Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

For 20 years she's been studying what transport gurus call shared mobility, dissecting factors that make it successful, like shorter wait times. Though you might think choosing how to get to work is simple, Shaheen will tell you that especially in urban areas, there are countless factors in play, from the time of day you're on the move to whether you own a smartphone. And more than any of the tech executives giving speeches lately about how we all should rethink our relationship to our cars, she knows just how revolutionary that could be. Walking the halls of one of the nation's oldest departments dedicated to transportation research, at the University of California, Berkeley, the engineering professor wonders how our lives would have turned out if the Model T had been marketed as something to be shared, not owned home by home. Would our vehicles still be idle an estimated 95% of the time?

Anticipating whether the car in front is going to take the next left or is slowing down is a fundamental part of driving, and key to not totalling your car. While keeping your eyes ahead should be second nature to those on the road, this most basic of tasks is still a challenge for driverless cars. But details have emerged of a patent filed by Google for its autonomous vehicles to detect and track brake and indicator lights of other cars on the road. Google has filed a patent for its autonomous vehicles to detect and track brake and indicator lights of other cars on the road. This will enable the driverless cars to better anticipate the movements of cars on the road. The technology would enable the driverless cars to anticipate the movements of cars on the road ahead using a forward-facing camera.

Today there is an ongoing battle between man and machine. While genuine machine consciousness is still years into the future, we are beginning to see computers make choices that previously demanded a human's input. But we are not beaten yet - humans are still superior in some areas and researchers in Denmark have developed a scheme that is mapping the human brain to blur the lines between the two. While machine consciousness is still years into the future, we are beginning to see computers make choices that previously demanded a human's input. 'It may sound dramatic, but we are currently in a race with technology - and steadily being overtaken in many areas,' said Danish physicist Jacob Sherson.

In late 2013, Clay Bavor began experimenting with teleportation. He paired an Oculus Rift headset to a robotic arm, upon which he mounted a couple of GoPro cameras. When he moved his head, the thinking went, the cameras would mimic the movement, acting as a second pair of eyes. If it worked, he'd be able to "teleport" himself (or his eyes, at least) a few feet away. He still has a video of the first time he ever got it running: There's Bavor, tall and thin in a t-shirt and jeans, standing among the contraptions with the Rift on his face. He reaches out his arm, waving his hand in front of the cameras at his side while simultaneously seeing it in front of his face. "Whoaaa," he says to himself. This is like the craziest thing I've ever experienced." The so-called Teleportation Robot was just one of Bavor's many side hustles at Google. Technically, his job was to lead the company's apps teams--the folks who make Gmail and Drive and Docs. But he'd been enthralled by VR ever since he ...

Robot cage fights are pretty sweet--who doesn't like seeing machines armed with spinning sawblades flamethrowers fight to the death--but medieval robot wars are so much more fun. Think about it: Hand-built robots catapulting rocks at castles, trying to breach their defenses. George R. R. Martin couldn't come up with something that crazy. But some 1,500 high school kids in Southern California did, coming together for an epic battle last month during the Los Angeles regional championship of the "Super Bowl of Smarts." Now, the thought of a robot war set in the Middle Ages probably brings to mind all the worst stereotypes of geeks and nerds and dweebs.

The rise of on-demand startups and other fast-growing businesses like online marketplaces has created a need for reliable ways to verify new hires and customers to make sure they are cut out for the task. Onfido, one of the group of startups building software-based solutions to do just that, has raised 25 million to meet that need. The Series B round was led by Idinvest Partners, with participation also from existing backers Wellington Partners and CrunchFund (founded by TC founder Michael Arrington), and brings the total raised by the startup to just over 30 million. It's not disclosing its valuation in this round, but as a marker, its competitor Checkr, which has raised just under 50 million, is valued between 250 million and 300 million. Onfido plans to use the capital in a couple of different ways.

Facebook wants to use artificial intelligence to make it easier for people to find photos and videos on the social network. Facebook isn't working on artificial intelligence just to let you text with robots. It's also using the technology to do something much simpler: help you quickly find the photos you're searching for or the videos you want to see on the social network. The company showed off several new ways to use "machine learning," a form of AI where computers can learn to work without first being programmed, to search through its massive trove of photos and videos. "We're doing research on cracking the image open, and understanding it on an individual pixel level," Joaquin Candela, head of Facebook's Applied Machine Learning group, said Wednesday at F8, the company's annual conference for software developers.

Artificial intelligence, machine learning, discovery algorithms… At this stage of human development we find ourselves gazing at a fantastic horizon, one where we have the opportunity to design intelligent entities -- robot brains. Will we design them in the image of our own neural networks? In today's research landscape, scientists are giving robots resilience and adaptability, deep learning and long-term modular memory. They're asking AI to perform complex tasks not so much with efficiency in mind as novelty. And guess what: it may change how we think about not only evolution, but the way we ourselves, as a network of individuals and organizations, approach innovation. Will we design for efficiency, our minds on reaching a fixed set of desired objectives -- or will we design for resilience, adaptability… emergence? We spoke with Jeff Clune of the Evolving AI Lab about how some of the emergent effects of machine learning are taking AI in a direction we didn't expect… and what it could potentially teach us about ourselves. Jeff's work has not gone unnoticed. He's been cited in Wired, the Atlantic, Nature, and Scientific American, to name a few.

The battle for the future of computing is a battle to bring artificial intelligence to the mainstream – and Google is quietly overhauling a machine learning tool used to improve some of its most popular services including Google Translate and Google Photos. TensorFlow can be used to help teach computers how to process data in ways similar to how the human brain handles information. It is also open source, meaning Google has published and shared the code online so that external developers can use and improve it. The latest version, released by Google on Wednesday, adds a feature many TensorFlow users have asked for since the tool made its public debut in late 2015: the ability to operate on multiple devices. Instead of being limited by the processing capabilities of a single computer, it can use distributed networks to handle more complicated tasks – as if TensorFlow will now be able to use many brain cells instead of being confined to just one.