To see into the future, even just by a few minutes, is a superhero-style gift none of us have. Except, in a twist of fate, we've been granted the power of foresight, and have used it to see into the future of smartphones. No, we didn't get bitten by a radioactive spider or suffer a risky experiment-gone-wrong. The phones in question aren't nailed together concepts that'll never be sold, either. They're genuine examples of next-generation phone tech that you can buy today.

Sir Tony Hoare - British computing pioneer and senior scientist at Microsoft Research - believes the computer industry needs a "grand challenge" to inspire it. In the same way that the lunar challenge in the 1960s sparked a decade of collaborative innovation and development in engineering and space technology, or the human genome project united biologists around the globe, so too must computer scientists pull together on such a scale to take their industry to its next major milestone. Speaking last Tuesday at an open day at Microsoft Research's lab in Cambridge, Hoare told the audience of around 60 journalists and analysts that there are seven such challenges facing researchers today. Significantly, these are not purely computational challenges, but involve a mix of disciplines from biology and psychology, right through to quantum physics. This reflects how much other areas rely on and use IT to support their research, but also the changing nature of computer science itself.

Using analogical model formulation with sketches to solve Bennett Mechanical Comprehension Test problems.

"Face recognition is an important social skill, but not all of us are equally good at it," says Beijing Normal University cognitive psychologist Jia Liu. A new study by Liu and colleagues Ruosi Wang, Jingguang Li, Huizhen Fang, and Moqian Tian provides the first experimental evidence that the inequality of abilities is rooted in the unique way in which the mind perceives faces. "Individuals who process faces more holistically"--that is, as an integrated whole--"are better at face recognition," says Liu. The findings will appear in an upcoming issue of Psychological Science, a journal published by the Association for Psychological Science. In daily life, we recognize faces both holistically and also "analytically"--that is, picking out individual parts, such as eyes or nose.

These nightmarish images offer a unique and mesmerizing insight into how computers see the world - and what happens when a mind-bogglingly complex system of artificial intelligence is let off its leash. To discern and process the billions of photos that pass through its site, Google engineers designed ingenious tools known as artificial neural networks, or'ANNs'. Google's ANNs are programmed, through an endless stream of similar photos, to recognise objects within images by their distinctive characteristics. For example, the ANN will be taught to recognise a fork by processing millions of pictures of forks - eventually understanding that it has a handle and two to four tines. Now Google is offering the public these networks' codes - allowing people to upload photos and mutate them into terrifying and wonderful versions of the original.

Miss Piggy doesn't like it when I ask her about Kermit. We're having a conversation in Facebook's Messenger app, and the famous Muppet is trying to convince me to watch her fictional talk show Up Late With Miss Piggy, whose backstage hijinks provide the plot of ABC's show The Muppets. She gushes about the show's house band, Electric Mayhem--"VERY hairy"--and asks me whether I'd rate the program a 10 out of 10 or a mere 9 out of 10. But when I ask about her green ex-lover, she snaps. "I wish everyone would stop asking me about Kermit...I've moved on."

This article is by Featured Blogger Bernard Marr from his LinkedIn page. Cars are increasingly generating more and more data as they become ever more connected and empowered by smart, Internet of Things technology. The need to capitalize on this data is forcing auto manufacturers to rethink their data strategies. Thanks to modern telemetry, vehicles have been gathering and transmitting data on how they are used for several decades. But the real explosion in data volume is down to customer data available from the applications and services available to today's motorists.

Robots are rapidly becoming more and more lifelike and unrecognizable from humans. Eerily human-like robots built by China's University of Science and Technology stole the show over at the World Robot Conference in Beijing last week. You may have already seen Jia Jia, a super realistic robot capable of micro facial expressions and basic conversation with humans. Jia Jia can also recognize faces, identify your gender and age, and detect your facial expressions. But a newcomer robot also made a debut: the university unveiled a male robot that can accurately paint calligraphy.

Last summer at the Black Hat cybersecurity conference, the DARPA Cyber Grand Challenge pitted automated systems against one another, trying to find weaknesses in the others' code and exploit them. "This is a great example of how easily machines can find and exploit new vulnerabilities, something we'll likely see increase and become more sophisticated over time," said David Gibson, vice president of strategy and market development at Varonis Systems. His company hasn't seen any examples of hackers leveraging artificial intelligence technology or machine learning, but nobody adopts new technologies faster than the sin and hacking industries, he said. "So it's safe to assume that hackers are already using AI for their evil purposes," he said. "It has never been easier for white hats and black hats to obtain and learn the tools of the machine learning trade," said Don Maclean, chief cybersecurity technologist at DLT Solutions.