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.

Standard Chartered is moving heavily into radical new technologies that could one day see robots providing bespoke wealth advice and artificial intelligence answering customer questions. The emerging markets-focused British bank has set up a new lab called the eXellerator in Singapore in an attempt to bring theoretical ideas from Silicon Valley to life. Chief executive Bill Winters has put the centre at the heart of a 1.5bn commitment to improving computing and IT systems. Some of the ideas are also urgently needed cost-saving initiatives. The bank has hired thousands of additional compliance officers in the past three years and last year hiked annual compliance spending by an extra 1bn in an effort to stop workers breaking laws and regulations, in the wake of expensive scandals including the breaking of US sanctions against Iran.

We propose here a simple, robust and scalable technique to perform supervised clustering on numerical data. It can also be used for density estimation, and even to define a concept of variance that is scale-invariant. This is part of our general statistical framework for data science. Here we discuss clustering and density estimation on the grid. The grid can be seen as an 2-dimensional or 3-dimensional array.

Students at Lebanese American University (LAU) participate in a hardware design workshop that leverages the tools of the fourth Industrial Revolution. We are in the midst of the greatest industrial revolution in human history. The Fourth Industrial Revolution (4ID) is an economic transformation a thousand-times wider and deeper than anything that has come before it. "The changes are so profound that, from the perspective of human history, there has never been a time of greater promise or potential peril," according to Professor Klaus Schwab, founder and executive chairman of the World Economic Forum (WEF). The 4ID is characterized by the confluence of next generation technologies like: quantum computing, artificial intelligence and machine learning, autonomous transportation and robotics, the Internet of Things, additive manufacturing including 3D printing, biotechnology, and more generally; the merging of the digital and physical worlds.

Technological history saw a new advancements in a match of Go between an artificial intelligence developed by Google and 18-time world champion, Lee Sedol in Seoul, South Korea. Five matches were held over a span of a week, starting on March 9 and ending March 15. "Yesterday, I was surprised," said Sedol after his defeat in game two, "but today, more than that, I'm speechless…there was not a moment in time when I felt that I was leading the game." AlphaGo, the AI machine, claimed victory 4–1 against Sedol. The win was a shock as experts had predicted that Go would not be conquered by a machine for another decade.

AlphaGo, the computer system Google engineers trained to master the ancient game of Go, needed only one move to make it abundantly clear it has left humans in its dust. The move came Thursday, in the second game of AlphaGo's 4-1 landmark victory over South Korean Lee Sedol, one of the world's best Go players. About an hour into Thursday's match, AlphaGo placed one of its stones in a nontraditional spot on the board that surprised those watching. "I don't really know if it's a good or bad move," said Michael Redmond, a commentator on a live English broadcast. Redmond, one of the Western world's best Go players, could only crack a bemused smile.

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).

It was oversold in the past. Easy to see why – the potential gains were (and still are) enormous and any indication you were on the right track meant people would throw money at you. And if you then couldn't deliver anything monetizable, the money people would shred you and your reputation. DL systems have achieved near-human performance in at least 6 problem domains (signal processing, low-level speech understanding, image understanding, text understanding, Atari games, and Go). From now on, we can use incremental improvements and we can reliably measure our progress.

It has been a nightmare of a PR week for Microsoft. It started with the head of Microsoft's Xbox division, Phil Spencer, having to apologise for having scantily clad female dancers dressed as school girls at a party thrown by Microsoft at the Game Developers Conference (GDC). He said that having the dancers at this event "was absolutely not consistent or aligned to our values. That was unequivocally wrong and will not be tolerated". The matter was being dealt with internally and so we don't know who would have been responsible and why they might have thought this was going to be a good idea.