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

Google is worried about artificial intelligence. No, not that it will become sentient and take over the world, but that, say, a helpful house robot might accidentally skewer its owner with a knife. The company's latest AI research paper delves into this issue under the title "Concrete Problems In AI Safety." Really, though, that's just a fancy way of saying "How Are We Going To Stop These Terror-Bots Killing Us All In Our Sleep." To answer this brain-tickler, Google's researchers have landed on five "practical research problems" -- key issues that programmers will need to consider before they start creating the next Johnny Five.

Angela Duckworth, in her best-selling book, "Grit: The Power of Passion and Perseverance," celebrates a man whom she calls a "grit paragon": Pete Carroll, the coach of the Seattle Seahawks, who led the team to a Super Bowl victory in 2014. It seems that Carroll had seen Duckworth's TED talk nine months earlier and got in touch, eager to reassure her that building grit was exactly what the Seahawks culture was all about. Two years later, Duckworth visited the Seahawks training camp. She lectured to the team's players and coaching staff. The subject was . . . Duckworth was impressed by the Seahawks, and she quotes sentiments that are characteristic of the Carroll ethos: "Compete in everything you do. Since the team trains ferociously all the time--going all out, for instance, in bone-crunching intra-squad practice sessions--this conversation may not have been entirely necessary. Duckworth, a professor of psychology at the University of Pennsylvania, finds grit in the best possible places. Her grit obsession, as she recounts, began at least a decade earlier. As a graduate student, she visited West Point, where each year twelve hundred new cadets go through a gruelling seven-week training regimen ("Barracks Beast") before entering freshman year. Most make it through, though some do not. Duckworth could make some guesses. In this same period, eager to find out what made top people successful, she was interviewing "leaders in business, art, athletics, journalism, academia, medicine and law." She discovered that "the highly successful had a kind of ferocious determination that played out in two ways.

Industrial robots used to be big, unwieldy, and dangerous, but new "human-safe" robots are now commonplace on automotive lines, working right next to people. Yet these robots are awkward coworkers; they coexist with us but do not meaningfully collaborate. Robots often need to be explicitly told how to be helpful or when to stay out of the way -- things human teammates seem to learn intuitively. A good human apprentice is a keen observer, inferring unspoken rules and customs, watching how others work, and then generalizing this knowledge for new situations. We are able to accomplish this partly because the human mind is able to process very complex information very efficiently.

It seems like everyone wants to invest in artificial intelligence (AI). And it's not just the tech giants: USAA is using AI to protect its users from identity theft and Under Armour has connected its health app, MyFitnessPal, to IBM Watson so users can get a more thorough read of their health. AI is already a 15 billion dollar industry, according to the MIT Technology Review, with more than 2,600 companies developing their own tech, and the value of AI is reported to rise to over 70 billion by 2020. Because of AI's business opportunities, hundreds of designers in digital agencies, people who were taught to create products and services that live on the Internet, are starting to build physical products that interact with us, respond to our moods, and make decisions for us. It's a challenge that requires every skill they've learned, plus many they haven't. Still, designers know the basics: The principles of user-centric design lay the groundwork for building a great AI system.

If you walk down the street and see two people meeting in front of a café, you know they'll shake hands or even hug a few seconds later, depending on how close their relationship is. Left: still frame given to the algorithm which had to predict what happened next. MIT used deep-learning algorithms -- neural networks that teach computers to find patterns by themselves from an ocean of data -- to build an artificial intelligence which can predict what action will occur next, starting from nothing but a still frame. Each of these networks was programmed to classify an action as either a hug, handshake, high-five, or kiss. These networks are then merged to predict what happens next.

Based on the agreement, both parties will work together on new technology innovation, enhanced user experience and developing a new e-commerce platform for enterprise-level products. Together, the companies are dedicated to driving the next generation of e-commerce artificial intelligence solutions, and conducting associated research and development for high-speed interconnect products. A key technology that JD.com has developed is JD Camera, an application for image recognition and similar image search in mobile terminals. JD Camera facilitates ease-of-shopping for users by allowing customers to quickly and easily search for their favorites products with just a photo rather than detailed language descriptions. "In the future, with the help of the Joint Lab, Camera will be enhanced from general photo-based searches to more advanced imaged-based searches that will allow users to view, select and purchase from suggested recommendations with an advanced image match algorithm for such items as clothing, make-up, furniture, etc.," said Weng Zhi, vice president of technology, JD.com.

The Clock Drawing Test (CDT) is a rapid, inexpensive, and popular neuropsychological screening tool for cognitive conditions. The Digital Clock Drawing Test (dCDT) uses novel software to analyze data from a digitizing ballpoint pen that reports its position with considerable spatial and temporal precision, making possible the analysis of both the drawing process and final product. We developed methodology to analyze pen stroke data from these drawings, and computed a large collection of features which were then analyzed with a variety of machine learning techniques. The resulting scoring systems were designed to be more accurate than the systems currently used by clinicians, but just as interpretable and easy to use. The systems also allow us to quantify the tradeoff between accuracy and interpretability. We created automated versions of the CDT scoring systems currently used by clinicians, allowing us to benchmark our models, which indicated that our machine learning models substantially outperformed the existing scoring systems.

Combating malware is very important for software/systems security, but to prevent the software/systems from the advanced malware, viz. metamorphic malware is a challenging task, as it changes the structure/code after each infection. Therefore in this paper, we present a novel approach to detect the advanced malware with high accuracy by analyzing the occurrence of opcodes (features) by grouping the executables. These groups are made on the basis of our earlier studies [1] that the difference between the sizes of any two malware generated by popular advanced malware kits viz. PS-MPC, G2 and NGVCK are within 5 KB. On the basis of obtained promising features, we studied the performance of thirteen classifiers using N-fold cross-validation available in machine learning tool WEKA. Among these thirteen classifiers we studied in-depth top five classifiers (Random forest, LMT, NBT, J48 and FT) and obtain more than 96.28% accuracy for the detection of unknown malware, which is better than the maximum detection accuracy (95.9%) reported by Santos et al (2013). In these top five classifiers, our approach obtained a detection accuracy of 97.95% by the Random forest.

The metamorphic malware variants with the same malicious behavior (family), can obfuscate themselves to look different from each other. This variation in structure leads to a huge signature database for traditional signature matching techniques to detect them. In order to effective and efficient detection of malware in large amounts of executables, we need to partition these files into groups which can identify their respective families. In addition, the grouping criteria should be chosen such a way that, it can also be applied to unknown files encounter on computers for classification. This paper discusses the study of malware and benign executables in groups to detect unknown malware with high accuracy. We studied sizes of malware generated by three popular second generation malware (metamorphic malware) creator kits viz. G2, PS-MPC and NGVCK, and observed that the size variation in any two generated malware from same kit is not much. Hence, we grouped the executables on the basis of malware sizes by using Optimal k-Means Clustering algorithm and used these obtained groups to select promising features for training (Random forest, J48, LMT, FT and NBT) classifiers to detect variants of malware or unknown malware. We find that detection of malware on the basis of their respected file sizes gives accuracy up to 99.11% from the classifiers.