World Go champion Lee Se-dol has beaten AlphaGo, an AI program developed by Google's DeepMind unit this weekend, though he still trails the program 3-1 in the series. Google's publicity stunt highlights the progress which has been made in the world of artificial intelligence and machine learning, as commentators predicted a run-away victory for Se-dol. DeepMind founder Demis Hassabis commented on Twitter "Lee Sedol is playing brilliantly! We are in trouble now…" allowing Se-dol to win the fourth game in the five game series. While the stunt demonstrates the potential of machine learning, Se-dol's consolation victory proves that the technology is still capable of making mistakes.

A Google artificial intelligence program has beaten the European champion of the board game Go. The Chinese game is viewed as a much tougher challenge than chess for computers because there are many more ways a Go match can play out. The tech company's DeepMind division said its software had beaten its human rival five games to nil. One independent expert called it a breakthrough for AI with potentially far-reaching consequences. The achievement was announced to coincide with the publication of a paper, in the scientific journal Nature, detailing the techniques used.

These days, Google is making headlines as its artificial intelligence (AI) AlphaGo beated top pro Go player Lee Se-dol 2:0 in a highly publicized five-game Go series. The internet search giant is expanding its AI business by taking over four robotics companies including DeepMind which designed AlphaGo. But in Korea, AI is an underdeveloped and poorly invested sector. "Korean companies have not made much progress in AI research. They still have a long way to go in terms of AI commercialization," said Jin Jeong-yeol, director of the Kohyoung Technology.

Just over a week ago, Google Deepmind's AlphaGo machine crushed 18-time World Go Champion Lee Sedol 4-1 in a 5 game series, heralding an achievement many experts predicted to be at least a decade away. And whilst the victory of machine over man was a great result for Google, Machine Learning, and Artificial Intelligence (AI) - it also served as a chilling reminder that the ever-extending arm of AI is showing absolutely no sign of slowing. DeepMind founder Demis Hassabis has stated that Go is "probably the most complex game ever devised by man." For starters, it's played on a 19 by 19 board, which allows for 10171 possible layouts, versus roughly 1050 possible configurations on a standard chessboard, and an estimated 1080 atoms in the universe. Because of this, players are often said to rely heavily on sub-conscious intuition or'gut feeling'.

DeepMind's stunning victories over Go legend Lee Se-dol have stoked excitement over artificial intelligence's potential more than any event in recent memory. But the Google subsidiary's AlphaGo program is far from its only project -- it's not even the main one. As co-founder Demis Hassabis said earlier in the week, DeepMind wants to "solve intelligence," and he has more than a few ideas about how to get there. Hassabis himself has had an unusual path to this point, but one that makes perfect sense in retrospect. A child chess prodigy who won the Pentamind championship at the Mind Sports Olympiad five times, he rose to fame at a young age with UK computer games developers Bullfrog and Lionhead, working on AI-heavy games like Theme Park and Black & White, and later forming his own studio, Elixir. Hassabis then left the games industry in the mid-2000s to complete a PhD in neuroscience before co-founding DeepMind in 2010.

This helps to validate DeepMind's machine learning techniques and the neural network construction behind it. Having proven their mettle in Go, the DeepMind team could now have the confidence (and funding) to tackle more complex AI challenges. ARTIFICIAL INTELLIGENCE (AI) just overcame a new hurdle: learning to play Go, a game considered thousands of times more complex than chess--well enough to beat the greatest human player at his own game. South Korean national Lee Se-dol, one of the world's top Go players, won only one of the five matches against Google's AlphaGo, missing out on the 1-million prize up for grabs in a recent'challenge' held in Seoul. AlphaGo, an AI system developed by Google DeepMind, just bested the best Go-playing human currently alive. This was not supposed to happen.

It may not look like one of Isaac Asimov's robots or sound like HAL from "2001: A Space Odyssey," but artificial intelligence is here, and it is already having a huge impact on how the world works. From the way you shop for a pair of shoes online to how fast a Formula 1 team can push its car's engine, AI is helping businesses across the globe save millions by improving performance and efficiency. Still, problems like trust and security, not to mention fears of the so-called singularity, when artificial intelligence would overtake human thinking, remain hurdles that the technology must overcome before it goes mainstream. AI hit the news this week after a program called AlphaGo, developed by engineers at DeepMind, the AI startup acquired by Google in 2014 for 580 million, defeated the world's No. 1 Go player Lee Sedol. AlphaGo beat Sedol 4 games to 1, claiming a 1 million prize.

Trying to understand what is _really_ going on in the universe.

In science news around the world, the first part of the two-part ExoMars program is on its way to the Red Planet, Google's DeepMind computer program AlphaGo beats the human world Go champion four games to one, China plans to create its own "Defense Advanced Research Projects Agency," the U.S. Environmental Protection Agency announces plans to further limit methane emissions from oil and gas wells, the U.S. Food and Drug Administration green-lights a plan to release mosquitoes in Florida that have been genetically modified to be sterile, and more. Also, German defense minister Ursula von der Leyen, who was accused of plagiarism in her 1990 dissertation, was cleared of misconduct by her degree-granting institution. And a watercolor painting showing the intricate structure of an Ebola virus wins the 2016 Wellcome Image Awards' overall prize.

In this paper we consider a version of the zero-shot learning problem where seen class source and target domain data are provided. The goal during test-time is to accurately predict the class label of an unseen target domain instance based on revealed source domain side information (\eg attributes) for unseen classes. Our method is based on viewing each source or target data as a mixture of seen class proportions and we postulate that the mixture patterns have to be similar if the two instances belong to the same unseen class. This perspective leads us to learning source/target embedding functions that map an arbitrary source/target domain data into a same semantic space where similarity can be readily measured. We develop a max-margin framework to learn these similarity functions and jointly optimize parameters by means of cross validation. Our test results are compelling, leading to significant improvement in terms of accuracy on most benchmark datasets for zero-shot recognition.