In the background, wearing a white t-shirt, is a camera man. Russia's Tigr is a decade-old armored car. Seating 10 soldiers inside with gear, the Tigr's primary missions is to get Russian forces safely to where they need to be, across rough terrain. Since it was made to be filled with people, the newest design takes the Tigr in an odd direction. Instead of a human-driven troop carrier, the latest Tigr model is a remotely controlled gun-firing robot.

Over the past two months, its team of nine developers has made it possible for the app to run offline, without connecting to its servers. The update, available Tuesday for iOS devices, is a big deal because Prisma previously needed a large amount of computing power in remote servers to process each image. That work can now be performed on individual phones thanks to the improved efficiency of the app. If you haven't used Prisma, it works a lot like Instagram, without the social network component: Upload or take a photo, and the free app will transform it into artwork in the style of famous masterpieces. You can make your selfie look like Edvard Munch's The Scream, or your backyard resemble Katsushika Hokusai's The Great Wave.

One of the trademarks that distinguishes robots from humans is the ability to reproduce. This dividing between man and machine just got blurrier. Researchers in Amsterdam have created robots that can mate and spawn offspring through a process similar to human reproduction. Robots have created quite a stir in the media recently, as more and more machines take on human tasks. Some estimates suggest automation could take over half of the work force. In addition to replacing jobs, robots could be replacing themselves.

In this paper, we deal with the problem of marginalization over and conditioning on two disjoint subsets of the node set of chain graphs (CGs) with the LWF Markov property. For this purpose, we define the class of chain mixed graphs (CMGs) with three types of edges and, for this class, provide a separation criterion under which the class of CMGs is stable under marginalization and conditioning and contains the class of LWF CGs as its subclass. We provide a method for generating such graphs after marginalization and conditioning for a given CMG or a given LWF CG. We then define and study the class of anterial graphs, which is also stable under marginalization and conditioning and contains LWF CGs, but has a simpler structure than CMGs.

Developing intelligent systems requires combining results from both industry and academia. In this report you find an overview of relevant research fields and industrially applicable technologies for building very large scale cyber physical systems. A concept architecture is used to illustrate how existing pieces may fit together, and the maturity of the subsystems is estimated. The goal is to structure the developments and the challenge of machine intelligence for Consumer and Industrial Internet technologists, cyber physical systems researchers and people interested in the convergence of data & Internet of Things. It can be used for planning developments of intelligent systems.

The robot that became racist: AI that learnt from the web finds white-sounding names'pleasant' and ... Niki.AI launches a highly capable bot for Messenger, and it can do a hell lot of things for you Gab.ai Sees Continued Success in Week One AI's, Bots and Canvases Part IV: The war is on! Refugees, Brexit and AI all to feature at Cambridge University's 2016 Festival of Ideas

Refugees, Brexit and AI all to feature at Cambridge University's 2016 Festival of Ideas How Many Finance Jobs Will AI Kill? Facebook AI's new algorithms, Navisens announces new technology, and Rackspace…

The robot that became racist: AI that learnt from the web finds white-sounding names'pleasant' and ... Niki.AI launches a highly capable bot for Messenger, and it can do a hell lot of things for you AI's, Bots and Canvases Part IV: The war is on! Refugees, Brexit and AI all to feature at Cambridge University's 2016 Festival of Ideas How Many Finance Jobs Will AI Kill?

Label ranking aims to learn a mapping from instances to rankings over a finite number of predefined labels. Random forest is a powerful and one of the most successfully general-purpose machine learning algorithms of modern times. In the literature, there seems no research has yet been done in applying random forest to label ranking. In this paper, We present a powerful random forest label ranking method which uses random decision trees to retrieve nearest neighbors that are not only similar in the feature space but also in the ranking space. We have developed a novel two-step rank aggregation strategy to effectively aggregate neighboring rankings discovered by the random forest into a final predicted ranking. Compared with existing methods, the new random forest method has many advantages including its intrinsically scalable tree data structure, highly parallel-able computational architecture and much superior performances. We present extensive experimental results to demonstrate that our new method achieves the best predictive accuracy performances compared with state-of-the-art methods for datasets with complete ranking and datasets with only partial ranking information.

A very, very small quadcopter, one inch in diameter can carry a one- or two-gram shaped charge. You can order them from a drone manufacturer in China. You can program the code to say: "Here are thousands of photographs of the kinds of things I want to target." A one-gram shaped charge can punch a hole in nine millimeters of steel, so presumably you can also punch a hole in someone's head. You can fit about three million of those in a semi-tractor-trailer. You can drive up I-95 with three trucks and have 10 million weapons attacking New York City. They don't have to be very effective, only 5 or 10% of them have to find the target. There will be manufacturers producing millions of these weapons that people will be able to buy just like you can buy guns now, except millions of guns don't matter unless you have a million soldiers. You need only three guys to write the program and launch them. So you can just imagine that in many parts of the world humans will be hunted. They will be cowering underground in shelters and devising techniques so that they don't get detected. This is the ever-present cloud of lethal autonomous weapons. Mary Wareham laughs a lot. It usually sounds the same regardless of the circumstance -- like a mirthful giggle the blonde New Zealander can't suppress -- but it bubbles up at the most varied moments. Wareham laughs when things are funny, she laughs when things are awkward, she laughs when she disagrees with you. And she laughs when things are truly unpleasant, like when you're talking to her about how humanity might soon be annihilated by killer robots and the world is doing nothing to stop it. One afternoon this spring at the United Nations in Geneva, I sat behind Wareham in a large wood-paneled, beige-carpeted assembly room that hosted the Convention on Certain Conventional Weapons (CCW), a group of 121 countries that have signed the agreement to restrict weapons that "are considered to cause unnecessary or unjustifiable suffering to combatants or to affect civilians indiscriminately"-- in other words, weapons humanity deems too cruel to use in war. The UN moves at a glacial pace, but the CCW is even worse.