Facebook boosts AI system with visual recognition to win online games
According to Mike Schroepfer, Facebook's CTO, the company is taking some of the basics of game-playing AI and improving them by adding a visual system to the machine in order to enhance its learning ability. The visual recognition system can watch and learn an opposing machine's moves on a games board to help it understand what is a good and bad move. Schroepfer explained that the system, which is only three months old, has beaten other gaming systems that are built using more traditional AI techniques.  

In the mid-'90s, a computer program called Chinook beat the world's top player at the game of checkers. Three years later, to much fanfare, IBM's Deep Blue supercomputer won its chess match against reigning world champion Gary Kasparov. And in 2011, another IBM machine, Watson, topped the best humans at Jeopardy!,the venerable TV trivia game show. Machines can now beat the best humans at a wide range of games traditionally held up as tests of intellect, from Scrabble to Othello. But there's one notable pastime where we humans still come out on top: the game of Go.

With all those other games, computers can win by, in essence, analyzing the many possible outcomes of every possible move. Yes, a chess grandmaster like Kasparov can look ahead in a similar way. But a machine can examine far more future moves than Kasparov ever could. Likewise, a machine can look ahead in a game of Go—the Eastern version of chess—but in this case, looking ahead is far more difficult. On a Go board—a 19-by-19 grid where players place pieces at the intersection of two lines—the number of possible moves is far greater, and identifying the benefits of a particular move is far more complicated, even mysterious. The top players will tell you they play in a way that's, on some level, subconscious. Getting a computer to play this way is another task entirely. You can't use the same approach as a Deep Blue or a Watson.

With this in mind, researchers at Facebook are now tackling Go with an increasingly important form of artificial intelligence known as deep learning.

In recent years, companies like Facebook, Google, and Microsoft have shown that deep learning is remarkably adept at recognizing photos, identifying spoken words, and translating from one language to another. To recognize a cat, for instance, a deep learning system analyzes thousands of known cat photos, feeding each into a network of machines that approximate the neural networks of the human brain. Thanks to these neural networks, your Facebook app can recognize photos of you and your friends. Google's smartphone digital assistant can recognize the commands you bark into your Android phone. And Microsoft can instantly translate your Skype calls. Now, Facebook is using similar technology to recognize a promising Go move—to visually understand whether it will be successful, kind of like a human would. Researchers are feeding images of Go moves into a deep learning neural network so that it can learn what a successful move looks like.

"We're pretty sure the best [human] players end up looking at visual patterns, looking at the visuals of the board to help them understand what are good and bad configurations in an intuitive way," Facebook CTO Mike "Schrep" Schroepfer told reporters at Facebook's California headquarters last week, before delivering a speech along similar lines this morning at the Web Summit in Dublin. "So, we've taken some of the basics of game-playing AI and attached a visual system to it, so that we're using the patterns on the board—a visual rec[ognition] system—to tune the possible moves the system can make." Though this system is only about two or three months old, he says, it can already beat systems built solely with more traditional AI techniques.

Learning Deeply

On one level, this work is a curiosity, a sideshow to the deep learning systems the company is building to tackle specific tasks across the world's most popular social network. Facebook is using neural nets to better determine what you want to see in your Facebook News Feed. It's building another system for blind Facebook users that can automatically describe photos via a text-to-speech engine. But the company's Go work—which Schrep describes as "super early"—demonstrates why deep learning is so powerful and how it can continue to push the boundaries of what machines can do.

Solving big AI problems requires a wide range of technologies, and deep learning can provide something akin to human intuition—or at least approximate the kind of intuitive tasks that we humans find difficult to explain. This includes playing Go, but such game playing is merely a small step towards something larger. After achieving so much success with image and speech recognition, many researchers believe, deep learning can now help computers understand "natural language"—the way that humans naturally speak.

During his briefing with reporters, Schrep also nodded to a system he has demonstrated in the past, where a neural net analyzes a synopsis of The Lord of the Rings and then answers questions about the plot of the J.R.R. Tolkien trilogy. "We took a very, very, very short version of The Lord of the Rings and fed it into the system," Schrep said. "And then immediately, right after you feed it this, you can start asking it questions, about the data it has just seen, that are relatively complex—that involve spatial relationships." This is indicative of a wider trend. Google recently published a research paper describing a computer bot that could—on some level—debate the meaning of life. A startup called MetaMind is exploring similar avenues.