A new machine learning tool that can detect whether emerging strains of the bacterium, Salmonella are more likely to cause dangerous bloodstream infections rather than food poisoning has been developed. The tool, created by a scientist at the Wellcome Sanger Institute and her collaborators at the University of Otago, New Zealand and the Helmholtz Institute for RNA-based Infection Research, a site of the Helmholtz Centre for Infection Research, Germany, greatly speeds up the process for identifying the genetic changes underlying new invasive types of Salmonella that are of public health concern. Reported today (8 May) in PLOS Genetics, the machine learning tool could be useful for flagging dangerous bacteria before they cause an outbreak, from hospital wards to a global scale. As the cost of genomic sequencing falls, scientists around the world are using genetics to better understand the bacteria causing infections, how diseases spread, how bacteria gain resistance to drugs, and which strains of bacteria may cause outbreaks. However, current methods to identify the genetic adaptations in emerging strains of bacteria behind an outbreak are time-consuming and often involve manually comparing the new strain to an older reference collection.

A report from the House of Lords recommends that ethics be a key focus in the future of artificial intelligence, saying it should be developed "for the common good and benefit of humanity" and never given "autonomous power to hurt, destroy or deceive" people. AI in the UK: Ready, Willing and Able highlights the UK's opportunity to become a world-leading nation in the development of artificial intelligence but insists that clear-cut ethics must be established around the development and deployment of such technology. The report claims that, as a world leader, the UK can "help shape the ethical development of artificial intelligence" on a global scale; thus helping to set the fundamental guidelines for all future AI development. The report also addresses other key issues surrounding the development of AI – the potential effect on workers and data privacy rights. For some time now the issue of job losses due to the introduction of AI and robotics has been discussed, with conflicting claims of job losses and exaggeration of figures.

By dividing the world into 3m x 3m squares, each with a unique 3-word address, what3words enables the most precise reference to any location around the globe. In contrast to GPS coordinates, 3-word addresses are easy to remember and, more importantly, they are designed for explicit and error-free voice input in more than 20 languages. Currently used by various businesses, governments, and individuals, this technology is now integrating with the automotive navigation system included in the all-new Mercedes-Benz User Experience (MBUX), featured in the new A-Class. At our Nuance Auto Forums in Detroit and Europe, attendees had the opportunity to experience the 3-word address system and to hear the what3word story and vision presented by Gigi Etienne, what3words Partnerships Manager, and Ashley Cashion, Head of Automotive and Mobility at what3words, personally. For those who could not attend, I had the good fortune to talk to these innovators about what3words and their vision on voice input in vehicles. What was the motivation for developing the 3-words address system?

Denmark generated 74% of its energy from renewable sources in 2017, making it a world leader in renewable power. But it's not the country's major energy providers that are driving this move away from fossil fuels. Rather, it is an increase in so-called "energy citizens" -- people that are decentralising the energy mix and installing smaller, renewable power projects. This is encouraged by government policies such as granting permits to wind projects only if the developers are at least 20 percent owned by local communities. It's not just Denmark -- across much of Europe energy citizens are playing an ever-more important role.

Thousands of attendees of the 2017 Champions League final in Cardiff, Wales were mistakenly identified as potential criminals by facial recognition technology used by local law enforcement. According to the Guardian, the South Wales police scanned the crowd of more than 170,000 people who traveled to the nation's capital for the soccer match between Real Madrid and Juventus. The cameras identified 2,470 people as criminals. Having that many potential lawbreakers in attendance might make sense if the event was, say, a convict convention, but seems pretty high for a soccer match. As it turned out, the cameras were a little overly-aggressive in trying to spot some bad guys.

LONDON – For a quarter century, the U.S. and its allies owned the skies, fighting wars secure in the knowledge that no opponent could compete in the air. Rapid technological progress in China's aerospace industry, particularly air-to-air missile systems fired from an aircraft, is changing the game for Western air forces and the global arms trade. Russia took the lead in modernizing its air force, and has been more willing to use it. In the longer term, however, China's roughly $13 trillion economy and growing wealth mean it is likely to pose the greater strategic challenge for the U.S. and its allies. In 2017, Chinese defense spending rose by 5.6 percent in constant U.S. dollar terms, while Russia's fell by 20 percent, according to the Stockholm International Peace Research Institute.

The European Aviation Safety Agency (EASA) is coming up with a law that'll require hobbyist and commercial drone pilots to acquire license before taking the drone flights. Plus, this new law will come in effect in 2019. The pilots have to sit for internet-based course and on the successful completion of the course will be provided with drone license. According to Bundesamt fur Zivilluftfahrt (BAZL), the Swiss Federal Office of Civil Aviation, Switzerland and EU will apply these laws. "You ought to learn, where and how high you are allowed to fly your drone or what influence temperature and wind can have," stated BAZL communications Chief Urs Holderegger.

The founder of the Yorkshire-based video games company behind hits such as Worms is set for a £50m windfall from a stockmarket listing valuing the business at up to £230m. Debbie Bestwick, 48, founded Team17 in 1990 and her stake will be worth approximately £100m when the company floats on Aim this month. Bestwick, who received an MBE in 2016 for services to the video games industry, intends to sell up to 50% of her shareholding in the flotation. Bestwick has positioned the Wakefield-based business as a champion of independent games developers. In the "about us" section of Team17's website it describes itself as "a games label created by independent developers for independent developers".

Despite cultural and religious exclusions, pork is the most widely eaten meat in the world, accounting for 36% of all meat consumed. Asia is the biggest consumer; China alone accounts for one quarter of global pig trade, and its middle class is expected to double in the next 15 years, increasing even more the market for pork. While trade will certainly be a consideration among top producing countries, such as China itself, the US, Brazil, Spain and Russia, there are challenges for the industry that could be reduced, or even eliminated, with the implementation of digital and other data driven technologies. Processors focus on employee safety and welfare priorities even while trying to improve meat production efficiencies. For producers and farmers disease mitigation, animal health and performance are top of mind.

We consider big spatial data, which is typically produced in scientific areas such as geological or seismic interpretation. The spatial data can be produced by observation (e.g. using sensors or soil instrument) or numerical simulation programs and correspond to points that represent a 3D soil cube area. However, errors in signal processing and modeling create some uncertainty, and thus a lack of accuracy in identifying geological or seismic phenomenons. Such uncertainty must be carefully analyzed. To analyze uncertainty, the main solution is to compute a Probability Density Function (PDF) of each point in the spatial cube area. However, computing PDFs on big spatial data can be very time consuming (from several hours to even months on a parallel computer). In this paper, we propose a new solution to efficiently compute such PDFs in parallel using Spark, with three methods: data grouping, machine learning prediction and sampling. We evaluate our solution by extensive experiments on different computer clusters using big data ranging from hundreds of GB to several TB. The experimental results show that our solution scales up very well and can reduce the execution time by a factor of 33 (in the order of seconds or minutes) compared with a baseline method.