Facebook Builds The ‘World’s Best’ Artificial Intelligence Lab Content User Generated

It’s time to stop thinking about Facebook as just a social media company. Between its efforts to deliver internet service with drones, buying Oculus for virtual reality, and its continued pursuit of artificial intelligence, Facebook has quickly become one of the most advanced technology research centers in the world.
It’s not alone: companies like Google and even IBM have similar schemes, and collectively, the developments across the field have accelerated to the point that artificial intelligences will surely shape the way humans interact with computers. In fact, they already do — but quietly, behind the curtains. Facebook has great interest in this technology, servicing 1.5 billion users monthly. The company tackles the problem of emulating general intelligence — that is, getting computers to think less like linear, logical machines, and like us free-form humans — with a multi-prong approach. While the Facebook Artificial Intelligence Research (FAIR) team works on solving generalized AI problems, smaller groups like Language Technology and Facebook M deploy practical features to users.


It all started in 2013. Facebook founder and CEO Mark Zuckerberg, chief technology officer Mike Schroepfer, and other company leadership were taking stock in the company’s accomplishments since launching almost a decade before, and looking to see what would allow them to thrive throughout the next 10 or 20 years.

Facebook had already been using machine learning on its hugely popular social network to decide what users would see on their News Feeds, but it was simple compared to the cutting-edge neural networks of the time.

Some Facebook engineers had also been experimenting with convolutional neural networks (CNNs), a powerful flavor of machine learning that is now popularly used for identifying images. Zuckerberg was impressed by the potential of artificial intelligence, even in its early stages, so he hired an engineer out of Google Brain, Marc’Aurelio Ranzato. Then, he went to the source: the inventor of CNNs, Yann LeCun.

Yann LeCun, who now serves as the director of FAIR, comes from a storied tenure of artificial intelligence research. He began his work in Bell Labs (founded by telephone father Alexander Graham Bell, and known for its experiments across myriad fields in telecommunications and technology) as a researcher starting in 1988, then moving to become a department head at AT&T Labs until developing 2003, when he began to teach at New York University. The modern convolutional neural network is a culmination of work throughout LeCun’s career. Ever wonder how an ATM can read your check? That was LeCun, whose early work included a neural network simulator called “SN” and deployed in 1996.

Dave Gershgorn/ Popular Science Yann LeCun, director of FAIR at Facebook, mainly works out of the company’s New York City offices, so he can also continue to teach at NYU.

“I expect you to build the best research lab in AI in the world.”
“I started talking with Schroepfer and Mark, and I guess they liked what I told them,” LeCun said in an interview with Popular Science. “And then they tried to convince me to run it…When someone like Mark comes to you and says ‘Oh, okay, you pretty much have carte blanche. You can put together a world-class research lab and I expect you to build the best research lab in AI in the world.’ I’ll say,’Hmm, interesting challenge.’”


The team subsequently tasked with creating the future of Facebook is a small, only about 30 research scientists and 15 engineers in total. Labor is divided over three branches: Facebook AI Research’s main office is in New York City’s Astor Place, where LeCun operates with a team of about 20 engineers and researchers. A similar number staffs the Menlo Park branch, and as of June, FAIR has opened a smaller Paris office of about 5 to collaborate with INRIA, the French Institute for Research in Computer Science and Automation. There are others that work within Facebook on AI deployment, like the Language Technology team; FAIR is the research arm.

These researchers and engineers come from all over the tech industry, and many have previously collaborated with LeCun. High-level artificial intelligence research isn’t an enormous field, and many of LeCun’s pupils have gone on to seed AI startups, which would be absorbed into larger companies like Twitter.


Dave Gershgorn/ Popular Science From left, Leon Bottou, Yann LeCun, and Rob Fergus work in their corner Facebook’s New York City office.


The size and academic weight of the team allows Facebook to be ambitious with their long-term goal, which doesn’t fall short of a system that LeCun would call “unambiguously intelligent.”

“Right now, even the best AI systems are dumb, in the way that they don’t have common sense,” LeCun said. He talks about a situation where I pick up a bottle, and leave the room. (We’re in a FB NYC conference room called Gozer the Gozerian — sharing the name of the Ghostbusters villain — an ominous name for a room to discuss the birth of true machine intelligence.) The human brain has no trouble imagining the entire simple scenario of someone picking up a bottle and leaving a room, but to a machine, huge swaths of information are missing based on that premise alone.

