If you're just talking about the time until we see some driverless vehicles on the road, probably not that long. Anthony Foxx, secretary of the U.S. Department of Transportation, went on the record in 2015 with the claim that "we're going to see [fully autonomous cars] within five years," though he allows that that "just means market availability." A more comprehensive timeline assembled by Recode suggests that by 2030, "Automakers will stop manufacturing cars that don't have at least some highly autonomous features." It goes on to predict that by the middle of the 21st century, we'll witness total fleet turnover, at which point virtually all vehicles on the road will be at least partially autonomous. If that's true, it's possible that driving your own car will rapidly come to be seen as a dangerous affectation like smoking

The future of the way we work will be put under the spotlight by some of the leading minds in technology and computing at the International Festival for Business 2016 (IFB2016). The advancement of Artificial Intelligence and its use in modern life has prompted debate on whether men and women will be needed to work in business in years to come. Scientists have created robots who can do cognitive work and the nightmarish future of androids ruling the planet with brutal oppression has long been a science fiction storyline. A panel which will include a brain specialist, a tech lawyer, a technology expert and a union leader will explore the possibility of humans becoming obsolete in the workplace in reality. The discussion, Man and Woman vs Machine: Is AI Going to Take Your Job?' will take over the Blue Skies Stage in the Liverpool Exhibition Centre on Thursday June 16.

Algorithmic systems that employ machine learning play an increasing role in making substantive decisions in modern society, ranging from online personalization to insurance and credit decisions to predictive policing. But their decision-making processes are often opaque--it is difficult to explain why a certain decision was made. We develop a formal foundation to improve the transparency of such decision-making systems. Specifically, we introduce a family of Quantitative Input Influence (QII) measures that capture the degree of influence of inputs on outputs of systems. These measures provide a foundation for the design of transparency reports that accompany system decisions (e.g., explaining a specific credit decision) and for testing tools useful for internal and external oversight (e.g., to detect algorithmic discrimination). Distinctively, our causal QII measures carefully account for correlated inputs while measuring influence. They support a general class of transparency queries and can, in particular, explain decisions about individuals (e.g., a loan decision) and groups (e.g., disparate impact based on gender). Finally, since single inputs may not always have high influence, the QII measures also quantify the joint influence of a set of inputs (e.g., age and income) on outcomes (e.g.

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

Toyota is close to acquiring two robotics companies from Google, according to a report in Wednesday's Nikkei newspaper. The newspaper said the two companies are in final talks, but details are yet to be discussed so the deal could still fall through. The deal would see Boston Dynamics, a developer of advanced two- and four-legged robots for the U.S. military, and Schaft, a Tokyo-based developer of humanoid robots, transferred to the Toyota Research Institute. The unit is a recently launched billion-dollar research arm based in Silicon Valley. Google acquired Boston Dynamics and Schaft several years ago as part of a push into robotics, but that hasn't gone anywhere since robotics head Andy Rubin left the company in late 2014.

The U.S. military's plans to build a satellite-launching robotic space plane are moving forward. On May 23, the Defense Advanced Research Projects Agency (DARPA) put out its official call for proposals for the futuristic space plane design. The goal of the Experimental Spaceplane (XS-1) project is to build a reusable space plane that, at optimal operation, should be able to fly 10 times in 10 days, at a cost of no more than 5 million per flight. Typically, space vehicles are not fully reusable, and the pieces that are reused need to undergo time-consuming safety checks between flights. XS-1 would be used primarily as a cheap and fast way to deliver satellites to orbit, DARPA officials said.

A rough estimate would show that thousands of Chinese robotics companies may have recorded, on average, annual sales of less than 100,000 yuan each. Mainland media reported that low-end robots have been installed on the assembly line of known labour-intensive factories as a showcase for local government subsidies and left unused. "There remains a big shortage of specialists who can assess the true situation in the country's robotics industry and determine arbitrary decision–making by local governments in granting subsidies," Wang said. His urgent call for a sweeping audit of subsidies to the domestic robotics industry followed a recent nationwide investigation launched by the Ministry of Finance on so-called "new energy vehicle" makers. The agency is targeting electric car manufacturers suspected of fraudulently obtaining subsidies from the government in 2013 and 2014, as well as those firms that applied last year.

The Netherlands is not the only country concerned about drones near airports. Last month, the European Aviation Safety Agency said it is setting up a task force to examine the risk of collisions between drones and aircraft.

Who says artificial intelligence doesn't involve humans? Try telling that to Silicon Valley startup Unanimous AI. After recently achieving the rare "superfecta" -- picking the top four finishers in the Kentucky Derby -- using UNU, a new form of human-based AI using algorithms, the company is ready to share its formula with the public. After more than a year of testing, the online platform is now available in open beta. UNU relies on an artificial "swarm" of human group intelligence that comes together in real time to make predictions, said Louis Rosenberg, its creator.

It's a weird feeling, cruising around Silicon Valley in a car driven by no one. I am in the back seat of one of Google's self-driving cars – a converted Lexus SUV with lasers, radar and low-res cameras strapped to the roof and fenders – as it manoeuvres the streets of Mountain View, California, not far from Google's headquarters. I grew up about eight kilometres from here and remember riding around on these same streets on a Schwinn Sting-Ray. Now, I am riding an algorithm, you might say – a mathematical equation, which, written as computer code, controls the Lexus. The car does not feel dangerous, nor does it feel like it is being driven by a human. It rolls to a full stop at stop signs, veers too far away from a delivery van, taps the brakes for no apparent reason as we pass a line of parked cars. I wonder if the flaw is in me, not the car: Is it reacting to something I can't see? The car is capable of detecting the motion of a cat, or a car crossing the street hundreds of metres away in any direction, day or night (snow and fog can be another matter). "It sees much better than a human being," Dmitri Dolgov, the lead software engineer for Google's self-driving-car project, says proudly. He is sitting behind the wheel, his hands on his lap. As we stop at the intersection, waiting for a left turn, I glance over at a laptop in the passenger seat that provides a real-time look at how the car interprets its surroundings. On it, I see a gridlike world of colourful objects – cars, trucks, bicyclists, pedestrians – drifting by in a video-game-like tableau. Each sensor offers a different view – the lasers provide three-dimensional depth, the cameras identify road signs, turn signals, colours and lights. The computer in the back processes all this information in real time, gauging the speed of oncoming traffic, making a judgment about when it is OK to make a left turn. Waiting for the car to make that decision is a spooky moment. I am betting my life that one of the coders who worked on the algorithm for when it's safe to make a left-hand turn in traffic had not had a fight with his girlfriend (or boyfriend) the night before and screwed up the code.