The Army wants AI to detect hidden targets, like this M109A6 Paladin armored howitzer camouflaged during wargames in Germany. AUSA: The Army has developed AI to spot hidden targets in reconnaissance photos and will field-test it in next year's massive Defender 20 wargames in Europe, the head of the service's Artificial Intelligence Task Force said here. It's just one of a host of AI applications the Army is exploring with combat applications, Brig. Shooting down drones, aiming tank guns, coordinating resupply and maintenance, planning artillery barrages, stitching different sensor feeds together into a single coherent picture, analyzing how terrain blocks units' fields of fire and warning commanders where there are blind spots in their defenses are all uses that will be tested. The most high-profile example of AI on the battlefield to date, the controversial Project Maven, used machine learning algorithms to sift hours of full-motion video looking for suspected terrorists and insurgents.

Turkey is about to add a new addition to its high technology investments and projects in order to increase efficiency in preventing smuggling and reducing formalities at customs. A total of 68 X-Ray Vehicle and Container Scanning systems, which provide detailed analysis of all kinds of vehicles and containers entering and exiting from customs gates without requiring physical intervention, were installed in all customs gates and ports with high trade volume, Trade Minister Ruhsar Pekcan said. "The Scanning Network Project will be completely developed with our local and national resources and will automatically identify illegal crime and threat elements in vehicle or cargo by using artificial intelligence (AI) technologies," Pekcan told Anadolu Agency (AA) in an interview yesterday. These high-tech systems provide x-ray film of vehicles and containers and ensure that customs controls, which can last for hours, take place quickly and in a qualified way within minutes, she said. She stressed that the ministry has established technical infrastructure to carry out customs controls in a manner that does not break the pace of international trade and to combat illegal trade in the most effective way by keeping public health and safety at the forefront.

Artificial intelligence programs are extremely good at finding subtle patterns in enormous amounts of data, but don't understand the meaning of anything. Whether you are searching the Internet on Google, browsing your news feed on Facebook, or finding the quickest route on a traffic app like Waze, an algorithm is at the root of it. Algorithms have permeated our daily lives; they help to simplify, distill, process, and provide insights from massive amounts of data. According to Ernest Davis, a professor of computer science at New York University's Courant Institute of Mathematical Sciences whose research centers on the automation of common-sense reasoning, the technologies that currently exist for artificial intelligence (AI) programs are extremely good at finding subtle patterns in enormous amounts of data. "One way or another," he says, "that is how they work."

At RMA we focus on both development and application of advanced computational models and engineering software to support complex decision making for water resources systems. We develop desktop, client-server, and mobile applications for data acquisition, visualization, and modeling of large scale river-reservoir systems. Through our collaboration with the U.S. Army Corps of Engineers and National Weather Service, our code supports emergency management operations across the country. Driven by increasing scale of water management issues and consequences, RMA is bringing distributed computing and machine learning techniques to strategic planning and real-time response.

The government has unveiled the 16 centres set to deliver a £200m, five-year programme to train 2,000 PhD students in artificial intelligence specialisms. The initiative is a core part of the AI sector deal announced by Theresa May in 2018 and is supported by £100m from government, £78m from industry, and £23m from the participating universities. Google, AstraZeneca, Rolls-Royce and the NHS are among the organisations to have backed the programme. The sector deal was announced in April last year as the government faced criticism for failing to protect the UK's tech sector in the run up to Brexit. A report by the House of Lords had claimed the UK lagged behind Germany, Canada and South Korea when it came to AI, as well as world-leaders China and the US.

One need not understand the finer details of the Dark Web in order to respect the risk these black markets pose in an increasingly data-centric world. In nearly every aspect of our modern lives, cybersecurity is becoming a necessary part of the conversation. Even novice "hackers" can rent cloud-based botnets and orchestrate DDoS attacks for around $25 per hour according to data released by Kaspersky Labs. Fortunately, the classic adage remains true, even on the Dark Web - you get what you pay for. Low-priced DDoS attacks are easily thwarted by modern network security systems designed to recognize incoming threats and divert resources to squash attacks. Thanks to advancements in Artificial Intelligence (AI) and Machine Learning (ML) in the area of cybersecurity, small businesses and enterprise-level companies can stay focused and maintain a high level of digital trust from their customers while keeping overhead costs in check.

Artificial intelligence has replaced many skills in recent years – including the skills needed to do some human jobs. The tech revolution has not gone unnoticed by American workers. A 2018 Gallup poll revealed that 70% of Americans believe AI will eliminate more jobs than it creates. Democratic presidential candidate Andrew Yang has sounded the alarm, raising the prospect that millions are at risk for long-term joblessness. I'm an expert on labor markets in the U.S., and I believe that AI will undoubtedly change the future of U.S. labor--but Yang is also exaggerating the impact AI will have on the workforce.

The Viewpoint column "Online Voting: We Can Do It! (We Have To)" in the September 2019 issue is naïve and unscientific. Although the column is explicitly framed as a response to the scientific community of experts who explain the dangers of Internet voting, it does not actually cite any of the scientific literature Ms. Orman is claiming to refute. The scientific community (the "9 out of 10 experts" she mentions) have published many articles and reports laying out the scientific basis for why online voting is inherently insecure (given any known or imminently foreseeable technology).1,2,3,4,5,6,7 Yet Ms. Orman does not cite any of these scientific papers among the bibliographic citations in the References section of her column. Given that Communication's Viewpoint format does not permit an extensive bibliography, she did not have room to cite all of references listed here,1,2,3,4,5,6,7 but in a response to the scientific community it would have been appropriate to cite (and explicitly respond to the science in) at least some of them.

In April 2019, following the Easter Sunday bomb attacks, the Government of Sri Lanka had to shut down Facebook and YouTube for nine days to stop the spreading of hate speech and false news, posted mainly in the local languages Sinhala and Tamil. This came about simply because these social media platforms did not have the capability to detect and warn about the provocative content. India's Ministry of Human Resource Development (MHRD) wants lectures on Swayama and NPTELb--the online teaching platforms--to be translated into all Indian languages. Approximately 2.5 million students use the Swayam lectures on computer science alone. The lectures are in English, which students find difficult to understand.

What would you do if you had the super-power to accurately answer, in a few milliseconds, a multiple-choice question with a billion choices? Would you design the next generation of Web search engines, which could predict which of the billions of documents might be relevant to a given query? Would you build the next generation of retail recommender systems that have things delivered to your doorstep just as you need them? Or would you try and predict the next word about to be uttered by U.S. President Donald Trump? The objective in extreme classification, a new research area in machine learning, is to develop algorithms with such capabilities.