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Fox News Flash top headlines for Nov. 23 are here. Check out what's clicking on Foxnews.com The U.S. military announced Friday it lost an unarmed drone over Tripoli, the Libyan capital -- the site of a months-long battle between the Libyan National Army and militias allied with the United Nations-supported government. The U.S. Africa Command said the remotely piloted aircraft was part of an operation conducted in Libya to assess the area's security and monitor for violent extremist activity. They didn't give a reason for the drone loss on Thursday, but the command will be investigating.

At the Common Good in the Digital Age tech conference recently held in Vatican City, Pope Francis urged Facebook executives, venture capitalists, and government regulators to be wary of the impact of AI and other technologies. "If mankind's so-called technological progress were to become an enemy of the common good, this would lead to an unfortunate regression to a form of barbarism dictated by the law of the strongest," he said. In a related but contextually different conversation, this summer Joy Buolamwini testified before Congress with Rep. Alexandria Ocasio-Cortez (D-NY) that multiple audits found facial recognition technology generally works best on white men and worst on women of color. What these two events have in common is their relationship to power dynamics in the AI ethics debate. Arguments about AI ethics can wage without mention of the word "power," but it's often there just under the surface. In fact, it's rarely the direct focus, but it needs to be. Power in AI is like gravity, an invisible force that influences every consideration of ethics in artificial intelligence. Power provides the means to influence which use cases are relevant; which problems are priorities; and who the tools, products, and services are made to serve. It underlies debates about how corporations and countries create policy governing use of the technology.

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations.

According to Business Insider, more than 50% of the total costs for delivering goods is attributable to what is known as "last mile delivery" – the point at which the package finally arrives at the buyer's door. In a recent study by Global Industry Analysts, the last mile delivery market worldwide is expected to reach over $35 Billion by 2025. Last mile delivery is the most expensive and time-consuming part of the shipping process, either due to lack of density and long distances in rural areas or traffic congestion in urban ones. The idea of using Unmanned Aerial Vehicles (UAVs) – or drones – for last mile delivery is gaining popularity. The use of drones to deliver parcels has the potential to significantly decrease delivery costs – no driver, truck or congestion – and expand coverage areas.

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At a packed hall in Lagos, a gathering of education and technology enthusiasts cheered for a milestone moment: the launch of Nigeria's first book on artificial intelligence for primary and secondary schools. The eight-chapter book illustrated with animations is written by Olubayo Adekanmbi, convener of Data Science Nigeria (DSN). His organisation has taken an active role in democratizing artificial intelligence application and research in Nigeria. With a suite of hands-on training programmes, toolkits and events, Data Science Nigeria aims to increase Nigeria's presence on the global AI map. "AI is a catalyst for good that creates new frontiers," Adekanmbi said, in his remarks at the launch.

THE THAMES seems to draw people who work on intelligence-gathering. The spooks of MI6 are housed in a funky-looking building overlooking the river. Two miles downstream, in a shared office space near Blackfriars Bridge, lives Arkera, a firm that uses machine-learning technology to sort intelligence from newspapers, websites and other public sources for emerging-market investors. London has the right time zone, between the Americas and Asia. It is a nice place to live.

We introduce a general tensor model suitable for data analytic tasks for heterogeneous data sets, wherein there are joint low-rank structures within groups of observations, but also discriminative structures across different groups. To capture such complex structures, a double core tensor (DCOT) factorization model is introduced together with a family of smoothing loss functions. By leveraging the proposed smoothing function, the model accurately estimates the model factors, even in the presence of missing entries. A linearized ADMM method is employed to solve regularized versions of DCOT factorizations, that avoid large tensor operations and large memory storage requirements. Further, we establish theoretically its global convergence, together with consistency of the estimates of the model parameters. The effectiveness of the DCOT model is illustrated on several real-world examples including image completion, recommender systems, subspace clustering and detecting modules in heterogeneous Omics multi-modal data, since it provides more insightful decompositions than conventional tensor methods.

Massive electronic health records (EHRs) enable the success of learning accurate patient representations to support various predictive health applications. In contrast, doctor representation was not well studied despite that doctors play pivotal roles in healthcare. How to construct the right doctor representations? How to use doctor representation to solve important health analytic problems? In this work, we study the problem on {\it clinical trial recruitment}, which is about identifying the right doctors to help conduct the trials based on the trial description and patient EHR data of those doctors. We propose doctor2vec which simultaneously learns 1) doctor representations from EHR data and 2) trial representations from the description and categorical information about the trials. In particular, doctor2vec utilizes a dynamic memory network where the doctor's experience with patients are stored in the memory bank and the network will dynamically assign weights based on the trial representation via an attention mechanism. Validated on large real-world trials and EHR data including 2,609 trials, 25K doctors and 430K patients, doctor2vec demonstrated improved performance over the best baseline by up to $8.7\%$ in PR-AUC. We also demonstrated that the doctor2vec embedding can be transferred to benefit data insufficiency settings including trial recruitment in less populated/newly explored country with $13.7\%$ improvement or for rare diseases with $8.1\%$ improvement in PR-AUC.