Word embeddings have been shown to be useful across state-of-the-art systems in many natural language processing tasks, ranging from question answering systems to dependency parsing. (Herbelot and Vecchi, 2015) explored word embeddings and their utility for modeling language semantics. In particular, they presented an approach to automatically map a standard distributional semantic space onto a set-theoretic model using partial least squares regression. We show in this paper that a simple baseline achieves a +51% relative improvement compared to their model on one of the two datasets they used, and yields competitive results on the second dataset.

Audi's Formula E race driver Lucas Di Grassi warned fans of impending doom to motorsport if autonomous cars become commonplace, at the FIA Sports Conference in Turin, Italy. "The threat we face in motorsport is autonomous vehicles," said Di Grassi.

When in 2012 a computer learned to recognize cats in YouTube videos and just last month another correctly captioned a photo of "a group of young people playing a game of Frisbee," artificial intelligence researchers hailed yet more triumphs in "deep learning," the wildly successful set of algorithms loosely modeled on the way brains grow sensitive to features of the real world simply through exposure. Using the latest deep-learning protocols, computer models consisting of networks of artificial neurons are becoming increasingly adept at image, speech and pattern recognition -- core technologies in robotic personal assistants, complex data analysis and self-driving cars. But for all their progress training computers to pick out salient features from other, irrelevant bits of data, researchers have never fully understood why the algorithms or biological learning work. Now, two physicists have shown that one form of deep learning works exactly like one of the most important and ubiquitous mathematical techniques in physics, a procedure for calculating the large-scale behavior of physical systems such as elementary particles, fluids and the cosmos. The new work, completed by Pankaj Mehta of Boston University and David Schwab of Northwestern University, demonstrates that a statistical technique called "renormalization," which allows physicists to accurately describe systems without knowing the exact state of all their component parts, also enables the artificial neural networks to categorize data as, say, "a cat" regardless of its color, size or posture in a given video.

The Duchess of Cambridge, Kate Middleton, will not only make you fall in love with her timeless charms and classic beauty. Her sense of humor and fun personality would make you understand why Prince William fell in love with her. Middleton showed the crowd her funny side during a gala event by one of their friends held on June 22. An interesting reason behind her humor might shock most of the people. People Magazine reported how Kate Middleton kid around the chefs of the said event saying Prince William is patient enough to put up with her cooking.

In March, Microsoft revealed that it was using the open-world game Minecraft to train AI agents to learn how to do things like climbing a hill. The company also promised to make it available to the public so they could work on their own artificial intelligence projects and research, and it's finally available today. Project Malmo (formerly known as Project AIX) is a Minecraft mod that works on Windows, Mac and Linux, and supports just about any programming language you might want to use. So yes, that means you will need to know how to code – but Microsoft says that even novice programmers can get in on the action. You can learn more about Project Malmo here and grab the mod from this GitHub repository to try it for yourself.

Wimbledon's famous All England Lawn Tennis and Croquet Club, bedecked in the iconic purple and green and overflowing with Ivy, flowers and generations of tradition might, at first look, seem like a strange place to be contemplating how technology and sport have become such familiar bedfellows. And yet this most traditional of sporting environs is embracing technology on a whole host of levels, whilst maintaining the dignity and history as the home of tennis. Mick Desmond, the AELTC's commercial and media director explained that there was an "alchemy" in the balance of seeking what's new and innovative whilst understanding and enhancing what makes this most famous of tennis tournaments great. "We look at what's true in our brand in terms of the all-white dress code, the grass courts and the strawberries and cream, and as an event we do transcend the sport. We look at the things that make us special and we amplify them. "But in the digital realm particularly we can be very innovative and push the boundaries… whilst still enveloped in a Wimbledon way." This intent, according to former England cricketer and academic, Ed Smith, is not as new as many might think - insisting that embracing the latest technology has actually been at the heart of sport's development. "If you think about sport as we now play it, it's rested on a series of technological evolutions," he insists, taking us on a whirlwind tour of innovation, from lawnmowers to the iPhone via vulcanized rubber and, of course the impact of sport in the media. "The year 1923 brought the moment that changed sport forever - a radio station in New York saying we're going to put every second of the World Series on air.

"Really, we're building collision avoidance for industrial drones," said Alexander Harmsen, CEO and co-founder of Iris Automation. "We see this huge need for industrial drones for mining exploration, pipeline inspection, agricultural surveying, forestry, or even package delivery." Without a way to avoid mid-air collisions, drones risk crashing into a Cessna, a flock of geese or a 747. Worst case scenario: a drone gets sucked into a jet engine causing catastrophic engine failure as high-velocity bits of metal penetrate fuel tanks, hydraulic lines and the cabin. Iris Automation's solution is an AI computer that blends real-time images and 3D maps to track incoming objects.

Google has announced that it had acquired a visual recognition machine learning technology start-up, Moodstocks for an undisclosed amount. Incorporated in 2009, the French start-up originally introduced on-device image recognition in 2012, which enables smartphones to automatically recognize the content their camera detects. With the recent acquisition, Google had made a substantial investment in the machine learning technologies since accurate object recognition is one of the difficult problems for machine learning. For the past 3 years, the small team of researchers and engineers based in Google has taken advantage of deep learning to extend the reach of their object recognition algorithms, licensing machine-readable executable object code including the launch of a software development kit for OEMs to enable them to embed the code into their products. The Moodstocks API, meanwhile, enable users to integrate visual search into their applications in addition to analyzing the images related to color, shape, and texture.

People on the streets of London, Düsseldorf, Bern, and other European cities will soon see robots rolling along the sidewalk to deliver prepared food, parcels, and retail goods to nearby customers. Starship Technologies, a robotics company launched two years ago by Skype cofounders Ahti Heinla and Janus Friis, says it plans to announce on Wednesday four industry partners that will be testing its delivery robots: Just Eat, a European food delivery company; Hermes, a German parcel delivery company (not to be confused with the similarly named French luxury goods maker); Metro Group, a major German retailer; and Pronto.co.uk, a food delivery service based in London. The four companies later this month will begin tests with real customers to assess the viability of automated product deliveries. A similar program is being developed for cities in the US. Starship has been conducting proof-of-concept tests without real customers for the past nine months.

Artificial intelligence that can understand and answer any work-related question it is asked has been made available in the UK for the first time. The computer software, called Starmind, uses machine learning to understand queries, then source answers from previous staff conversations on a subject or track down experts within the company who are able to help. Its creators refer to it as'brain technology', adding its aim is to become a central knowledge bank within any company, an instant database of information that can be accessed by anyone. AI software which understands and answers work-related questions has been made available in the UK. Starmind is an artificial intelligence software for the workplace, designed in Switzerland.