National Electric Vehicle Sweden (NEVS), the company that discontinued the Saab name last June, debuted its InMotion electric level 5 autonomous car concept at CES Asia that's essentially a modular room on wheels. There aren't even any dashboard controls: Occupants adjust the seating arrangements, lighting and environmental settings with a paired app. A concept for the self-driving living/working space of the future has arrived. With its plastic rhomboid shape and wheel pods, the InMotion looks the part of a forward-looking concept car. Even the windows are intended to operate as displays, maximizing workspace.

The U.S. entertainment and media business is expected to see modest-to-slow growth over the next five years, according to a new report from PricewaterhouseCoopers. The report singles out traditional sectors such as cinema and television as areas of sluggishness, while pointing to emerging industries including e-sports and virtual reality as sectors poised for the most growth. Total domestic revenue for all media and entertainment industries is expected to rise less than 4% annually over the next five years, reaching $759 billion in 2021. The forecast data comes as media companies are scrambling to find innovative ways to reach consumers in a rapidly changing environment. "[Entertainment and media] companies are operating amidst a wave of geopolitical turbulence, regulatory changes and technological disruption," Mark McCaffrey, PwC's leader for U.S. technology, media, and telecommunications, said in a statement.

We tackle the problem of collaborative filtering (CF) with side information, through the lens of Gaussian Process (GP) regression. Driven by the idea of using the kernel to explicitly model user-item similarities, we formulate the GP in a way that allows the incorporation of low-rank matrix factorisation, arriving at our model, the Tucker Gaussian Process (TGP). Consequently, TGP generalises classical Bayesian matrix factorisation models, and goes beyond them to give a natural and elegant method for incorporating side information, giving enhanced predictive performance for CF problems. Moreover we show that it is a novel model for regression, especially well-suited to grid-structured data and problems where the dependence on covariates is close to being separable.

Kiva is an online non-profit crowdsouring microfinance platform that raises funds for the poor in the third world. The borrowers on Kiva are small business owners and individuals in urgent need of money. To raise funds as fast as possible, they have the option to form groups and post loan requests in the name of their groups. While it is generally believed that group loans pose less risk for investors than individual loans do, we study whether this is the case in a philanthropic online marketplace. In particular, we measure the effect of group loans on funding time while controlling for the loan sizes and other factors. Because loan descriptions (in the form of texts) play an important role in lenders' decision process on Kiva, we make use of this information through deep learning in natural language processing. In this aspect, this is the first paper that uses one of the most advanced deep learning techniques to deal with unstructured data in a way that can take advantage of its superior prediction power to answer causal questions. We find that on average, forming group loans speeds up the funding time by about 3.3 days.

Recently, the attention mechanism plays a key role to achieve high performance for Neural Machine Translation models. However, as it computes a score function for the encoder states in all positions at each decoding step, the attention model greatly increases the computational complexity. In this paper, we investigate the adequate vision span of attention models in the context of machine translation, by proposing a novel attention framework that is capable of reducing redundant score computation dynamically. The term "vision span" means a window of the encoder states considered by the attention model in one step. In our experiments, we found that the average window size of vision span can be reduced by over 50% with modest loss in accuracy on English-Japanese and German-English translation tasks.% This results indicate that the conventional attention mechanism performs a significant amount of redundant computation.

Google is poised to begin a grand experiment in using machine learning to widen access to healthcare. If it is successful, it could see the company help protect millions of people with diabetes from an eye disease that leads to blindness. Last year researchers at the search and ads company announced that they had trained image recognition algorithms to detect signs of diabetes-related eye disease roughly as well as human experts. The software examines photos of a patient's retina to spot tiny aneurisms indicating the early stages of a condition called diabetic retinopathy, which causes blindness if untreated. At the 2017 WIRED Business Conference in New York City today, a leader of Google's project said that work has begun on integrating the technology into a chain of eye hospitals in India.

After receiving more than 18,300 applications, NASA has finally announced its new class of astronauts – some of whom could move on to deep-space missions aboard the Orion spacecraft. The space agency introduced 12 men and women today on stage at the Johnson Space Center in Houston, during an event that was attended by Vice President Mike Pence. Vice President Pence wished'Godspeed' to the new class, and revealed the Trump administration will be reopening the National Space Council, with Pence as a chair, in efforts to'ensure that America will never again lose our lead in space exploration and space innovation technology.' The lineup includes: Kayla Barron, Zena Cardon, Raja Chari, Matthew Dominick, Bob Hines, Dr Warren'Woody' Hoburg, Jonny Kim, Robb Kulin, Jasmin Moghbeli, Loral O'Hara, Dr Frank Rubio, Jessica Watkins The chosen few will undergo two years of training, after which they will be assigned to various missions, including research on the International Space Station, launches aboard commercial spacecraft, and even deep-space exploration. After brief introductions from Johnson Center Director Ellen Ochoa and the showing of a video from current astronauts welcoming the newcomers, Flight Operations Director Brian Kelly introduced the new candidates one by one, in alphabetical order. The lineup includes: Kayla Barron, Zena Cardon, Raja Chari, Matthew Dominick, Bob Hines, Dr Warren'Woody' Hoburg, Jonny Kim, Robb Kulin, Jasmin Moghbeli, Loral O'Hara, Dr Frank Rubio, Jessica Watkins.

It's a dirty job but somebody's got to do it. And when it comes to the expensive, claustrophobic and sometimes dangerous work of inspecting natural gas and oil pipelines, that somebody is a robot. "We can make sure that these critical elements of energy infrastructure operate more safely, more reliably, more economically," said Edward Petit de Mange, the managing director at the San Diego hub of Diakont, an international high-tech engineering and manufacturing company with offices in Russia, Italy and North America. According to the federal government, more than 2.6 million miles of pipelines supply the nation's energy needs. But aging and deteriorating pipelines pose substantial risks.

For militaries, tech like this provides an excellent platform for surveillance missions against military and terrorist targets. It can utilize its high flight ceiling to maintain line-of-sight contact with over 400,000 square miles of ground and water. For both militaries and tech firms, covering so much territory makes it an excellent data relay and communications node. This will allow the drone to replace or back up satellite communications, maintain coverage between distant aircraft and ships, or even provide broadband to rural Chinese households. While conversations around drone usage are often limited to their roles as potential missile-toting killers and parcel-delivering quadcopters, some of the most important drones of the future may be those like the Caihong X and Helios Prototype, unseen and high up, gathering data day in and day out.

As someone so flummoxed by traffic I wrote a book about it, I have a near-clinical aversion to vehicular congestion. My global default strategy is to simply drive as little as possible, but there are times when I simply must put foot to gas pedal. Like many, I have become increasingly dependent on the Waze app, which, via each drivers' smartphone, turns an inchoate, undifferentiated mass of drivers into something resembling a collective form of networked intelligence. Waze, it occurred to me the other day while stuck in a bit of unexpected congestion (which had been duly flagged by at least 13 "Wazers"), is helping us turn into ants. Every time drivers travel down a path, Waze tracks their speed--information that can then be broadcast to every following driver.