In a disappointing defeat against France, the human German soccer team was knocked out of the European Championship July 7. But at least the nation can take heart that its robots proved victorious in the annual RoboCup robot soccer tournament that took place this past weekend in Leipzig, Germany. The winning team, from the University of Bremen, called "B-Human," beat out the University of Texas, Austin's team--the wonderfully named "Austin Villa"--on penalties after a goalless draw in the final of the tournament, according to The Telegraph. Robot teams play two 10-minute halves on a 9-by-6-meter pitch, autonomously roving about the field trying to pass the ball to teammates and score goals. But unlike the cool efficiency that the German human soccer team is known for, the robots are a bit more ramshackle in their approach to the sport.

Discussing the pros and cons of articial intelligence is, to be sure, an emotional topic. People have feared artificial intelligence (AI) almost as soon as it was invented. Hollywood in particular has done a masterful job of stoking those fears with movies like "2001: A Space Odyssey," "Terminator" and "The Matrix" all making AI systems into demonic forces. Most recently, renowned scientist Stephen Hawking told Larry King he thought humans and AI could "co-exist," but that "a rogue AI could be difficult to stop" without appropriate safeguards in place. Coming from someone like Hawking, that kind of warning carries some weight with people.

Future historians of technology may look back at one week this March as a tipping point of a new era, and it all started with smooth black-and-white stones on a simple wooden board. It was a five-game match of Go, the ancient Chinese board game, pitting top-ranked world champion Lee Se-dol against an artificial intelligence system called AlphaGo from Google's DeepMind. Although Lee confidently predicted a shutout victory over AlphaGo, the system beat him a resounding 4-1. Games were live-streamed around the world, with a monumental ending reminiscent of Garry Kasparov's 1997 defeat against Deep Blue. But this AI victory goes far beyond the basic mathematical win that Deep Blue achieved. Among other factors, chess has fewer possible legal moves and a well-defined end state: checkmate.

Google buys machine vision startup focusing on'instant object recognition' It's a good time to be a machine learning startup. Two weeks after Twitter bought, has purchased . The acquisition was made for an unknown sum, and seems primarily a grab for talent. Moodstocks' engineers and researchers will move to Google's Paris R&D site, and the startup's primary commercial product -- an image recognition API for smartphones -- will be phased out. "Ever since we started Moodstocks, our Read More ... Tags: Computer systems Artificial intelligence Machine intelligence Machine learning Places: Americas North America United States Google today announced it has acquired French machine learning startup Moodstocks for an undisclosed sum. The deal is expected to close in the next few weeks and seems to be focused primarily on the talent, with the team at Moodstocks moving to Google's Paris R&D site, and its image recognition API for smartphones to be gradually phased out.

An artificial intelligence platform, called Rechtwijzer, could soon give lawyers in Australia a run for their money by being called on in legal battlegrounds like divorce, custody, employment and debt disputes. National Legal Aid and RMIT University are showcasing Dutch technology that could make time-consuming and expensive court conflict a thing of the past. The technology would be similar to eBay's dispute resolution service that helps people log on, rather than lawyer up. The dispute resolution robot was born in the Netherlands and can mediate everything from divorces, tenancy disputes, and employment, debt and consumer matters. For custody matters, for example, it will ask the ages of the children to be sensitive to their development needs.

Up to 40 per cent of current Australian jobs could disappear within the next 10 to 15 years as robots and computers continue their unstoppable advance. They have already replaced humans in workplaces such as factories, supermarkets and airline check-in counters. Hugh Durrant-Whyte, director at the Centre for Translational Data Science at the University of Sydney, said technology was taking on middle-class professions once thought safe from automation -- professions such as law, accountancy and banking. "We always used to think of automation as moving everybody up," he said. "The big difference now is machine learning and artificial intelligence are solving jobs that we thought traditionally were very highly qualified jobs … it's eating out the middle of the job market, rather than the bottom end."

Time is the most valuable and finite asset in the world. Our weekly dose of "Monday Morsels" centre's on optimising our time to impact our lives and business. We have no intent on the theme, just purely lessons that can consistently be used. This week we cover quantum computing, how reality is not what it seems, what's on the other side of fear, and which artificial intelligence platforms will reign. Richard Feynman is undoubtedly the most famous physicist since Einstein and the man that first embedded my intrigue for science & technology.

The skies above are growing increasingly crowded with satellites zipping across Earth's upper atmosphere, relaying signals for everything from the picture on your television to the map on your phone. While this constant communication makes the world go round, all of that orbiting technology brings with it a problem, in the form of space junk – the debris from rocket launches and defunct satellites which hangs on in space. Lapping the Earth at thousands of miles per hour means even smallest chunks of metal or flecks of paint can cause significant damage if they run into the path of a satellite. But scientists at the University of Surrey are gearing up to test technologies which could target potentially hazardous space junk and remove it from orbit before it can cause any damage. The first experiment will use a net to capture a target in the form of a small CubeSat launched from the main satellite.

A system of nested dichotomies is a method of decomposing a multi-class problem into a collection of binary problems. Such a system recursively applies binary splits to divide the set of classes into two subsets, and trains a binary classifier for each split. Although ensembles of nested dichotomies with random structure have been shown to perform well in practice, using a more sophisticated class subset selection method can be used to improve classification accuracy. We investigate an approach to this problem called random-pair selection, and evaluate its effectiveness compared to other published methods of subset selection. We show that our method outperforms other methods in many cases when forming ensembles of nested dichotomies, and is at least on par in all other cases.

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