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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

Soon, software will know how you feel--and will use that data to sell you things. The gig economy will go global (but it's not Uber-take-all). The tech industry will finally be inclusive. AI will achieve something like common sense, and it will be open source too. But that future won't build itself. Actual people (at least for now) have to make these things happen, and they aren't the C-suite hotshots you always hear about. The 25 people in these pages are the unsung creative, technical, and social visionaries working to bring the incredible world of tomorrow to you today. Get to know them now. Welcome to our second annual Next List. Surveillance is about to get much harder for overly snoopy governments. In November 2014 the Facebook-owned messaging service WhatsApp made a big change to its Android app: It encrypted messages so that even Facebook can't descramble them, no matter how many court orders the company receives. But the crypto software wasn't written by a Facebook employee.

For anyone that hasn't yet joined the Becoming a Data Scientist Podcast Data Science Learning Club, I thought I'd write up a summary of what we've been doing! The first activity involved setting up a development environment. Some people are using R, some using python, and there are several different development tools represented. In this thread, several people posted what setup they were using. I posted a "hello world" program and the code to output the package versions.

Diffbot, an artificial intelligence company that helps clients extract and combine data from multiple Web sources, announced today it raised 10 million from investors including Tencent and Felicis Ventures to expand its "knowledge-as-a-service" offerings to businesses and consumer apps. The Palo Alto, CA-based startup, founded in 2009, still has a tiny staff of 14. But Diffbot's ambition is huge: to catalog trillions of facts across the Web--many of them drawn from page elements such as comment forums, which can't be mined by traditional search engines. The startup says it has made a significant start on that goal, having indexed 1.2 billion entities such as people, products, and places since the middle of last year. Its Global Index also encompasses 10 to 20 times that number of facts, says Diffbot founder and CEO Mike Tung.

So, you bought into the current machine learning craze and went on to collect millions/billions of records from this promising new data source. Now, what do you do with them? Too often, the abundance of data quickly turns into an abundance of problems. How do you extract that "magic essence" from your data without falling into the common pitfalls? In her session at @ThingsExpo, Natalia Ponomareva, Software Engineer at Google, will provide tips on how to be successful in large scale machine learning.

Inside a large, windowless room in an electronics factory in south Shanghai, about 15 workers are eyeing a small robot arm with frustration. Near the end of the production line where optical networking equipment is being packed into boxes for shipping, the robot sits motionless. "The system is down," explains Nie Juan, a woman in her early 20s who is responsible for quality control. Her team has been testing the robot for the past week. The machine is meant to place stickers on the boxes containing new routers, and it seemed to have mastered the task quite nicely. But then it suddenly stopped working. "The robot does save labor," Nie tells me, her brow furrowed, "but it is difficult to maintain."

An historic milestone in artificial intelligence set by Alan Turing - the father of modern computer science - has been achieved at an event organised by the University of Reading. The 65 year-old iconic Turing Test was passed for the very first time by computer programme Eugene Goostman during Turing Test 2014 held at the renowned Royal Society in London on Saturday. 'Eugene' simulates a 13 year old boy and was developed in Saint Petersburg, Russia. The development team includes Eugene's creator Vladimir Veselov, who was born in Russia and now lives in the United States, and Ukrainian born Eugene Demchenko who now lives in Russia. The Turing Test is based on 20th century mathematician and code-breaker Turing's 1950 famous question and answer game, 'Can Machines Think?'.

But those labor savings aren't the only gains to be had from the adoption of driverless trucks. Where drivers are restricted by law from driving more than 11 hours per day without taking an 8-hour break, a driverless truck can drive nearly 24 hours per day. That means the technology would effectively double the output of the U.S. transportation network at 25 percent of the cost. And the savings become even more significant when you account for fuel efficiency gains. The optimal cruising speed from a fuel efficiency standpoint is around 45 miles per hour, whereas truckers who are paid by the mile drive much faster. Further fuel efficiencies will be had as the self-driving fleets adopt platooning technologies, like those from Peloton Technology, allowing trucks to draft behind one another in highway trains.

With technological advancement comes economic adaptation. This is one of the greatest lessons of the next 10 years which will show some pretty dramatic shifts. For the youth, this means preparing for a completely different reality and on-demand economy. Please recognize, your children will also live in a different world. A world where machine intelligence is everywhere.