How Facebook’s Yann LeCun is charting a path to human-level artificial intelligence
Facebook’s chief AI scientist told TNW about his work at FAIR, the social network’s research lab
FAIR origins
FAIR began life after Mark Zuckerberg and Facebook CTO Michael Schroepfer identified AI as critical to the company’s long-term future.
“They were 100% right about this,” says LeCun, now Facebook’s chief AI scientist.
They decided tocreate a new research lab from scratch — and handpicked LeCun to lead it.
“What attracted me was there was a lot of opportunity. But also, I had somewhat of a carte blanche to organize the lab in the way I thought would be the most successful.”
LeCun immediately made open research the cornerstone of his plans. FAIR now publishes almost all of its work, and open sources the majority of its code, datasets, and tools — such asPyTorch, a toolkit for quickly creating new machine learning models.
The motivation behind this approach isn’t purely altruistic. It allows Facebook to influence what researchers work on, foster collaborations with academia and industry, and attract talent to FAIR.
“The currency for a scientist is his or her intellectual impact on the community,” says LeCun. “So if you want to hire the best scientists in the world, and you tell them you can come here but you can’t talk about what you do — they’re not going to join.”
But isn’t he worried that Facebook’s rivals could steal the lab’s secrets?
“That’s fine. Why would it be bad? The value of a lot of the technology we produce is multiplied by a coefficient that is basically Facebook’s ability to deploy them in its services. It’s much more difficult — even if it’s open source — for another company to deploy it in ways that would compete directly with us.”
On the contrary, sharing the research helps Facebook improve its own products:
AI advances and challenges
Much of the fundamental research behind the AI in Facebook products nowtakes place atFAIR. Among its most impactful creations areMemory Networks, which improve how machines talk to people by helping them retain enough data to answer general knowledge questions. An influential2014 papershowed how the approach could answer questions about the plot of theLord of the Rings.
“DeepMind was working on a very similar idea exactly simultaneously,” LeCun recalls. “We posted a paper on arXiv, and then three days later DeepMind posted their paper, because they didn’t want to be completely scooped. ”
At that time, DeepMind was one of the driving forces behind London’s emergence as a global AI hotbed. FAIR had initially planned to join them by launching its first European lab in the city.But after studying the availability of talent, the company pivoted to Paris.
“It was a more difficult turf to enter, whereas continental Europe was completely open,” says LeCun. “There was essentially no ambitious fundamental research lab in AI or even in information technology in continental Europe really.”
The lab has since become one of FAIR’s largest AI research centers and the birthplace of many of its computer science breakthroughs. Its recent innovations include Facebook AI Similarity Search (FAISS), a tool for quickly finding videos, text, or images that are similar to each other. The system can be usedto recommendInstagram posts or to detect extremist propaganda videos that have been tweaked and then reposted to evade removal.
FAIR has also helped Facebook use AI to find hate speech on the platform. But these systems have received criticism fornot detecting every language.
However, self-supervised learning isexpanding the linguistic rangeof the tools. But LeCun believes the technique will only reach its potential once it can reason like a human.
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Story byThomas Macaulay
Thomas is a senior reporter at TNW. He covers European tech, with a focus on AI, cybersecurity, and government policy.Thomas is a senior reporter at TNW. He covers European tech, with a focus on AI, cybersecurity, and government policy.
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