It seems it may only be a matter of time before the best Go player on the planet is a computer. AlphaGo beat the European champion in Go and was driven by machine learning, a technology that has underpinned the recent major advances in artificial intelligence in computer vision, speech recognition and language translation.1 Machine learning is a data driven approach to artificial intelligence. AlphaGo learnt how to play Go by many games played against itself, and by observing a large history of games played by professional players. The end result is that by the time of its first match against the European Champion AlphaGo had already played many more games of Go than any human could possibly play in their lifetime. And since that win AlphaGo has been actively learning to improve itself. Relentlessly playing all day and all night in an effort to ready itself to play the world champion.

Singapore-basedAPIs - Helping to Make Tech Invisible. Read more ... » startup is making a buzz in the broadcast and advertising sector with a promising technologyTaiwanese entrepreneur selected as'young global leader'. Read more ... » across TV, Radio, Digital Signage, Cinema, Mobile, WebHow AI informs the customer service experience. Read more ... » and connected TV. Launched in 2014, EYWAMEDIA, enables broadcasters and advertisers to enable audience consumption patterns, engage them real-time, create a content-ad strategy and finally create attribution, cross targeting and retargeting revenues using multiscreen technology.

In 1957, Herbert A. Simon, a pioneer in artificial intelligence and later a Nobel Laureate in economics, predicted that in 10 years a computer would surpass humans in what was then regarded as the premier battleground of wits: the game of chess. Though the project took four times as long as he expected, in 1997 my colleagues and I at IBM fielded a computer called Deep Blue that defeated Garry Kasparov, the highest-rated chess player ever. You might have thought that we had finally put the question to rest--but no. Many people argued that we had tailored our methods to solve just this one, narrowly defined problem, and that it could never handle the manifold tasks that serve as better touchstones for human intelligence. These critics pointed to weiqi, an ancient Chinese board game, better known in the West by the Japanese name of Go, whose combinatorial complexity was many orders of magnitude greater than that of chess. Noting that the best Go programs could not even handle the typical novice, they predicted that none would ever trouble the very best players.

Mergers and acquisitions are a reality in the technology world and one that most startups and small businesses have to confront at some point. One of the key questions your organisation will no doubt ask itself if approached for a buy-out or capital injection is: "Am I going to get the best value for my business in the current climate?" Similarly, large enterprises need to consider whether they're getting the most bang for their buck or punch for their pound when mulling an acquisition. So what does the landscape look like in 2016? In the past, the tech world has experienced'boom-and-bust' economic cycles - think the bursting of the dot-com bubble in the late-90s, or the impact of the pan-European recession from as early as 2007.

That was the result of the match between Google's AlphaGo and human champion Lee Sedol at the fiendishly complex game of Go, and it came with a disconcerting question: what next? Where will the machines claim their next victory: putting you out of a job; solving the mysteries of science; bettering human abilities in the bedroom? AlphaGo's success was down to artificial intelligence (AI): the computer program taught itself how to improve its game by playing millions of matches against itself. But the trouble with using games such as chess and Go as measures of technological progress is that they are competitions. There's a winner and there's a loser – and this month's biggest tech news story had a clear victor.

Interesting article posted recently in MIT Technology Reviews. What kind of metrics would help detect such tweets? Which algorithm would you use? I am thinking about tweets indexation, The same NLP (natural language processing) technique can be applied to email messages and other texts produced by users, maybe even to detect if a piece of code was written when the programmer was drunk.This would require the use of a training set, to train the algorithm. But no matter the ML algorithm used, you will need to work with a training set anyway.

When HP Labs research scientist Lei Liu was a child in XianYang, China, he read a newspaper article detailing how HP originated in a garage in Palo Alto. "That inspired me," he recalls. "Silicon Valley was clearly somewhere where you could have a dream, incubate it, and see it come true." Today, Lei is living that dream as a member of HP's Print and 3D Lab. After studying for his B.S. and M.S. in computer science at the Beijing University of Posts and Telecommunications, he moved to Michigan State University where he received his Ph.D. in Computer Science and Engineering, focusing on data mining and machine learning.

During the holiday season, warehouses across the country used different kinds of robots to fill retail orders and they made--and continue to make--warehouses more efficient than ever before. In fact, if robots were more broadly implemented it's estimated retailers could reduce their fulfillment costs by 450 million to 900 million in North America, according to research from Janney Capital Markets. Today there is a growing group of startup companies developing robots for use in manufacturing, e-commerce and logistics operations. A decade ago, robots working in a warehouse -- or, for that matter, writing newspaper stories, caring for the elderly or providing comfort to hospitalized children -- was the stuff of science fiction. But today, the world is on the cusp of a rapid economic transformation via robotics and artificial intelligence.

Ride-hailing service Uber has sounded out car companies about placing a large order for self-driving cars, an auto industry source has said. "They wanted autonomous cars," the source, who declined to be named, said. "It seemed like they were shopping around." Loss-making Uber would make drastic savings on its biggest cost -- drivers -- if it were able to incorporate self-driving cars into its fleet. Volkswagen's Audi, Daimler's Mercedes-Benz, BMW and car industry suppliers Bosch and Continental are all working on technologies for autonomous or semi-autonomous cars.

Shedding drivers would save Uber a lot of money. Ride-hailing service Uber has sounded out car companies about placing a large order for self-driving cars, an auto industry source has said. "They wanted autonomous cars," the source, who declined to be named, said. "It seemed like they were shopping around." Loss-making Uber would make drastic savings on its biggest cost -- drivers -- if it were able to incorporate self-driving cars into its fleet.