The proliferation of data within the military poses a significant challenge for operators interpreting differing data sets into meaningful information upon which to make informed and timely decisions. Artificial Intelligence capabilities are developing at pace and can present opportunities through Deep Learning for operators and decision makers to interpret vast, disparate data sets concurrently. The Defence and Security Accelerator (DASA) has funded two projects led by Montvieux, in excess of £500,000, following a DASA themed competition to find new technologies, processes and ideas to'Revolutionise the human information relationship for Defence'. The Prediction Toolset is a Deep Learning based Artificial Intelligence capability that uses current and historical information to predict the change of control on the ground, in both space and time, between opposing groups fighting within an operational theatre. This capability provides foresight to analysts and collection managers, enabling them to proactively anticipate future events on the ground, thereby enhancing the protection of forces and improving the efficiency and effectiveness of information collection.

Many believed an algorithm would transcend humanity with cognitive awareness. Machines would discern and learn tasks without human intervention and replace workers in droves. They quite literally would be able to "think". Many people even raised the question whether we could have robots for spouses. But I am not talking about today.

A Twitter bug meant that private tweets were made public, the site has admitted. Android users who had kept their private for more than four years were vulnerable to the bug, which would have exposed their posts despite them having chosen for them not to be public. The company allows users to protect tweets, hiding them from public view so that only approved people can follow and read posts from an account. Twitter users often protect their tweets because allowing anyone to read them might endanger them or cause other problems. Those using Twitter for Android may have been affected by the bug if they made changes to their account's settings, such as changing the email address they use on their account, Twitter said.

NASA's next flagship telescope is the James Webb spacecraft, but the long-term direction of NASA's research remains uncertain. America's space agency has now turned to a team of expert astronomers to choose its eventual successor which will be built and sent into space in the 2030s. Four vastly different designs have been put forward which are designed to look for alien life, distant Earth-like worlds, black holes and the birth of new galaxies and high-energy gas disks. All four of the proposed missions look vastly different and the momentous decision will likely sculpt NASA's research for decades to come. Analysis of The Great Observatory programme in the 1970s gave the scientific community, and the wider world at large, access to analysis of the entire spectrum of electromagnetic light from Gamma rays to infrared radiation. LUVOIR will continue a mission similar to that which has been covered over the last two decades by Hubble and will study the first stars of the universe to find signs of life and the creation of worlds.

Just about everyone in the cybersecurity field has accepted the notion that adversaries are just as smart as we are. Anytime we celebrate the latest threat detection and prevention breakthrough, we're well aware that the bad guys are at work devising ways to evade or disrupt it. Artificial intelligence (AI) in its various permutations--from traditional machine learning (ML) to deep learning (DL)--is no exception. As it evolves and grows, we fully expect that adversaries will ramp up their knowledge and ability to exploit tools that use AI. What does this mean for enterprise security teams and cybersecurity vendors?

This story was originally published by ProPublica. At the FBI Laboratory in Quantico, Virginia, a team of about a half-dozen technicians analyzes pictures down to their pixels, trying to determine if the faces, hands, clothes or cars of suspects match images collected by investigators from cameras at crime scenes. The unit specializes in visual evidence and facial identification, and its examiners can aid investigations by making images sharper, revealing key details in a crime or ruling out potential suspects. But the work of image examiners has never had a strong scientific foundation, and the FBI's endorsement of the unit's findings as trial evidence troubles many experts and raises anew questions about the role of the FBI Laboratory as a standard-setter in forensic science. FBI examiners have tied defendants to crime pictures in thousands of cases over the past half-century using unproven techniques, at times giving jurors baseless statistics to say the risk of error was vanishingly small. Much of the legal foundation for the unit's work is rooted in a 22-year-old comparison of bluejeans. Studies on several photo comparison techniques, conducted over the last decade by the FBI and outside scientists, have found they are not reliable. Since those studies were published, there's no indication that lab officials have checked past casework for errors or inaccurate testimony. Image examiners continue to use disputed methods in an array of cases to bolster prosecutions against people accused of robberies, murder, sex crimes and terrorism. The work of image examiners is a type of pattern analysis, a category of forensic science that has repeatedly led to misidentifications at the FBI and other crime laboratories. Before the discovery of DNA identification methods in the 1980s, most of the bureau's lab worked in pattern matching, which involves comparing features from items of evidence to the suspect's body and belongings. Examiners had long testified in court that they could determine what fingertip left a print, what gun fired a bullet, which scalp grew a hair "to the exclusion of all others." Research and exonerations by DNA analysis have repeatedly disproved these claims, and the U.S. Department of Justice no longer allows technicians and scientists from the FBI and other agencies to make such unequivocal statements, according to new testimony guidelines released last year. Though image examiners rely on similarly flawed methods, they have continued to testify to and defend their exactitude, according to a review of court records and examiners' written reports and published articles.

