Data from intelligence, surveillance and reconnaissance (ISR) technologies stream in every second of every day from nearly every corner of the globe, fed by a vast and varied network of data-gathering devices and systems controlled by the United States, including a constellation of satellites, squadrons of drones and other surveillance tools. These platforms generate massive amounts of information; the Navy alone creates a Library of Congress' worth of ISR data every day, but the vast majority of that goes unanalyzed. Other experts cite even more modest figures. "We analyze 0.5 percent or less than 0.5 percent of all the data that's available to us," says Michael Moskal, manager of research programs at Modus Operandi, a company that contracts with the Department of Defense on big-data analysis. "What are we going to do with the other 99.5 percent of the data? Now, however, the DOD is trying to narrow the gap between information and analysis by deploying artificial intelligence to enhance that crucial military state -- situational awareness. The program at the forefront of this effort is Project Maven. Launched in 2017, the initiative established an Algorithmic Warfare Cross-Functional Team to "accelerate DOD's integration of big data and machine learning … [turning] the enormous volume of data available … into actionable intelligence and insights at speed," claims an April 2017 Department of Defense memorandum. Project leaders first focused Maven on drone video, which was inundating analysts with daily terabytes of footage. Before the military turned to AI, "it took a team of analysts working 24 hours a day to exploit only a fraction of one drone's sensor data," Gregory Allen, an adjunct fellow at the Center for a New American Security, writes in the Bulletin of the Atomic Scientists. The Maven team initially aimed to create an algorithm by the end of 2017 to help fight ISIS. That ambitious goal bore fruit in December, when the team deployed an algorithm that could identify objects of interest, like cars. "Eventually, we hope that one analyst will be able to do twice as much work, potentially three times as much, as they're doing now.

Though nearly seventy percent of Earth's surface is comprised of water, only three percent is considered fresh and drinkable--and most of that striking minority is trapped in glaciers or polar ice caps. Juxtapose the dearth of natural drinking water with the disquieting realization that nearly a billion people still lack unfettered access to clean water, and the world's oceans suddenly look a lot smaller. This global quandary has led to the ambitious goal of making oceans drinkable--but doing so is going to require a ton of innovation and processing power. Let's take a look at how the next generation of supercomputers might help solve our water challenges and more. Researchers at the Lawrence Livermore National Laboratory believe the answer lies in carbon nanotubes (and a whole lot more, but let's start here for now).

It's called the "grain," a small IoT device implanted into the back of people's skulls to record their memories. Human experiences are simply played back on "redo mode" using a smart button remote. The technology promises to reduce crime, terrorism and simplify human relationships with greater transparency. While this is a description of Netflix's Black Mirror episode, "The Entire History of You," in reality the concept is not as far-fetched as it may seem. This week life came closer to imitating art with the $19 million grant by the US Department of Defense to a group of six universities to begin work on "neurograins."

At AcademyHealth's 2018 Health Datapalooza on Thursday, the US Food and Drug Administration offered a vote of confidence for artificial intelligence in healthcare, promising more refined strategies for regulation, touting its tech incubator for AI innovation, and announcing a new machine learning partnership with Harvard. "We're implementing a new approach to the review of artificial intelligence," FDA Commissioner Dr. Scott Gottlieb said. As one example, he pointed to the agency's approval earlier this year of a new clinical decision support software that uses AI algorithms to help alert neurovascular specialists of brain deterioration faster than existing technologies. "AI holds enormous promise for the future of medicine, and we're actively developing a new regulatory framework to promote innovation in this space and support the use of AI-based technologies," Gottlieb said. "So, as we apply our Pre-Cert program -- where we focus on a firm's underlying quality -- we'll account for one of the greatest benefits of machine learning -- that it can continue to learn and improve as it is used."

