Tokyo stocks snapped their four-session losing streak on Tuesday as the yen weakened against the dollar. The Nikkei gained 323.74 points, or 1.94 percent, to close at 17,048.55. On Friday, it gave up 211.57 The market was closed on Monday for a national holiday. The Topix ended up 24.88 points, or 1.85 percent, at 1,369.93, after slumping 13.92 points the previous trading day.

This is a guest post by Ola Lidmark Eriksson, CTO at Wide Ideas. Two years ago, I asked myself if it would be possible to use machine learning to better predict the outcome of soccer games. I decided to give it a serious try and today, two years and contextual data from 30,000 soccer games later, I've gained lots of interesting insights. To begin with, I harvested as many data points as possible. I mined old game data from every different source and API I could find.

How will Practo cope up with network connectivity when Internet penetration in tier 3 cities is not high? How can telecom operators play a role in aiding remote healthcare? Today, it is true that mobile broadband has not been fully covered in our country, but we believe that India will get there soon. Today, we have around 30 percent of our traffic coming from tier 2 and 3 towns, so we can say that the Internet has reached some parts of these cities. From the B2B side, our product Practo Tab runs our entire Practo Ray software in offline mode; hence doctors and other healthcare providers can use it in this mode and then synchronize it back with the cloud once they get connectivity.

What made move 37 so interesting is that no one expected it. It was early in game two of the million-dollar Google DeepMind Challenge Match, and AlphaGo, an artificial intelligence (AI) system developed by Google, placed its 19th stone on a part of the game board that no human Go master would have considered. Some called it a "mistake." Others called it "creative" and "unique." But considering that AlphaGo went on to win its third game in a row against one of the strongest Go players in the world, the move should probably have been called what it really was: "intuitive." Note: as of March 13, 2016 AlphaGo lead its best of five match against 9-dan Go master Lee Sedol three games to one.

With characters in computer games and interactive media increasingly being based on real actors, the individuality of an actor's performance should not only be reflected in the appearance and animation of the character but also in the AI that governs the character's behavior and interactions with the environment. Machine learning methods applied to motion capture data provide a way of doing this. This paper presents a method for learning the parameters of a finite-state machine (FSM) controller. The method learns both the transition probabilities of the FSM and also how to select animations based on the current state.

Each are British artificial intelligence and machine learning startups bought by US tech giants--HP, Google, Microsoft, and Apple, respectively. Alongside growing VC funding in AI, US tech firms are snapping up British-founded startups, leading to concerns that the UK is losing the best of its artificial intelligence to Silicon Valley just as it becomes a key technology. Simon Walker, partner in corporate technology at law firm Taylor Wessing, said the sale of AI startups to US firms isn't new and doesn't look like it'll stop soon. "It is obviously disappointing that the AI cannot be retained in the UK," he said. "However, top-of-the-market AI, such as that developed by companies such as SwiftKey and DeepMind, requires huge investment and a significant platform for its use and it is only very large tech companies which have the necessary resources and platforms."

The holder of 18 international Go titles and a ninth-dan player, Mr Lee became a professional player at the age of 12 and had confidently predicted before the match-up that he would win all five games. In a worst-case scenario, he said, he feared losing one game to AlphaGo. Mr Lee said he has been playing Go - or "baduk", as it is known in Korea - for such a long time that he may have become a little jaded. But the defeat has served to rekindle the old enthusiasm, he said. Immediately after Mr Lee's defeat in the final game, the Korea Baduk Association asked Demis Hassabis, the CEO of AlphaGo designer Google DeepMind, for a rematch.

The video does not say that if Atlas comes after you, it will find you and kill you. Your own paranoid mind made that bit up for itself. But Courtney Hohne, a PR person at the corporate parent of Boston Dynamics, which made Atlas, wrote an internal email that described it as "terrifying, ready to take humans' jobs": We're not going to comment on this video because there's really not a lot we can add, and we don't want to answer most of the Qs it triggers. Here's a snatch of the video she was talking about: Google is now planning to sell Boston Dynamics, the company under Google-parent Alphabet that makes military robots, because they aren't commercial enough. "We don't want to answer most of the Qs it triggers" could mean a lot of different things -- most likely that it is difficult to get across the benefit of a product that can do a bunch of things humans can do when the media atmosphere is filled with humans worrying about machines taking their jobs (robots don't steal jobs, by the way).

Our world as we know it is running on artificial intelligence. We have cars that park themselves, and air traffic control is almost fully automated. Virtually every field has benefited from advances in artificial intelligence, from the military to medicine to manufacturing. However, almost none of the recent advancements in artificial intelligence have advanced the education industry. Why is education lagging behind?

Everything You Know About Artificial Intelligence is Wrong. Will the Singularity Artificial General Intelligence winners be Hedge Fund Managers, the Military and Spy Agencies? When Artificial Intelligence (AI) work began over 50 years ago, the AI field was directly aimed at the construction of "thinking machines"--that is, computer systems with human-like general intelligence. But this goal proved very difficult to achieve; and so, over the years, AI researchers have come to focus mainly on producing "narrow AI" systems: software displaying intelligence regarding specific tasks in relatively narrow domains. This "narrow AI" work has often been exciting and successful.