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?

Given a graph in which a few vertices are deemed interesting a priori, the vertex nomination task is to order the remaining vertices into a nomination list such that there is a concentration of interesting vertices at the top of the list. Previous work has yielded several approaches to this problem, with theoretical results in the setting where the graph is drawn from a stochastic block model (SBM), including a vertex nomination analogue of the Bayes optimal classifier. In this paper, we prove that maximum likelihood (ML)-based vertex nomination is consistent, in the sense that the performance of the ML-based scheme asymptotically matches that of the Bayes optimal scheme. We prove theorems of this form both when model parameters are known and unknown. Additionally, we introduce and prove consistency of a related, more scalable restricted-focus ML vertex nomination scheme. Finally, we incorporate vertex and edge features into ML-based vertex nomination and briefly explore the empirical effectiveness of this approach.

It's no secret that the U.S. military sees artificial intelligence in its future, for everything from swarming drones to automated cybersecurity practices. And despite its clear potential for military applications, top Pentagon researchers also have acknowledged that AI currently has a lot of limitations--machines can parse greater amounts of information more quickly than humans, but they still can't think like humans. But although machines can't really understand the human mind, humans might be falling behind in understanding the machines they've created. That's part of what's behind an effort by military researchers called Explainable Artificial Intelligence (XAI), which looks to create tools that allow a human on the receiving end of information or a decision from an AI machine to understand the reasoning that produced it. In essence, the machine needs to explain its thinking.