Nissan will begin testing cars with self-driving abilities in London beginning next month, Reuters reports. The manufacturer will use a version of its LEAF electric car with autonomous technology on public roads in London. As PC Mag notes, the company had previously tested a self-driving prototype of the LEAF on public roads in Japan back in 2015, but the announcement marks Nissan's first tests in the European market. Alongside the testing expansion, Nissan also plans to expand its factory in England. The automotive market continues to be a popular area of investment for a variety of tech companies.

An ambitious startup claims it has an SoC that will deliver breakthrough performance-per-watt and software for it that can run high data-rate networks at ultra-low power levels. Greenwaves Technologies' GAP8 chip and GreenOFDM code aim to bring new levels of processing and communications to nodes on the Internet of Things. The eight-core chip includes a dedicated block which can process Google's Tensorflow algorithm for machine-learning tasks like image recognition. The OFDM code ultimately aims to be a new and more efficient physical layer, supporting links up to 10 Mbit/second for any IoT network. The ten-person company was formed in January when the developers behind the separate chip and software efforts met and decided to team up.

The Lily Camera, a throw-and-shoot camera, is displayed during CES Unveiled at the 2016 Consumer Electronics Show in Las Vegas in Jan. 2016. On Dec. 20, Lily Robotics was up against a wall. It was five days before Christmas, and dozens of eager customers who had spent more than $499 to pre-order the company's flagship product were wondering if they were ever going to see it. A San Francisco-based startup that had promised to build an autonomous flying camera, Lily was among the most-anticipated consumer hardware companies in Silicon Valley. In May 2015, its splashy launch video, featuring a four-propeller robot whizzing around a kayaker and snowboarder, went viral and was watched 5.3 million times in its first month.

LAUREL, MD. – Deep in the woods of suburban Maryland the men and women of the security details for President-elect Trump and Vice President-elect Pence are preparing for the worst on Inauguration Day. "We train for the scope of issues that can come up," one senior U.S. Secret Service special agent told Fox News before a training exercise Tuesday at the agency's James J. Rowley Training Center. Minutes later, while driving on a massive lot that doubled as a mock Pennsylvania Avenue, dozens of special agents, and the re-enactors playing those they were protecting, were run through a gauntlet of scenarios along the imitation parade route. Fox News embedded with the Secret Service for a training exercise that encompassed nearly 40 different scenarios that could take place over the course of the presidential ride from the steps of the U.S. Capitol to 1600 Pennsylvania Avenue. Organizers of the operation spared no detail, setting up rows of barricades in front of spectators that flanked the route, complete with protestors and unruly onlookers. From a routine ankle sprain for the First Lady to an all-out armed assault on the motorcade, the security details – as with real life protective situations – were preparing to tackle anything and everything that could come their way on January 20.

IN A former leatherworks just off Euston Road in London, a hopeful firm is starting up. BenevolentAI's main room is large and open-plan. In it, scientists and coders sit busily on benches, plying their various trades. The firm's star, though, has a private, temperature-controlled office. That star is a powerful computer that runs the software which sits at the heart of BenevolentAI's business.

Hours of screen time can be good for teenagers' brains, according to new research from the University of Oxford. The study insists many parents may be too concerned about computers harming their children. In fact, they calculated the'sweet spot' at which point young people get the most out of online activity: 257 minutes. According to their calculations, four hours and 17 minutes is the Goldilocks number, providing enough time to develop social connections and skills. It is only after that point that devices could begin to cripple teenage brains.

It is sad to report that, as a people, we have become less trusting in the world around us and the institutions we engage with on a daily basis. According to Gallup, confidence in institutions such as banks, government, and the police (to name a few) have decreased over the past 10 years. When it comes to media, trust in television news and newspapers has decreased by 10 percent since 2006. Both are now trusted by less than 21 percent of the U.S. population, per the same study. From a consumer perspective, instead of relying on salespeople or brand advertising and other traditional sources of media, we now inherently seek answers online.

Getty A Tanscorp UU smart robot is displayed at CES 2017 at the Sands Expo and Convention Center in Las Vegas. The European parliament has urged the drafting of a set of regulations to govern the use and creation of robots and artificial intelligence, including a form of "electronic personhood" to ensure rights and responsibilities for the most capable AI. In a 17-2 vote, with two abstentions, the parliament's legal affairs committee passed the report, which outlines one possible framework for regulation. "A growing number of areas of our daily lives are increasingly affected by robotics," said the report's author, Luxembourgish MEP Mady Delvaux. "In order to address this reality and to ensure that robots are and will remain in the service of humans, we urgently need to create a robust European legal framework".

On Tuesday, the White House released a chilling report on AI and the economy. It began by positing that "it is to be expected that machines will continue to reach and exceed human performance on more and more tasks," and it warned of massive job losses. Yet to counter this threat, the government makes a recommendation that may sound absurd: we have to increase investment in AI. The risk to productivity and the US's competitive advantage is too high to do anything but double down on it. This approach not only makes sense, but also is the only approach that makes sense.

The promise of compressive sensing (CS) has been offset by two significant challenges. First, real-world data is not exactly sparse in a fixed basis. Second, current high-performance recovery algorithms are slow to converge, which limits CS to either non-real-time applications or scenarios where massive back-end computing is available. In this paper, we attack both of these challenges head-on by developing a new signal recovery framework we call {\em DeepInverse} that learns the inverse transformation from measurement vectors to signals using a {\em deep convolutional network}. When trained on a set of representative images, the network learns both a representation for the signals (addressing challenge one) and an inverse map approximating a greedy or convex recovery algorithm (addressing challenge two). Our experiments indicate that the DeepInverse network closely approximates the solution produced by state-of-the-art CS recovery algorithms yet is hundreds of times faster in run time. The tradeoff for the ultrafast run time is a computationally intensive, off-line training procedure typical to deep networks. However, the training needs to be completed only once, which makes the approach attractive for a host of sparse recovery problems.