An army of'killer robots' that will assist infantry on the battlefield has been unveiled in propaganda footage released by Russia The video, released by the Kremlin, appears to showcase the state's latest drone technology. That includes and AI-controlled driverless tank that follow the aim of a soldier's rifle to obliterate targets with its own weaponry. Russia's Advanced Research Foundation (ARF) said the ultimate goal is to have an army of robots entirely controlled by Artificial Intelligence algorithms. Currently the drones are deployed alongside infantry who remotely control the vehicles, but in the future the tech will be fully autonomous. That means the military hardware will be able to target and kill enemies without any human intervention.

Stanford University launched its Institute for Human-Centered AI on Monday. Known as Stanford HAI, the institute's charter is to develop new technologies while guiding AI's impact on the world, wrestle with ethical questions, and come up with helpful public policies. The Institute intends to raise US $1 billion to put towards this effort. The university kicked off Stanford HAI (pronounced High) with an all-day symposium that laid out some of the issues the institute aims to address while showcasing Stanford's current crop of AI researchers. The most anticipated speaker on the agenda was Microsoft co-founder Bill Gates.

The 2020 Democratic candidates are out of the gate and the pollsters have the call! Bernie Sanders is leading by two lengths with Kamala Harris and Elizabeth Warren right behind, but Cory Booker and Beto O'Rourke are coming on fast! The political horse-race season is upon us and I bet I know what you are thinking: "Stop!" Every election we complain about horse-race coverage and every election we stay glued to it all the same. The problem with this kind of coverage is not that it's unimportant.

With public and academic attention increasingly focused on the new role of machine learning in the health information economy, an unusual and no-longer-esoteric category of vulnerabilities in machine-learning systems could prove important. These vulnerabilities allow a small, carefully designed change in how inputs are presented to a system to completely alter its output, causing it to confidently arrive at manifestly wrong conclusions. These advanced techniques to subvert otherwise-reliable machine-learning systems--so-called adversarial attacks--have, to date, been of interest primarily to computer science researchers (1). However, the landscape of often-competing interests within health care, and billions of dollars at stake in systems' outputs, implies considerable problems. We outline motivations that various players in the health care system may have to use adversarial attacks and begin a discussion of what to do about them.

Late last year, I picked up John Carreyrou's "Bad Blood," which chronicles the long con pulled by Elizabeth Holmes, an entrepreneur who dropped out of Stanford at nineteen to found Theranos, a company that she claimed would reinvent the biomedical industry. I was instantly engrossed--"Bad Blood" unfolds like a thriller, offering a breathless barrage of details exposing how Holmes deceived her investors and colleagues at nearly every turn. Holmes wanted to disrupt the blood test: she boasted that her company was developing a method for running hundreds of lab tests from a single drop of blood, employing a machine called "The Edison" that used nanotechnology and robotics to analyze the sample. In just a few short years, thanks to Carreyrou's investigations and leaks from whistle-blowers, Holmes went from Silicon Valley's golden girl--named the youngest self-made female billionaire by Forbes--to a disgraced fraudster whose company was under investigation by the U.S. Securities and Exchange Commission. "Bad Blood" does a formidable job charting the Theranos ordeal, but it doesn't get into Holmes's head.

WASHINGTON - The top U.S. military officer plans to meet with Google representatives next week amid growing concerns that American companies doing business in China are helping its military gain ground on the U.S. Gen. Joseph Dunford says efforts like Google's artificial intelligence venture in China allow the Chinese military to access and take advantage of U.S.-developed technology. He told an audience at the Atlantic Council on Thursday that it's not in America's national security interest for U.S. companies to help the Chinese military make technological advances. Last week acting Defense Secretary Patrick Shanahan expressed similar concerns and noted that Google is stepping away from some Pentagon contracts. Google has said it would not renew a defense contract involving the use of artificial intelligence to analyze drone video.

This paper presents an integrated methodology supporting decisions in urban planning. In particular, it deals with the prioritization and the selection of a portfolio of projects related to buildings of some values for the cultural heritage in cities. More precisely, our methodology has been validated to the historical center of Naples, Italy. Each project is assessed on the basis of a set of both quantitative and qualitative criteria with the purpose to determine their level of priority for further selection. This step was performed through the application of the Electre Tri-nC method which is a multiple criteria outranking based method for ordinal classification (or sorting) problems and allows to assign a priority level to each project as an analytical "recommendation" tool. To identify the efficient portfolios and to support the selection of the most adequate set of projects to activate, a set of resources (namely budgetary constraints) as well as some logical constraints related to urban policy requirements have to be taken into consideration together with the priority of projects in a portfolio analysis model. The process has been conducted by means of the interaction between analysts, municipality representative and experts. The proposed methodology is generic enough to be applied to other territorial or urban planning problems. We strongly believe that, given the increasing interest of historical cities to restore their cultural heritage, the integrated multiple criteria decision aiding analytical tool proposed in this paper has significant potential to be used in the future.

Wide-band Electromagnetic Induction Sensors (WEMI) have been used for a number of years in subsurface detection of explosive hazards. While WEMI sensors have proven effective at localizing objects exhibiting large magnetic responses, detecting objects lacking or containing very low amounts of conductive materials can be challenging. In this paper, we compare a number of target detection algorithms in the literature in terms of detection performance. In the comparison, methods are tested on two real-world data sets: one containing relatively low amounts of ground noise pollution, and the other demonstrating highly-magnetic soil interference. Results are quantitatively evaluated through receiver-operator characteristic (ROC) curves and are used to highlight the strengths and weaknesses of the compared approaches in hand-held explosive hazard detection.

Techno-optimist prognosticators will tell you that driverless trucks are just around the corner. They will also gently tell you--always gently--that yes, truck driving, a job that nearly 3.7 million Americans perform today, is perhaps on the brink of extinction. A startup called Peloton Technology sees the future a bit differently. Based in Mountain View, California, the eight-year-old company has a plan to broadly commercialize a partially automated truck technology called platooning. It would still depend on drivers sitting in front of a steering wheel, but it would be more fuel efficient and, hopefully, safer than truck-based transportation today.

The age of artificial intelligence (AI) has arrived, and is transforming everything from healthcare to transportation to manufacturing. America has long been the global leader in this new era of AI, and is poised to maintain this leadership going forward. Realizing the full potential of AI for the Nation requires the combined efforts of industry, academia, and government. The Administration has been active in developing policies and implementing strategies that accelerate AI innovation in the U.S. for the benefit of the American people. These activities align with four main pillars of emphasis: AI for American Innovation, AI for American Industry, AI for the American Worker, and AI with American Values.