File photo - Troopers with the U.S. Army 2nd Armored Brigade Combat Team, 3rd Infantry Division fire the main gun round at a target during unit gunnery practice with newly acquired M1A1-SA Abrams tanks at Fort Stewart, Georgia, U.S. March 29, 2018. Picture taken March 29, 2018. The targets are dispersed across expansive, mountainous terrain, yet moving in coordination for attack. The armored vehicle cannot fire upon the enemy tanks and give away its position, so it "networks" the targeting specifics to an armed overhead drone which then attacks the enemy tanks -- exploding them with Hellfire missiles, all without putting soldiers at risk. In similar fashion - perhaps a forward operating unmanned ground vehicle receives the targeting information and, controlled by a human operator, fires on the enemy tanks without exposing the location of a manned crew.

Artificial intelligence and machine learning continue to gain a foothold in our everyday lives. Whether for complex tasks like computer vision and natural language processing, or something as basic as an online chatbot, their popularity shows no signs of slowing. Companies have also started to explore deep learning, which is an advanced subset of machine learning. By applying "deep neural networks" deep learning takes inspiration from how the human brain works. Unlike machine learning, deep learning can actually train its processes directly on raw data, requiring little to no human intervention.

A new report estimates the cost of waste in the U.S. healthcare system alone ranges as high as $935 billion a year, about 25 percent of total healthcare spending. A growing army of startups and established practitioners sees the inefficiencies as a trillion-dollar opportunity to apply AI. The U.S. spends about 18 percent of its gross domestic product on healthcare, more than any other country. A report published online by the Journal of the American Medical Association surveyed 54 studies to estimate annual waste figures in six broad categories, including failures from choosing ineffective treatments (up to $166 billion), failures from coordinating multiple treatments ($78 billion), fraud and abuse ($84 billion) and administrative complexity ($266 billion). "Implementation of effective measures to eliminate waste represents an opportunity to reduce the continued increases in U.S. health care expenditures," the report concluded.

India has just 144 police officers for every 100,000 citizens, compared to 318 per 100,000 citizens in the European Union. In recent years, authorities have turned to facial recognition technology to make up for the shortfall. New Delhi's law enforcement agencies adopted the technology in 2018, and it's also being used to police large events and fight crime in a handful of other states, including Andhra Pradesh and Punjab. But India's government now has a much more ambitious plan. It wants to construct one of the world's largest facial recognition systems.

The mission of the Future Concepts Directorate is to serve as the Judge Advocate General's Corps' (JAGC) subject matter expert on the application of the law to future conflict by assessing the legal requirements of the future operational environment, to propose and review Army doctrine, and to provide an intellectual foundation and disciplined approach to design, develop, and field a globally responsive future JAG Corps. Your browser does not support the video tag. Future Concepts Directorate (FCD) executes its mission along three lines of effort. RUSSIA'S NEW HYPERSONIC MISSILE TRAVELS NEARLY TWO MILES A SECOND WEAPONIZING BIOTECH: HOW CHINA'S MILITARY IS PREPARING FOR A'NEW DOMAIN OF WARFARE'

We're about to enter the "exascale era" of computing, which could have widespread positive impacts for governments, businesses and society at large. The U.S. Department of Energy recently announced contracts for supercomputers that will each provide more than an exaflop of performance. That means they can perform 1 quintillion mathematical calculations (called floating-point operations, or flops) every second. The work done on these systems will impact all our lives. And the technologies developed for them will enhance computing systems at all scales, transforming the future of the enterprise.

Fox News Flash top headlines for Oct. 25 are here. Check out what's clicking on Foxnews.com NASA has unveiled its plan to send a new lunar rover, VIPER, to the surface of the Moon. "VIPER is going to rove on the South Pole of the moon and assess where the water ice is," said NASA Administrator Jim Bridenstine during a wide-ranging speech at the International Astronautical Congress in Washington D.C. on Friday. The government space agency notes that the Moon has vast reservoirs of water ice, an amount that could potentially reach millions of tons.

BROOKLYN, New York, Tuesday, October 22, 2019 – The ubiquity and sheer volume of data generated today give experts in virtually every domain ample information to track everything from financial trends, disaster evacuation routes, and street traffic, to animal migrations, weather patterns, and disease vectors. But using this data to build visualizations of complex predictive models using machine learning is a challenge to experts who lack the requisite computer science skills. A team at the NYU Tandon School of Engineering's Visualization and Data Analytics (VIDA) lab, led by Claudio Silva, professor in the department of computer science and engineering, developed a framework called VisFlow, by which those who may not be experts in machine learning can create highly flexible data visualizations from almost any data. Furthermore, the team made it easier and more intuitive to edit these models by developing an extension of VisFlow called FlowSense, which allows users to synthesize data exploration pipelines through a natural language interface. The research, "FlowSense: A Natural Language Interface for Visual Data Exploration with a Dataflow System" won the best-paper award at this year's IEEE Conference on Visual Analytics Science and Technology (VAST).

Texas A&M is playing a leading role in expanding the capabilities of automated vehicles and investigating how they can be safely used on rural roads. The Texas A&M Engineering Experiment Station (TEES) was recently awarded $7 million in federal grant funding from the Department of Transportation (DOT). In partnership with researchers from George Washington University and the University of California-Davis, A&M professors will be studying the specifics of how automated vehicles work on rural roadways -- something Alireza Talebpour, assistant professor in the Department of Civil and Environmental Engineering, said not much is currently known about. "Autonomous vehicle testing has pretty much only been done in urban centers," Talebpour said. "The technology is useless if it only works in big cities – the majority of roads in the United States are rural. We want to enable autonomous driving for people who don't live in big cities."

The Open Geospatial Consortium's (OGC) Innovation Program is testing ways to increase public safety by sharing data that smart cities collect. The project, called the Smart City Interoperability Reference Architecture (SCIRA), is part of a long-running partnership with the Homeland Security Department's Science and Technology Directorate. The program aims to develop standards and practices to improve first responders' awareness and communication by studying how shared data can drive decisions and make disaster response more effective. "That really rests on being able to build a framework of systems that work together," Innovation Program Director Josh Lieberman said. The plan is to bring together data from sensors and other sources along with insights from people.