It's been an emotional week in the realm of game AI as the world watched the historic five-game showdown between legendary Go world champion Lee Sedol and Google DeepMind's famed… read more With Google beating a human player in GO, AI has leaped ahead of predictions. This article looks at the implications of that acceleration.

Google's move to give away its latest machine-learning software, key to its speech- and photo-recognition programs, isn't as crazy as it may appear. The unit of Alphabet said Monday it is releasing its TensorFlow system for free under an open-source license. That's one of the company's crown jewels, a machine-learning program that teaches computers to be smarter. But Google retains much of what makes its machine-learning effort special: massive piles of data, a powerful network of computers to run the software and a big team of artificial-intelligence experts to tweak the algorithms. "It's not a suicidal idea to release this," said Nello Cristianini, a professor of artificial intelligence at the U.K.'s University of Bristol.

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."

Over the last several years, artificial intelligence (AI) has shifted from being an esoteric branch of computer science to an everyday technology that most of us carry in a pocket or purse--AI is what drives Apple's Siri, Facebook's photo-tagging, Spotify playlists and Google's auto-complete, just for starters. But can we also expect that someday soon AI will report and write the important news of the day--and technology stories like this one? Well, guess what: It already has. First, a bit of background: Many of the most exciting AI advances are driven by research in cognitive computing and natural language generation (NLG) processing, which allow computers to analyze massive quantities of data and generate a plain English document that highlights the most important insights. Those advances are made stronger through deep learning, a field of AI that uses neural networks to teach computers to sift through massive amounts of data to find their own patterns.

Deep Learning is a new area of Machine Learning research that has been gaining significant media interest owing to the role it is playing in artificial intelligence applications like image recognition, self-driving cars and most recently the AlphaGo vs. Lee Sedol matches. Recently, Deep Learning techniques have become popular in solving traditional Natural Language Processing problems like Sentiment Analysis. For those of you that are new to the topic of Deep Learning, we have put together a list of ten common terms and concepts explained in simple English, which will hopefully make them a bit easier to understand. We've done the same in the past for Machine Learning and NLP terms, which you might also find interesting. In the human brain, a neuron is a cell that processes and transmits information.

Deep Learning is, as with all technology, neither inherently good nor bad. However, it is not just lunatics in foil hats who are worried that self aware computers could spell danger. The CEO and co-founder of DeepMind himself, Demis Hassabis, has acknowledged that the advanced techniques his own group is pioneering may cause AI to spiral out of human control, and could need to be constrained, while his co-founder, Shane Legg, considers a human extinction due to artificial intelligence the top threat in this century. As a result, contingencies have been put in place. DeepMind investor Elon Musk has just spent 10 million on a study of AI dangers, and Hassabis and his co-founders put in the conditions of Google's takeover that there be an outside board of advisors to monitor the progress of the company's AI efforts.

You--ve likely heard the news that the Google DeepMind --AlphaGo-- computer not only beat a human expert at the game of Go, defeating the European Go champion, Fan Hui in five straight games, but also beat the reigning world champion grandmaster, South Korea--s Lee Sedol, 4 games to 1. Go is considered to be a significantly more difficult game for a computer to tackle than chess, if only because of the vastly greater number of possible moves over a much larger playing field. Chess has on the order of 1040 possible legal and realistic positions in a 40-move game; Go can have up to 10360, give or take a few tens of orders of magnitude. When Deep Blue beat world chess champion Gary Kasparov back in 1997, it did it with a brute force approach -- a massively parallel computer that would typically search to a depth of between six and eight moves, and up to a maximum of about 20 moves in some situations. It was an expert system (not AI), with separate programing modules/libraries for openings, end games, and middle game strategy and tactic evaluation. All the legal moves and rules had to be programmed into it, and it could not learn as it went (although its programmers made adjustments after each game).

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.