Over the past two years, as the debate over immigration policies has grown increasingly heated, an argument was often introduced as a counter to some of the more abrasive stances. At first glance, it may have appeared to be a fact-based response to the animosity and divisiveness that defined the debate. For those of us with deeper, first-hand knowledge though, it was just as fear-based and misinformed. The argument was most succinctly summed up by an op-ed in The Los Angeles Times, "Robots, not immigrants, are taking American jobs." It states, "A White House report released in December says 83% of U.S. jobs in which people make less than $20 per hour are now, or soon will be, subject to automation … and warns Americans to get ready for an era of 60% unemployment."

According to a new report by the House of Lords select committee on AI, the UK is already in an exceptionally strong position. "Britain contains leading AI companies, a dynamic academic research culture, a vigorous start-up ecosystem and a constellation of legal, ethical, financial and linguistic strengths located in proximity to each other," the committee concludes. In the heavy industry sector, the cost of unpredicted repairs or machine failures can be very expensive. For example: A cargo train with an engine failure in will incur costs from its own repairs, from the transit required to reach the broken down engine, and with holding up other trains and cargo in the process. In this episode of the O'Reilly Podcast, I talk with Simon Moss, vice president of industry consulting and solutions, Americas, at Teradata.

The Pentagon claims it will beat carmakers to produce widely used self driving vehicles. 'We're going to have self-driving vehicles in theater for the Army before we'll have self-driving cars on the streets,' Michael Griffin, the undersecretary of defense for research and engineering, Griffin told the House Armed Services Committee members on April 18. 'But the core technologies will be the same.' Officals told a Capitol Hill hearing'We're going to have self-driving vehicles in theater for the Army before we'll have self-driving cars on the streets,'. It comes amid a race between Waymo, Uber and Tesla and others to get self driving cars on roads around the world. However, the Pentagon is targetting the battlefield for its self driving vehicles. Griffin claims 52 percent of casualties in combat zones can been attributed to military personnel delivering food, fuel and other logistics.

Facebook's smart speakers may be hitting the shelves soon after all. The social media giant was set to release a pair of voice-activated smart speakers at its F8 developers conference this week, but ultimately delayed the launch in the wake of its massive privacy scandal. Now, Facebook is considering rolling out the devices internationally, before possibly bringing them stateside to the US, CNBC reported, citing sources familiar with the situation. Facebook CEO Mark Zuckerberg was expected to unveil a pair of smart speakers at the firm's 2018 F8 developers conference, but the firm put those plans on hold after its privacy scandal The devices would come with a smart assistant built in that's connected to Facebook's artificial intelligence technology called'M'. However, it seems that the firm ended its role as a text-based virtual assistant, and now may have plans for M to serve as the technology powering its voice-activated digital assistant.

On-demand robotic flying taxis and drone deliveries are years away from reality, but the U.S. Department of Transportation has removed at least one barrier to their operation. Responding to work by companies including Boeing Co., Intel Corp. and Uber Technologies Inc., the department on Friday said it would use the same process to consider approval of drone taxis that would carry passengers and cargo for hire as it does for approving traditional commercial air carriers. Inc. and Alphabet Inc.'s Google unit are both developing drones to deliver products. Under U.S. law, the agency must certify that any business carrying people or cargo for hire is economically "fit, willing and able" to perform. Certifying an airline under those regulations can take years, but the DOT can exempt operators that are using smaller or mid-sized aircraft, it said in a notice set to be posted Monday in the Federal Register.

Artificial intelligence (AI) has worked its way into a variety of industries, from the obvious (autonomous vehicles) to the hidden (anti-money laundering due diligence). But while organizations are clearly recognizing the value associated with incorporating AI into their business processes, they also are encountering a number of challenges with integrating this new intelligence into their operational processes. The value of using algorithms to unmask hidden patterns and then correlate findings with other seemingly unrelated variables to create real "intelligence" is becoming increasingly clear with each completed proof-of-concept (POC) project. But it is the larger, organization-wide deployment of AI that will generate the return on investment (ROI) that companies large and small have been seeking. To fully access the operational and economic benefits of AI, however, organizations are realizing that, in most cases, enabling AI is not a plug-and-play proposition.