Recent News Puts AI In The Spotlight

While the collective tech world was agog at the recent Go championship match between a world master and Google DeepMind’s AlphaGo software, other AI innovations have quietly been making waves of their own. It might not be as flashy as a computer beating a human champion in four of the five games–or the human turning around and beating the computer, for that matter–but there are no doubt serious implications involved.

Microsoft, for example, announced the news that it was teaming up with Minecraft to create an AI interface. Long perceived as the go-to world building game for eight-year-olds everywhere, Minecraft is actually an ageless and highly-sophisticated gaming world of its own. With the advent of the open-source AIX interface, Microsoft is inviting AI developers to use Minecraft as their own personal proving grounds, largely due to the high-volume of unforeseeable game play ramifications.

Another software research lab has been working on developing an AI that can win at poker, a seemingly basic card game on the surface but one that is actually filled with nuance and pitfalls. As Thor Oluvsrud points out for CIO.com, poker–specifically Texas Hold ‘Em–is even more difficult for a computer to play than loftier games of strategy like Go or chess. In those games, the players (and therefore, the computer) can see the board at all times. They can develop predictive strategies in order to prepare their moves. But in card games, the players don’t know what all of the other players are holding, and therefore have a much harder time predicting what moves they’ll make based on the moves of other players.

But what is all of this effort for? Surely there’s more to AI development than being able to play against the computer on your tablet. As always, the goal for AI technology is to develop robots that don’t have to be told every single “if/then” scenario in order to make a sound decision. Coding for every possible outcome is hard enough when it’s chess, but it’s nearly impossible when it’s life-altering and emergency situations. For now, though, the tools to develop this type of understanding of outcome-based predictive decision making lie in games.

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AI experimentation heads to Minecraft.

Microsoft, owner of the evergreen popular video game, Minecraft, have announced the news that pro computer scientists and casual amateurs will now be able to help evaluate and develop Artificial Intelligence software by simply playing the game.

The aim of the AI project, known as AIX, will be to see whether machines can learn to play Minecraft freely, as opposed to being stuck to a list of event led specific tasks and choosing an appropriate response.

The AIX AI program, will not require any extra hardware from users such as the codes for Nuclear warheads, but instead. will be taught to play Minecraft as an add-on for the game, allowing interested home users and academics to evaluate the AI engine it is working on.

AIX won’t cost players any extra either and will be released as an open source modification but users will be cordoned off and sandboxed from other Minecraft players.

This is, no doubt, to ensure that should the AIX software one day become self-aware, it won’t be able to gain access to the nuclear launch codes, rename itself Skynet, and launch a sequence of increasingly poor sequels that suck the soul out of what was once a really good film.

In order to take part in the AIX AI experiment, players will have to install AIX on top of their preexisting game installation. This will then allow the AIX artificial intelligence code to control a character that will consequently ‘learn’ through in game feedback and game world consequences.

A senior researcher, Fernando Diaz, said:

“We’re trying to program it to learn, as opposed to accomplishing specific tasks.”

Rather than writing the code that gives the AI character the knowledge of how to climb a mountain, researchers are instead more interested in seeing whether the AIX AI can learn how to do it for themselves, without pre-written instructions.

Katja Hofmann, the AIX project leader at the Microsoft’s lab in the UK explained that:

“People build amazing structures that do amazing things in Minecraft, and this allows experimenters to put in tasks that will stretch AI technology beyond its current capacity.”

By utilizing this ‘group’ think’ rollout of AIX to the masses, Hofmann hopes that it will speed up the research of computers gaining human-like intelligence. AIX is already up and running in a private beta test, but is expected to be available to anyone who wants it by the summer.

The news comes at what many commentators see as a crucial moment in time for AI.  AlphaGo, Google’s AI machine managed to claim victory in a series of high profile games against the reigning GO world champion.

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What we learned in Seoul with AlphaGo

Go isn’t just a game—it’s a living, breathing culture of players, analysts, fans, and legends. Over the last 10 days in Seoul, South Korea, we’ve been lucky enough to witness some of that incredible excitement firsthand. We've also had the chance to see something that's never happened before: DeepMind's AlphaGo took on and defeated legendary Go player, Lee Sedol (9-dan professional with 18 world titles), marking a major milestone for artificial intelligence.

Pedestrians checking in on the AlphaGo vs. Lee Sedol Go match on the streets of Seoul (March 13)

Go may be one of the oldest games in existence, but the attention to our five-game tournament exceeded even our wildest imaginations. Searches for Go rules and Go boards spiked in the U.S. In China, tens of millions watched live streams of the matches, and the “Man vs. Machine Go Showdown” hashtag saw 200 million pageviews on Sina Weibo. Sales of Go boards even surged in Korea.

Our public test of AlphaGo, however, was about more than winning at Go. We founded DeepMind in 2010 to create general-purpose artificial intelligence (AI) that can learn on its own—and, eventually, be used as a tool to help society solve some of its biggest and most pressing problems, from climate change to disease diagnosis.

Like many researchers before us, we've been developing and testing our algorithms through games. We first revealed AlphaGo in January—the first AI program that could beat a professional player at the most complex board game mankind has devised, using deep learning and reinforcement learning. The ultimate challenge was for AlphaGo to take on the best Go player of the past decade—Lee Sedol.

To everyone's surprise, including ours, AlphaGo won four of the five games. Commentators noted that AlphaGo played many unprecedented, creative, and even “beautiful” moves. Based on our data, AlphaGo’s bold move 37 in Game 2 had a 1 in 10,000 chance of being played by a human. Lee countered with innovative moves of his own, such as his move 78 against AlphaGo in Game 4—again, a 1 in 10,000 chance of being played—which ultimately resulted in a win.

The final score was 4-1. We're contributing the $1 million in prize money to organizations that support science, technology, engineering and math (STEM) education and Go, as well as UNICEF.

We’ve learned two important things from this experience. First, this test bodes well for AI’s potential in solving other problems. AlphaGo has the ability to look “globally” across a board—and find solutions that humans either have been trained not to play or would not consider. This has huge potential for using AlphaGo-like technology to find solutions that humans don’t necessarily see in other areas. Second, while the match has been widely billed as "man vs. machine," AlphaGo is really a human achievement. Lee Sedol and the AlphaGo team both pushed each other toward new ideas, opportunities and solutions—and in the long run that's something we all stand to benefit from.

But as they say about Go in Korean: “Don’t be arrogant when you win or you’ll lose your luck.” This is just one small, albeit significant, step along the way to making machines smart. We’ve demonstrated that our cutting edge deep reinforcement learning techniques can be used to make strong Go and Atari players. Deep neural networks are already used at Google for specific tasks—like image recognition, speech recognition, and Search ranking. However, we’re still a long way from a machine that can learn to flexibly perform the full range of intellectual tasks a human can—the hallmark of true artificial general intelligence.

Demis and Lee Sedol hold up the signed Go board from the Google DeepMind Challenge Match

With this tournament, we wanted to test the limits of AlphaGo. The genius of Lee Sedol did that brilliantly—and we’ll spend the next few weeks studying the games he and AlphaGo played in detail. And because the machine learning methods we’ve used in AlphaGo are general purpose, we hope to apply some of these techniques to other challenges in the future. Game on!

Posted by Demis Hassabis, CEO and Co-Founder of DeepMind http://ift.tt/1Ugfyxa Demis Hassabis CEO and Co-Founder DeepMind

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