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.

Katherine Johnson as she received the Medal of Freedom. Johnson played a pivotal role in the American space program. She was one of the first African-American women to work at NASA (and the agency's predecessor, the National Advisory Committee for Aeronautics). A mathematician, she worked as a "human computer" performing calculations for the Mercury, Apollo, and Shuttle programs. A NASA biography of Johnson says she was so respected by her peers that "John Glenn requested that she personally re-check the calculations made by the new electronic computers before his flight aboard Friendship 7--the mission on which he became the first American to orbit the Earth."

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.

Human-robot strike teams, autonomous land mines, and covert swarms of minuscule robotic spies: the US Department of Defense's idea of the future of war seems like a sci-fi movie. In a report that dreams of new ways to destroy adversaries and protect American assets in equal portions, the DOD's science research division cements the idea that artificial intelligence and autonomous robotic systems will be a crucial part of the nation's ongoing defense strategy. US military already uses a host of robotic systems in the battlefield, from reconnaissance and attack drones to bomb disposal robots. However, these are all remotely-piloted systems, meaning a human has a high level of control over the machine's actions at all times. The new DOD report sees tactical advantages from humans and purely self-driven machines working together in the field.

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

A CIA-linked firm has joined forces with Amazon in a bid to use "computer vision" to snoop on the Earth in unprecedented detail. CosmiQ Works, a firm closely associated with the US intelligence agency, is working with the online retail giant and the satellite mapping firm DigitalGlobe to train algorithms to work out what's happening on the surface of our planet. Satellites can already capture astonishingly detailed images from up in space, but the CIA-linked project wants to go one step further and use artificial intelligence to analyse these pictures. The partners hope to collect 60 million satellite images and store them in a database called SpaceNet which will be open and accessible by members of the public. Programmers will then design algorithms which can work out what's happening in the images or highlight the buildings, objects and natural features in the photos.

Aptonomy Inc. has developed drone technology that could make prison breaks, robberies or malicious intrusions of any kind impossible for mere mortals. Dubbing it a kind of "flying security guard," the company has built its systems on top of a drone often used by movie-makers, the DJI S-1000, a camera-carrying octocopter. To that skeleton, Aptonomy adds a new flight controller, and second computer to power day- and night-vision cameras, bright lights, and loudspeakers, among other things. And more importantly than the hardware features, Aptonomy has developed artificial intelligence and navigational systems that allow its drones to fly low and fast, avoiding obstacles in structure-dense environments, and detecting human activity or faces in the area, autonomously. A user can open up a browser, get onto the Aptonomy interface, click on a point on a map to send out a drone to a particular location, then watch that flight in real time, or review a recording of it later.

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?

Graphs are a common data modality, useful for modeling complex relationships between objects, with applications spanning fields as varied as biology (Jeong et al., 2001; Bullmore and Sporns, 2009), sociology (Wasserman and Faust, 1994), and computer vision (Foggia et al., 2014; Kandel et al., 2007), to name a few. For example, in neuroscience, vertices may be neurons and edges adjoin pairs of neurons that share a synapse (Bullmore and Sporns, 2009); in social networks, vertices may correspond to people and edges to friendships between them (Carrington et al., 2005; Yang and Leskovec, 2015); in computer vision, vertices may represent pixels in an image and edges may represent spatial proximity or multi-resolution mappings (Kandel et al., 2007). In many useful networks, vertices with similar attributes form densely-connected communities compared to vertices with highly disparate attributes, and uncovering these communities is an important step in understanding the structure of the network. There is an extensive literature devoted to uncovering this community structure in network data, including methods based on maximum modularity (Newman and Girvan, 2004; Newman, 2006b), spectral partitioning algorithms (Luxburg, 2007; Rohe et al., 2011; Sussman et al., 2012; Lyzinski et al., 2014b), and likelihood-based methods (Bickel and Chen, 2009), among others. In the setting of vertex nomination, one community in the network is of particular interest, and the inference task is to order the vertices into a nomination list with those vertices from the community of interest concentrating at the top of the list.