The first European Ethical Charter on the use of artificial intelligence in judicial systems will be presented at the Council of Europe office in Brussels from 11am on Wednesday 23 January. The charter was adopted by the Council of Europe's European Commission for the Efficiency of Justice, known as CEPEJ, in December 2018. It sets out 5 key principles to help policymakers, legal professionals and private sector companies make sure that the use of artificial intelligence in judicial systems and related fields complies with international standards on human rights, privacy and data protection. The charter is accompanied by an in-depth study on the existing use of artificial intelligence in judicial systems, as well as recommendations on how artificial intelligence can best be used in this context and when its use should be considered with extreme caution. The charter will be presented by the Council of Europe's Director for Human Rights, Christophe Poirel, the Secretariat of CEPEJ (Stéphane Leyenberger and Clementina Barbaro), and CEPEJ member Merethe Eckhardt (Denmark). The presentation, which is strictly by invitation only, will be followed by a question and answer session with participants.

From the 9 to the 11 of January Barcelona has hosted the launch of AI4EU, an Artificial Intelligence project in European territory that aims to make available to users resources based on this technology that facilitate scientific research and innovation, analyse future research needs in Artificial Intelligence (AI) and create an observatory of ethics that ensures an AI focused on human being. CARTIF is one of the entities in charge of developing this initiative whose ultimate objective is to encourage all the countries of the European Union to invest resources and efforts in AI for the benefit of society. Framed in the Horizon 2020 program and with an investment by the European Commission of 20 million euros, AI4EU lasts 36 months and brings together 79 advanced research centers and large companies in the sector from 21 countries across the continent who have committed to carry out the following guidelines: • Build a sustainable Open AI On-Demand-Platform to make available expertise, knowledge, algorithms and tools to all user from all sectors. CARTIF will participate mainly in the elaboration of the EU Strategic Research Agenda for AI. Specifically CARTIF will collect, analyse and integrate road-mapping activities of important EU initiatives given its membership and active participation in BDVA, EFFRA and euRobotics.

All healthcare providers share the goal of treating more patients, cutting the cost of healthcare, and achieving better patient and business outcomes. With these goals in mind, many providers are now embracing solutions for artificial intelligence. AI promises to help healthcare providers deliver better outcomes by improving preventive medicine, enhancing diagnostics and enabling clinicians to treat more patients. With AI applications and systems, healthcare providers can easily sift through large amounts of data to identify infections sooner, predict which patients are likely to have certain problems and identify needs in large groups of people. At the same time, AI can help providers optimize the use of existing resources to improve productivity and contain costs.

The Pentagon's research wing is trying to reduce the amount of computing power and hardware needed to run advanced artificial intelligence tools, and it's turning to insects for inspiration. The Defense Advanced Research Projects Agency on Friday began soliciting ideas on how to build computing systems as small and efficient as the brains of "very small flying insects." The Microscale Biomimetic Robust Artificial Intelligence Networks program, or MicroBRAIN, could ultimately result in artificial intelligence systems that can be trained on less data and operated with less energy, according to the agency. Analyzing insects' brains, which allow them to navigate the world with minimal information, could also help researchers understand how to build AI systems capable of basic common sense reasoning. "Nature has forced on these small insects drastic miniaturization and energy efficiency, some having only a few hundred neurons in a compact form-factor, while maintaining basic functionality," officials wrote in the solicitation.