristic, and pornographic content and flagging it for removal.
“The experiences people have on the whole family of Facebook products depend critically on A,” said Joaquin Candela, the head of Facebook’s Applied Machine Learning group, or AML, which puts research into action on the platform itself. “Today, Facebook could not exist without AI. Period.”
As the field becomes more advanced, Facebook will rely on LeCun and his team to help it stay ahead of competitors, new or current, who are likely to embrace the science.
After years of criticism and marginalization, LeCun finally has it all: 80 researchers, the backing of Facebook’s vast financial resources, and mainstream faith in his work. All he has to do now is deliver.
From an early age, LeCun believed he could get computers to see. Facial recognition and image detection may be standard today, but when LeCun was a university student in Paris in the early 1980s, computers were effectively blind, unable to make sense of anything within images or to figure out what was appearing inside their cameras’ lenses. It was in college that LeCun came across an approach to the field that had remained largely unexplored since the 1960s, but that he thought could potentially “allow machines to learn many tasks, including perception.”
The approach, called an artificial neural network, takes systems of small, interconnected sensors and has them break down content like images into tiny parts, then identify patterns and decide what they’re seeing based on their collective inputs. After reading the arguments against neural nets — namely that they were hard to train and not particularly powerful— LeCun decided to press ahead anyway, pursuing a PhD where he’d focus on them despite the doubts. “I just didn’t believe it,” he said of the criticism.
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Hard times in the artificial intelligence field occur with such frequency and intensity that they have their own special name: AI Winter. These periods come about largely when researchers’ results don’t live up to their boasts, making it seem like the science doesn’t work, causing funding and interest to dry up as a result, and technological progress along with it.
LeCun has seen his fair share of AI Winter. After settling into an AI research job at Bell Labs in the mid ’90s, internal strife at AT&T caused his team there to come apart just as it was rolling out check-reading ATMs — neural-net-powered technology that’s still in use today — right as LeCun believed it was making clear progress. “The whole project was disbanded essentially on the day that it was becoming really successful,” LeCun said. “This was really depressing.”
At the same time, other methods were gaining favor with mainstream researchers. These methods would later fall back out of favor, but their rise was enough to push neural nets — and LeCun, their longtime champion — to the margins of the field. In the early 2000s, other academics wouldn’t even allow him to present his papers at their conferences. “The computer-vision community basically rejected him,” Geoff Hinton, a neural net pioneer who’s currently an engineering fellow at Google and a professor at the University of Toronto, told BuzzFeed News. “The view was that he was carrying on doing things that had been promising in the ’80s but he should have got over it by now,” Hinton explained.
“That’s not the view anymore,” he added.
Other neural net researchers encountered similar problems at the time. Yoshua Bengio, a professor at the University of Montreal and head of Montreal Institute for Learning Algorithms, had a hard time finding grad students willing to work with him. “I had to twist the arms of my students to work in this area because they were scared of not having a job when they would finish their PhD,” he told BuzzFeed News.
In 2003, LeCun laid the foundation for his redemption. That year, he joined New York University’s faculty and also got together with Hinton and Bengio in a largely informal coalition to revive neural nets. “We started what I’ve been calling the Deep Learning Conspiracy,” LeCun said with a smile.
