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Site: http://phys.org/technology-news/

After shocking the world by defeating Lee Se-Dol—one of the greatest modern players of the ancient board game—in their opening match on Wednesday, the AlphaGo computer proved it was no fluke with another victory after a gruelling four-and-a-half-hour encounter. "I am quite speechless. I admit it was a very clear loss on my part," Lee told reporters after the match, adding he had found "no weakness" in AlphaGo's performance during Thursday's match. "AlphaGo played a near perfect game today... I will try my best so that I will win at least one game," said an ashen-faced Lee, who had earlier predicted that he would beat the supercomputer by a "landslide". The 33-year-old must prevail in all three remaining matches—held on Saturday, Sunday and Tuesday—to win the series that has a cash prize of $1 million. AlphaGo's creators have described Go as the "Mt Everest" of AI, citing the complexity of the game, which requires a degree of creativity and intuition to prevail over an opponent. The most famous AI victory to date came in 1997 when the IBM-developed supercomputer Deep Blue beat Garry Kasparov, the then-world class chess champion, in its second attempt. But a true mastery of Go, which has more possible move configurations than there are atoms in the universe, had long been considered the exclusive province of humans—until now. AlphaGo first came to prominence with a 5-0 drubbing of European champion Fan Hui last October, but it had been expected to struggle against 33-year-old Lee who has topped the world rankings for most of the past decade. The computer uses two sets of "deep neural networks" that allow it to crunch data in a more human-like fashion—dumping millions of potential moves that human players would instinctively know were pointless. It also employs algorithms that allow it to learn and improve from matchplay experience. Hailed as the "match of the century" by local media, the showdown at the Four Seasons Hotel in Seoul is being closely watched by AI experts as well as tens of millions of Go fans mostly in East Asia. The matches are being as broadcast live on major TV and cable channels in South Korea, Japan and China, with many Go fans rooting for Lee. "He is fighting alone against dozens of the world's top scientists and computers with massive processing power... I can't imagine how much pressure Lee is under," one online commentator wrote during Thursday's game. Lee appeared to struggle early on after AlphaGo made several moves that were "shockingly unconventional", said Kim Seong-Ryong, a Go commentator and professional player. "If you conducted a survey of all the 1,300 professional Go players in the South, Japan and China, not a single person would have chosen that move," Kim said after one of the computer's unexpected plays. Explore further: Human champion certain he'll beat AI at ancient Chinese game


News Article | August 18, 2016
Site: http://www.techtimes.com/rss/sections/smartphone.xml

A new iPhone 7 leak is making rounds on the internet, and if it's anything to go by, the anticipated handset won't be hitting the shelves as early as previously expected. According to 9to5Mac, the Apple smartphone will go official on Sept. 23, one week later than the first speculated release date. That detail comes from none other than AT&T, where the major U.S. carrier's retail schedule just got out to the public. Before this piece of news made the headlines, the iPhone 7 launch was originally believed to kick off on Sept. 16, with preorders starting on Sept. 9. At the time, many already assumed that was the real deal, as the ol' reliable tipster Evan Blass or @evleaks was the one who spread the word. To clear things up, these release dates are likely based on the past schedules of earlier iPhone models. In the case of the iPhone 6 and iPhone 6 Plus, they went up for preorder on Sept. 12 and rolled out on Sept. 19, Friday, back in 2014. That's a seven-day gap. Meanwhile, the iPhone 6s and iPhone 6s Plus became available for preorder on Sept. 12 and landed on storefronts on Sept. 25, Friday, back in 2015. That there is a 13-day gap. Assuming that preorders for the iPhone 7 and iPhone 7 Plus indeed get off the ground on Sept. 9, the history-backed release dates are naturally on a Friday either Sept. 16 or Sept. 23. To boil things down, because of the recent leak, there's a strong possibility that the next Apple flagship is going to launch on Sept. 23 instead of Sept. 16. However, nothing's set in stone just yet until the Cupertino brand itself announces anything, not to mention that the company hasn't said that the Sept. 7 event is official just yet. In other words, it's recommended to take this news with the proverbial grain of salt. It's also worth pointing out that an iPhone 7 Plus sporting a Deep Blue color has been spotted, providing another option to consider before shipping gets into gear. Are you one of the fans who can't wait to get their hands on the iPhone 7 or iPhone 7 Plus? If so, feel free to hit us up in the comments section below and let us know. This goes without saying, but hat tip to 9to5Mac for getting the word out. © 2016 Tech Times, All rights reserved. Do not reproduce without permission.