The artificial intelligence community doesn’t know enough right now about the how machines learn to bring this level of inference. Stepping to achieve that goal, Facebook is focusing on building machines that can learn well enough to understand the world around them.

“Right now, even the best AI systems are dumb.”

The biggest barrier, says LeCun, is what’s called “unsupervised learning.” Right now machines mainly learn in one or two ways: supervised learning, where the system is shown thousands of pictures of dogs, until it understands the attributes of a dog. This method is explained in Google’s DeepDream, where researchers reversed the process to reveal its efficacy.

“We don’t even have a basic principle on which to build this. We’re working on it, obviously,” LeCun says, and laughs. “We have lots of ideas, they just don’t work that well.”


But that’s not to say that there hasn’t been progress made. Right now, LeCun is excited about work on a “memory” network that can be integrated into present convolutional neural networks, giving them the ability to retain information. He likens the new mode of memory retention to short term and long term memory in the brain, governed by the hippocampus and cerebral cortex respectively. (LeCun actually detests CNNs being compared to brains, instead preferring a model of a black box with 500 million knobs.)

The memory module allows researchers to tell the network a story, and then have it answer questions about the story later.

For the story, they used J.R.R. Tolkein’s Lord of the Rings Well, not the entire book, but short summaries of major plot points. (“Bilbo took the ring.”) When asked questions about where the ring was at certain points in the story, the AI would be able to answer in short, correct answers. This means it “understands” relationships between objects and time, according to CTO Mike Schroepfer, who stressed this technology’s ability to help Facebook show you what you want to see with higher accuracy.

“By building systems that understand the context of the world, understand what it is you want, we can help you there,” Schroepfer said at a developer presentation in March. ”We can build systems that make sure all of us spend time on the things we care about.”

The FAIR team is developing this context around a project called “Embed the World.” To help machines better understand reality, the FAIR team is teaching them to represent the relationships between everything in vectors: images, posts, comments, photos, and video. The neural network is creating an intricate web of content that groups like pieces of media, and distances different ones. There’s a helpful video to visualize this:

With this system, LeCun says that we can start to “replace reasoning with algebra.” And it’s incredibly powerful. The artificial neural networks developed in the Embed the World project can link two photos that were taken in the same location based on visual similarities in the photos, but also figure out if text describes the scene. It’s recreating a virtual memory of reality, and clustering it in the context of other places and events. It can even “virtually represent a person,” based on their previous likes, interests, and digital experiences. This is somewhat experimental, but has great implications for Facebook’s News Feed and is used in a limited way to track hashtags.

“If we have an idea that actually works, within a month it can be in front of 1.5 billion people,” LeCun said, “Lets keep our eyes focused on the horizon, where our long-term goal is, but on the way there are a lot of things that we’re going to build that are going to have applications in the short term.”


Dave Gershgorn/ Popular Science Rob Fergus, right, stands among FAIR researchers at Facebook’s New York City office. Fergus’ work is concerned with the visual element of artificial intelligence.

Rob Fergus, a veteran of NYU and MIT’s Computer Science and Artificial Intelligence Lab, leads the AI research team concerned with vision. His team’s work that can already been seen in the automatic tagging of photos, but Fergus says the next step is video. Lots of video is “lost” in the noise because of a lack of metadata, or it’s not accompanied by any descriptive text. AI would “watch” the video, and be able to classify video arbitrarily.

This has major implications for stopping content Facebook doesn’t want from getting onto their servers—like pornography, copyrighted content, or anything else that violates their terms of service. It also could identify news events, and curate different types of video category. Facebook has traditionally farmed these tasks out to contracted companies, so this could potentially play a role in mitigating costs.


A separate group within Facebook, called Language Technology, focuses on developing translation, speech recognition, and natural language understanding. FAIR, LeCun’s realm, is the research arm of Facebook’s AI push, and Language Technology (under the umbrella of Applied Machine Learning) is one of the places that actually deploys the software.

There are 330 million people using these translation services, which are most often accessed by clicking the “See Translation” button. If you’ve been the first person to click the translation button, congratulations, you’ve operated artificial intelligence. The first click initiates the translation request to the server, which is then cached for other users. Packer says that Shakira’s posts are translated almost instantly. The team is also rolling out native translation of content, which will display a “See the original” button.

If you’ve been the first person to click the translation button, congratulations, you’ve operated artificial intelligence.

Copyright © 2016 Popular Science

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Source: Popular Science


Images credits: Ryan Snook

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