Facebook is known for a variety of mantras embedded in its culture, often spelled out on signs at its offices or recited by CEO Mark Zuckerberg and other executives: "Code wins arguments," "Move fast and break things," or "Done is better than perfect."

A sign on the wall at the company's New York office perfectly sums up the approach Yann LeCun brings to his leadership of Facebook's nascent efforts in the field of artificial intelligence and machine learning: "Always be Open."
Artificial intelligence has become a vital part of scaling Facebook. It's already being used to recognize the faces of your friends in photographs, and curate your newsfeed. DeepText, an engine for reading text that was unveiled last week, can understand "with near-human accuracy" the content in thousands of posts per second, in more than 20 different languages. Soon, the text will be translated into a dozen different languages, automatically. Facebook is working on recognizing your voice and identifying people inside of videos so that you can fast forward to the moment when your friend walks into view.

Facebook wants to dominate in AI and machine learning, just as it already does in social networking and instant messaging. The company has hired more than 150 people devoted solely to the field, and says it's tripled its investment in processing power for research—though it won't say how much that investment is.

If the mobile cloud was the previous era of computing, the next will be the era of AI, says Jen-Hsun Huang, the CEO of Nvidia, one of the world's largest makers of graphics processors and a partner in Facebook's open-source hardware design. "It is the most important computing development in the last 20 years, and Facebook and others are going to have to race to make sure that AI's a core competency."

Yet Facebook, which only seriously entered the field less than three years ago, will need more than money to compete, since it's one of technology's hottest fields right now. "They were a late comer," says Pedro Domingos, a professor of computer science at the University of Washington and the author of The Master Algorithm. "Companies like Google and Microsoft were far ahead." They've been building intelligent software since well before Mark Zuckerberg announced plans to program an intelligent butler that would control his home.

Microsoft, which has been working on machine learning since 1991, has several hundred scientists and engineers in dozens of research areas related to the field. Google Assistant, the centerpiece of that company's deep learning efforts, is on the way to becoming the front-end brain for most of its apps and services. Chinese search giant Baidu poached the head of Google's deep learning project, Andrew Ng, back in 2014. OpenAI, a nonprofit, has $1 billion in funding from Tesla founder Elon Musk and other tech heavyweights. Amazon CEO Jeff Bezos, speaking at the Code conference, said his company has been working on AI behind the scenes for four years and that it already has a thousand people dedicated to its voice recognition ecosystem. Apple and Uber have also invested heavily in artificial intelligence, and are competing to attract the same pool of talent.

All of this is riding on a wave of striking innovation in the field, some of which came from LeCun himself—widely considered one of the most accomplished scientists in the field—during his pre-Facebook days. And Facebook has rapidly gone from not having a formal research lab of any kind to housing two of them. Facebook's Artificial Intelligence Research program (FAIR), headed by LeCun, focuses on fundamental science and long-term research. Then there's the Applied Machine Learning (AML) division, led by Spanish-born Joaquin Candela, a longtime machine learning expert who, among other things, created a course on the topic at the University of Cambridge. His team finds ways to apply the science to existing Facebook products.

The two divisions are separate, with both LeCun and Candela reporting to Facebook CTO Mike Schroepfer. The challenge is figuring out how to make the two groups work together, with long-range scientific research feeding into near-term business goals. One obvious way to make that happen: Get the two teams sitting next to each other. "They have to have personal relationships," says LeCun. "And they have to collaborate really closely."

At Facebook, they not only sit next to one another but near the very top of the organization—just feet from Zuckerberg's and Schroepfer's offices, in fact—a sign of how valuable AI and machine learning has become to the company.

But just because you sit next to someone doesn't make the task of capitalizing on deep science any easier. To understand how LeCun and Candela plan to make it work, you have to first understand where LeCun and Candela came from.

There's a big blue thumbs-up logo taped to the front door of Yann LeCun's office in the computer science department at New York University. LeCun, one of the world's foremost experts in deep learning, didn't put it there. Wearing a navy blue polo shirt with a small image of Einstein stitched above the word "THINK" on a recent Wednesday, he laughs and says that when it was announced two and a half years ago that he was joining Facebook, someone put it there, and he just never took it down.

Photo: Daniel Terdiman

LeCun, 55, is still a part-time professor of computer science at NYU, which is located just steps from Facebook's posh Big Apple digs. You'd never pick him out of a crowd as the one spearheading the massive AI ambitions of the world's largest social networking company—yet he's also the kind of guy whose first ride in a Tesla sedan was with Elon Musk.

If you've ever deposited a check using an ATM, then you've probably seen LeCun's research at work. As one of the fathers of a branch of deep learning known as convolutional neural nets, LeCun is a celebrity in the world of AI. That's because ConvNets, as they're sometimes called, are today considered the building blocks for developing scalable automated natural language understanding and image recognition tools, and even voice recognition or visual search systems, all of which are immensely valuable to Facebook, Google, Baidu, Microsoft, and others. LeCun's work in the field focused on models that aimed to replicate the way living beings' visual cortexes work.

LeCun was given broad freedom to build FAIR as he saw fit, adding people and bringing structure to a group of about a dozen AI researchers in the U.S. that pre-dated him. There was plenty of rationale for Zuckerberg and Schroepfer to grant LeCun that freedom: He'd spent 14 years at Bell Labs and developed a sense for what worked, and what didn't, and had been thinking all along about how he would set up a new research lab if given the chance.

The key to success, he believes, is a dedication to openness. LeCun's dual lives in industry and academia are grounded in a philosophy dictating that researchers publish their work for all to see, speak at conferences, interact widely with academia, and post code to open-source repositories like GitHub.

"I've seen lots of my friends join [big tech companies] coming from research labs that had a culture of openness and try to change the culture of the company and completely fail," says LeCun. One of the first questions he asked before joining Facebook was about its commitment to the open-source world and a culture of openness.

He also wanted to nail the balance between doing research and translating that work to product. Many tech companies, he felt, had trouble figuring out how to do that without the researchers losing their focus. Perhaps the most notorious example is the work done by Silicon Valley's legendary Xerox PARC on the graphical user interface, which Apple applied to the Lisa, and then the Macintosh, after Steve Jobs's famous visit in 1979.

One model LeCun had seen fail was called "hybrid research," where scientists are embedded in engineering groups. That usually stunted their creativity. Another involved hiding researchers away in an ivory tower with little communication with the rest of the company. That was good for stature, but little else.

LeCun would know. From 2002 to 2003, he worked in NEC's prestige lab at Princeton, an advanced research shop the Japanese company had set up with no real urgency to impact product. "They never asked them to produce anything for the company," he says, "Then all of a sudden they did. They told these people it would be nice if you produced stuff we could use, and basically everybody left. Including me, by the way. And it was impossible to break the barriers that existed between research and development."