Mastering arcade games seems cute by comparison. Researchers at DeepMind, the Google-owned artificial intelligence lab, announced Wednesday they had achieved a breakthrough not thought possible for at least another decade: a computer program that defeats humans at Go, an enormously complicated strategy game. See Also: This robot can solve Rubik's Cube in one second This network was named by EContent Magazine to its "Trendsetting Products of 2014" list.

There's a wide class of generative models for which variational methods are the only known practical way to do inference. This includes basically any model with black-box ("neural") dependence relations, and many others as well, e.g., Bayesian nonparametrics for any significant dataset size. The point of variational methods is not to calculate partition functions (although you do get that as a side effect); the point is to fit sophisticated models that have complex latent structure. Which does yield improvements across pretty much any metric you'd care about.

DeepMind was a relatively unknown artificial intelligence (AI) startup in London up until 2014, when it was bought by Google for around 400 million. Today some of the smartest people in the world are queuing up to work at DeepMind, according to an article by Celemency Burton-Hill in The Guardian in February. Interestingly, the same article states that no one has ever left DeepMind, which has created a series of algorithms that can learn for themselves and beat the best humans at games like Go and "Space Invaders." Based in up-and-coming King's Cross, DeepMind now employs around 250 people. However, as Burton-Hill points out, getting a job there is far from easy.

The importance of Google owned DeepMind's AI victory over the world's best Go player is difficult to fathom. People expect computers to be smarter than human beings, however, Go is one game that was expected to be beyond what AI is capable of right now. The reason for this is that GO is a deceptively simple game with very few rules. All the pieces on the board have the same value, unlike chess where having more'valuable' pieces means that you will win more often than not. Players themselves describe the game as being based on intuition and'feel' rather than any set rules.

What next for Google's DeepMind, now that the company has mastered the ancient board game of Go, beating the Korean champion Lee Se-Dol 4–1 this month? A paper from two UCL researchers suggests one future project: playing poker. And unlike Go, victory in that field could probably fund itself – at least until humans stopped playing against the robot. The paper's authors are Johannes Heinrich, a research student at UCL, and David Silver, a UCL lecturer who is working at DeepMind. Silver, who was AlphaGo's main programmer, has been called the "unsung hero at Google DeepMind", although this paper relates to his work at UCL.

This article was first published in the July 2015 issue of WIRED magazine. Be the first to read WIRED's articles in print before they're posted online, and get your hands on loads of additional content by subscribing online The future of artificial intelligence begins with a game of Space Invaders. From the start, the enemy aliens are making kills -- three times they destroy the defending laser cannon within seconds. Half an hour in, and the hesitant player starts to feel the game's rhythm, learning when to fire back or hide. Finally, after playing ceaselessly for an entire night, the player is not wasting a single bullet, casually shooting the high-score floating mothership in between demolishing each alien. No one in the world can play a better game at this moment. This player, it should be mentioned, is not human, but an algorithm on a graphics processing unit programmed by a company called DeepMind. Instructed simply to maximise the score and fed only the data stream of 30,000 pixels per frame, the algorithm -- known as a deep Q-network – is then given a new challenge: an unfamiliar Pong-like game called Breakout, in which it needs to hit a ball through a rainbow-coloured brick wall. "After 30 minutes and 100 games, it's pretty terrible, but it's learning that it should move the bat towards the ball," explains DeepMind's cofounder and chief executive, a 38-year-old artificial-intelligence researcher named Demis Hassabis. "Here it is after an hour, quantitatively better but still not brilliant. But two hours in, it's more or less mastered the game, even when the ball's very fast. After four hours, it came up with an optimal strategy -- to dig a tunnel round the side of the wall, and send the ball round the back in a superhuman accurate way. The designers of the system didn't know that strategy."

This past week, the Go-playing world was rocked by DeepMind AlphaGo's unexpected victory over legendary champion Lee Se-dol. Sure, supercomputers have beaten chessmasters at their own game before, but due to the extremely complex nature of the 5000-year old game of Go, this was an unprecedented upset that experts had predicted wouldn't happen for another 10 years. So what does this mean for us, and more dramatically, the rest of humanity? Is it time to welcome our new robot overlords? Here's a handy timeline of AI victories to help you make sense of it all.

In principle, zero-shot learning makes it possible to train a recognition model simply by specifying the category's attributes. For example, with classifiers for generic attributes like \emph{striped} and \emph{four-legged}, one can construct a classifier for the zebra category by enumerating which properties it possesses---even without providing zebra training images. In practice, however, the standard zero-shot paradigm suffers because attribute predictions in novel images are hard to get right. We propose a novel random forest approach to train zero-shot models that explicitly accounts for the unreliability of attribute predictions. By leveraging statistics about each attribute's error tendencies, our method obtains more robust discriminative models for the unseen classes. We further devise extensions to handle the few-shot scenario and unreliable attribute descriptions. On three datasets, we demonstrate the benefit for visual category learning with zero or few training examples, a critical domain for rare categories or categories defined on the fly.

The ancient strategy game of Go may have met its ultimate match. The brain-taxing board game is a little like an Eastern version of chess, except many times more complex. It has millions of devotees in China, Korea and Japan. Many of them tuned in today to watch an artificial intelligence computer built by Google's DeepMind beat the world champion, Lee Sedol, in the first of a five-game contest. Duels like these don't come often.

World Go champion Lee Se-dol has beaten AlphaGo, an AI program developed by Google's DeepMind unit this weekend, though he still trails the program 3-1 in the series. Google's publicity stunt highlights the progress which has been made in the world of artificial intelligence and machine learning, as commentators predicted a run-away victory for Se-dol. DeepMind founder Demis Hassabis commented on Twitter "Lee Sedol is playing brilliantly! We are in trouble now…" allowing Se-dol to win the fourth game in the five game series. While the stunt demonstrates the potential of machine learning, Se-dol's consolation victory proves that the technology is still capable of making mistakes.