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.

A Google artificial intelligence program has beaten the European champion of the board game Go. The Chinese game is viewed as a much tougher challenge than chess for computers because there are many more ways a Go match can play out. The tech company's DeepMind division said its software had beaten its human rival five games to nil. One independent expert called it a breakthrough for AI with potentially far-reaching consequences. The achievement was announced to coincide with the publication of a paper, in the scientific journal Nature, detailing the techniques used.

These days, Google is making headlines as its artificial intelligence (AI) AlphaGo beated top pro Go player Lee Se-dol 2:0 in a highly publicized five-game Go series. The internet search giant is expanding its AI business by taking over four robotics companies including DeepMind which designed AlphaGo. But in Korea, AI is an underdeveloped and poorly invested sector. "Korean companies have not made much progress in AI research. They still have a long way to go in terms of AI commercialization," said Jin Jeong-yeol, director of the Kohyoung Technology.

Just over a week ago, Google Deepmind's AlphaGo machine crushed 18-time World Go Champion Lee Sedol 4-1 in a 5 game series, heralding an achievement many experts predicted to be at least a decade away. And whilst the victory of machine over man was a great result for Google, Machine Learning, and Artificial Intelligence (AI) - it also served as a chilling reminder that the ever-extending arm of AI is showing absolutely no sign of slowing. DeepMind founder Demis Hassabis has stated that Go is "probably the most complex game ever devised by man." For starters, it's played on a 19 by 19 board, which allows for 10171 possible layouts, versus roughly 1050 possible configurations on a standard chessboard, and an estimated 1080 atoms in the universe. Because of this, players are often said to rely heavily on sub-conscious intuition or'gut feeling'.

DeepMind's stunning victories over Go legend Lee Se-dol have stoked excitement over artificial intelligence's potential more than any event in recent memory. But the Google subsidiary's AlphaGo program is far from its only project -- it's not even the main one. As co-founder Demis Hassabis said earlier in the week, DeepMind wants to "solve intelligence," and he has more than a few ideas about how to get there. Hassabis himself has had an unusual path to this point, but one that makes perfect sense in retrospect. A child chess prodigy who won the Pentamind championship at the Mind Sports Olympiad five times, he rose to fame at a young age with UK computer games developers Bullfrog and Lionhead, working on AI-heavy games like Theme Park and Black & White, and later forming his own studio, Elixir. Hassabis then left the games industry in the mid-2000s to complete a PhD in neuroscience before co-founding DeepMind in 2010.

This helps to validate DeepMind's machine learning techniques and the neural network construction behind it. Having proven their mettle in Go, the DeepMind team could now have the confidence (and funding) to tackle more complex AI challenges. ARTIFICIAL INTELLIGENCE (AI) just overcame a new hurdle: learning to play Go, a game considered thousands of times more complex than chess--well enough to beat the greatest human player at his own game. South Korean national Lee Se-dol, one of the world's top Go players, won only one of the five matches against Google's AlphaGo, missing out on the 1-million prize up for grabs in a recent'challenge' held in Seoul. AlphaGo, an AI system developed by Google DeepMind, just bested the best Go-playing human currently alive. This was not supposed to happen.