News Article | August 30, 2016
Site: http://www.techtimes.com/rss/sections/smartphone.xml

Apple is widely expected to launch a new Space Black color choice when it announces the iPhone 7 and iPhone 7 Plus. Newly leaked images appear to show both the iPhone 7 and iPhone 7 Plus in both Space Black and Blue aluminum cases. According to early reports, Samsung is seeing high demand for its latest flagship phablet, the Galaxy Note 7. The new Blue Coral color the company introduced with the launch of the Note 7 is in short supply, with many customers reporting having to face up to three weeks of wait time when placing an order. The company also just announced that its Pink Gold Galaxy S7 and Galaxy S7 edge are now available for purchase in the United States. When Samsung announced the Pink Gold Galaxy S7 and S7 edge in April, many saw the move as being influenced by Apple's Rose Gold iPhone 6s and iPhone 6s Plus. It now looks like Apple could be gaining inspiration from Samsung's Blue Coral Galaxy Note 7, as images of what are reportedly the iPhone 7 and iPhone 7 Plus in both Space Black and metallic Blue color choices. In June, an image of what was believed to be an iPhone 7 in a Deep Blue color leaked and it was said to replace the Space Gray color in Apple's iPhone 7/7 Plus lineup. Reports quickly surfaced claiming Apple was actually going to introduce a new Space Black hue that matches the Space Black Apple Watch. The new images show a much lighter Blue metallic tone than the Deep Blue iPhone 7 leaked in June. As you can see in the image above, the iPhone 7 Plus features a similar design to that of the iPhone 6s Plus with a few tweaks. The antenna bands that run across the rear of the iPhone 6/6s series have been relocated to the top and bottom of the handset to create a cleaner look. The iPhone 7 Plus' 12-megapixel dual-lens rear camera protrudes slightly. A Blue iPhone 7 is also shown next to a Blue iPhone 7 Plus, showing the smaller handset's reported 12-megapixel rear camera, which is expected to also include OIS (optical image stabilization). It's unclear if Apple is going to launch both a new Space Black and Blue color options for the iPhone 7 and iPhone 7 Plus or stick to its usual move of introducing one new color. Several images of the Space Black iPhone 7 and iPhone 7 Plus have leaked and considering that the Space Black color is already an Apple Watch color option and likely Apple Watch 2 choice, chances are good we'll see it announced on Sept. 7. We'll keep you posted on all iPhone 7 and iPhone 7 Plus news as we head into next week's event. © 2016 Tech Times, All rights reserved. Do not reproduce without permission.


News Article
Site: http://phys.org/technology-news/

Go: a game of complexity and a symbol for unity of contradiction. Credit: Chinese Association of Automation On March 15, 2016, Lee Sodol, an 18-time world champion of the ancient Chinese board game Go, was defeated by AlphaGo, a computer program. The event is one of the most historic in the field of artificial intelligence since Deep Blue bested chess Grandmaster Garry Kasparov in the late 1990s. The difference is that AlphaGo may represent an even bigger turning point in AI research. As outlined in a recently published paper, AlphaGo and programs like it possess the computational architecture to handle complex problems that lie well beyond the game table. Invented over 2500 years ago in China, Go is a game in which two players battle for territory on a gridded board by strategically laying black or white stones. While the rules that govern play are simple, Go is vastly more complex than chess. In chess, the total number of possible games is on the order of 10100. But the number for Go is 10700. That level of complexity is much too high to use the same computational tricks used to make Deep Blue a chess master. And this complexity is what makes Go so attractive to AI researchers. A program that could learn to play Go well would, in some ways, approach the complexity of human intelligence. Perhaps surprisingly, the team that developed AlphaGo, Google Deep Mind, did not create any new concepts or methods of artificial intelligence. Instead, the secret to AlphaGo's success is how it integrates and implements recent data-driven AI approaches, especially deep learning. This branch of AI deals with learning how to recognize highly abstract patterns in unlabeled data sets, mainly by using computational networks that mirror how the brain processes information. According to the authors, this kind of neural network approach can be considered a specific example of a more general technique called ACP, which is short for "artificial systems," "computational experiments," and "parallel execution." ACP effectively reduces the game space AlphaGo must search through to decide on a move. Instead of wading through all possible moves, AlphaGo is trained to recognize game patterns by continuously playing games against itself and examining its game play history. In effect, AlphaGo gets a feel for what Go players call "the shape of a game." Developing this kind of intuition is what the authors believe can also advance the management of complex engineering, economic, and social problems. The idea is that any decision problem that can be solved by a human being can also be solved by any AlphaGo-like program. This proposal, which the authors advance as the AlphaGo thesis, is a decision-oriented version of the Church-Turing thesis, which states that a simple computer called a Turing machine can compute all functions computable by humans. AlphaGo's recent triumph therefore holds a lot of promise for the field of artificial intelligence. Although advances in deep learning that extend beyond the game of Go will likely be the result of decades more research, AlphaGo is a good start. Explore further: Human champion certain he'll beat AI at ancient Chinese game


