NASA's Mars 2020 rover has successfully'passed its driving test' in a major mission milestone that saw it move under its own weight ahead of its launch next year. The rover will leave for Mars in July or August 2020 from the Cape Canaveral Air Force Station in Florida and will travel aboard the new Space Launch System rocket. NASA's robotic vehicle had to demonstrate it could move forwards, backwards and pirouette during the more than 10-hour marathon'driving test' on Tuesday. The next time the Mars 2020 rover drives, it will be rolling over Martian soil. The semi-autonomous vehicle will search for signs of ancient microbial life within the Jezero crater, which contains a dried up lake once filled with water.

EU presidency extends access to free AI course across EU Jan Petter Myklebust 13 December 2019 European Union employment ministers have endorsed a proposal from Finland's Presidency of the Council of the EU to provide European citizens with free access to a successful online course on basic artificial intelligence (AI), developed and run by the University of Helsinki in partnership with private firm Reaktor. To achieve this, the course on "Elements of AI" will be made available in all official EU languages. The Finnish government will fund the project with €1.7 million (US$1.9 million) from Finland's Ministry of Economic Affairs and Employment as part of the EU Council presidency's effort to democratise awareness of AI and develop people's skills for jobs of the future. At the launch of the initiative in Brussels on 10 December, Finland's Minister of Employment Timo Harakka said: "Our investment has three goals: we want to equip EU citizens with digital skills for the future; we wish to increase practical understanding of what artificial intelligence is; and by doing so, we want to give a boost to the digital leadership of Europe." "As our presidency ends, we want to offer something concrete. It's about one of the most pressing challenges facing Europe and Finland today: how to develop our digital literacy," Harakka said.

U.S. chipmaker Nvidia is set to win unconditional EU antitrust approval for its $6.8 billion acquisition of Mellanox Technologies, people familiar with the matter said on Wednesday. Nvidia, known for its powerful gaming graphics chips, is looking to boost its data center and artificial intelligence business via the takeover, its biggest deal, helping it to better compete with rival Intel . The European Commission, which is scheduled to decide on the deal by Dec. 19, declined to comment. Nvidia and Mellanox also declined to comment. U.S. authorities have already cleared the deal without conditions while approval is still pending in China where Mellanox has major customers such as Alibaba and Baidu .

Unless you're living under a rock, you've surely noticed the AI-hype-train ploughing through the media promising no less than transforming every industry. While there's certainly currently both exaggerated claims and inflated expectations when it comes to Artificial Intelligence, more than the general public may think is already in the realm of reality rather than fiction. In fact, most likely you've been knowingly or unknowingly using AI-based technology already more than once today by, for example, either using Face ID to authenticate on your iPhone, searching on Google, reading subtitles on YouTube or looking at recommended items on Amazon. And, as with many other technologies, as soon as it works it's not considered AI (technology) anymore. AI is also all over cybersecurity.

IDAHO FALLS, Idaho, Dec. 5, 2019 – A powerful new supercomputer arrived this week at Idaho National Laboratory's Collaborative Computing Center. The machine has the power to run complex modeling and simulation applications, which are essential to developing next-generation nuclear technologies. Named after a central Idaho mountain range, Sawtooth arrives in December and will be available to users early next year. That is the highest ranking reached by an INL supercomputer. Of 102 new systems added to the list in the past six months, only three were faster than Sawtooth.

Big data is around us. However, it is common to hear from a lot of data scientists and researchers doing analytics that they need more data. How is that possible, and where does this eagerness to get more data come from? Very often, data scientists need lots of data to train sophisticated machine-learning models. The same applies when using machine-learning algorithms for cybersecurity.

The year 2019 seemed to be the year of unpredictability, not the least of which was the seemingly ever-changing foreign trade policy of major world economies. Interestingly, it's that same unpredictable nature of foreign trade policy that serves as a springboard for supply chain predictions for 2020. Here are the top five predictions that will have a major impact on the world's global supply chains. Historically, digital transformation of the supply chain has taken place by targeting various functional silos within their own walls. This approach lacked the ability to evaluate the interconnected nature of supply chain decisions.

Most research on emotion analysis from text focuses on the task of emotion classification or emotion intensity regression. Fewer works address emotions as structured phenomena, which can be explained by the lack of relevant datasets and methods. We fill this gap by releasing a dataset of 5000 English news headlines annotated via crowdsourcing with their dominant emotions, emotion experiencers and textual cues, emotion causes and targets, as well as the reader's perception and emotion of the headline. We propose a multiphase annotation procedure which leads to high quality annotations on such a task via crowdsourcing. Finally, we develop a baseline for the task of automatic prediction of structures and discuss results. The corpus we release enables further research on emotion classification, emotion intensity prediction, emotion cause detection, and supports further qualitative studies.

Voting rules allow multiple agents to aggregate their preferences in order to reach joint decisions. A common flaw of some voting rules, known as the no-show paradox, is that agents may obtain a more preferred outcome by abstaining from an election. We study strategic abstention for set-valued voting rules based on Kelly's and Fishburn's preference extensions. Our contribution is twofold. First, we show that, whenever there are at least five alternatives and seven agents, every Pareto-optimal majoritarian voting rule suffers from the no-show paradox with respect to Fishburn's extension. This is achieved by reducing the statement to a finite - yet very large - problem, which is encoded as a formula in propositional logic and then shown to be unsatisfiable by a SAT solver. We also provide a human-readable proof which we extracted from a minimal unsatisfiable core of the formula. Secondly, we prove that every voting rule that satisfies two natural conditions cannot be manipulated by strategic abstention with respect to Kelly's extension and give examples of well-known Pareto-optimal majoritarian voting rules that meet these requirements.

Autonomous vehicles rely on machine learning to solve challenging tasks in perception and motion planning. However, automotive software safety standards have not fully evolved to address the challenges of machine learning safety such as interpretability, verification, and performance limitations. In this paper, we review and organize practical machine learning safety techniques that can complement engineering safety for machine learning based software in autonomous vehicles. Our organization maps safety strategies to state-of-the-art machine learning techniques in order to enhance dependability and safety of machine learning algorithms. We also discuss security limitations and user experience aspects of machine learning components in autonomous vehicles.