We can get a hint of why the Go victory is important, however, by looking at the difference between the companies behind these game-playing computers. Deep Blue was the product of IBM, which was back then largely a hardware company. But the software – AlphaGo – that beat Go player Lee Sedol was created by DeepMind, a branch of Google based in the UK specialising in machine learning. AlphaGo's success wasn't because of so-called "Moore's law", which states that computer processor speed doubles roughly every two years. Computers haven't yet become powerful enough to calculate all the possible moves in Go – which is much harder to do than in chess. Instead, DeepMind's work was based on carefully deploying new machine-learning methods and integrating them within more standard game-playing algorithms. Using vast amounts of data, AlphaGo has learnt how to focus its resources where they are most needed, and how to do a better job with those resources. This is roughly how it works. At any point in the game, the algorithm has to consider the game tree, a theoretical diagram describing every possible move and countermove up to any depth, in order to work out the best move to make next. This tree is way too big for any computer to search fully and so various methods exist for making a reasonable decision based on just a section of the tree. The use of neural networks allowed AlphaGo to assess a particular move without exploring its consequences too deeply. Neural networks are a class of learning algorithms that can be trained by being shown many examples of the required behaviour. In this case they were trained by providing AlphaGo with examples from millions of past matches, as well as from playing millions of matches with itself. The finer details may be interesting only to few experts, but the take-home message is important for everybody. Most of the machine-learning elements of AlphaGo's software were rather general-purpose modules. This means they weren't designed specifically just to play Go. Actually, some of them closely resemble the computer tools currently used for analysing images, and others are like the reinforcement learning tools found in various game playing programmes. This means that we can expect other applications like this, where machine-learning elements are combined in other ways or embedded into other types of software, to give them a new advantage. This might mean more intelligent self-driving cars, or digital personal assistants. The past few years have been an exciting time for artificial intelligence, and there is more to come. But we should also consider for a moment the many concerns that are being voiced about its future. These are not specifically related to AlphaGo but the general field of data-driven AI. This kind of technology does not pose a threat to the existence of our species but it does pose some challenge to our everyday lives. There is some concern about artificial intelligence making human workers redundant, of course. But we should also consider how our autonomy can be affected if we allow data-driven machines to make decisions that affect us, and the technology that makes this possible. Through the internet, we rely on a rather unified global data infrastructure for much of our communication and many of our transactions: from payments and purchases to access to news, education and entertainment. The artificial intelligence that powers this infrastructure means that it is isn't a passive medium but instead is looking back at us. It is constantly trying to learn from our actions, to infer our intentions, and – when appropriate – to gently nudge us in some direction. Sometimes this is to encourage us to buy something, but work has also been done on technologies that try to persuade us to change our attitudes or to alter our emotions. This is the dominant business model of artificial intelligence on the internet: to make us click. Having lots of very intelligent agents competing for our attention, and perhaps even competing for influence on our behaviour, is a scenario that should be carefully investigated. This goes beyond the basic web surveillance we have learnt about in the last few years and covers the possibility of machines inferring, predicting and perhaps even shaping our behaviour. Would we want to engage in an online conversation with an online digital assistant in the future when the tool is as smart as AlphaGo – and is programmed to pursue its own goals? If there is any risk coming today from artificial intelligence it does not involve killer robots. It comes from our willingness to embed data-driven AI at the very centre of the internet that we depend so heavily on before having fully understood its consequences for our privacy and autonomy. There won't be robots chasing us down the street any time soon, but there might be lots of online agents each pursuing their own goals, observing us, inferring our aims and using that information to steer us. Not quite an existential threat, but still something to consider before it is too late. Explore further: Go master: AI will one day prevail but beauty of Go remains

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