Inspired by widely-used techniques of causal modelling in risk, failure, and accident analysis, this work discusses a compositional framework for risk modelling. Risk models capture fragments of the space of risky events likely to occur when operating a machine in a given environment. Moreover, one can build such models into machines such as autonomous robots, to equip them with the ability of risk-aware perception, monitoring, decision making, and control. With the notion of a risk factor as the modelling primitive, the framework provides several means to construct and shape risk models. Relational and algebraic properties are investigated and proofs support the validity and consistency of these properties over the corresponding models. Several examples throughout the discussion illustrate the applicability of the concepts. Overall, this work focuses on the qualitative treatment of risk with the outlook of transferring these results to probabilistic refinements of the discussed framework.

Communication structure plays a key role in the learning capability of decentralized systems. Structural self-adaptation, by means of self-organization, changes the order as well as the input information of the agents' collective decision-making. This paper studies the role of agents' repositioning on the same communication structure, i.e. a tree, as the means to expand the learning capacity in complex combinatorial optimization problems, for instance, load-balancing power demand to prevent blackouts or efficient utilization of bike sharing stations. The optimality of structural self-adaptations is rigorously studied by constructing a novel large-scale benchmark that consists of 4000 agents with synthetic and real-world data performing 4 million structural self-adaptations during which almost 320 billion learning messages are exchanged. Based on this benchmark dataset, 124 deterministic structural criteria, applied as learning meta-features, are systematically evaluated as well as two online structural self-adaptation strategies designed to expand learning capacity. Experimental evaluation identifies metrics that capture agents with influential information and their optimal positioning. Significant gain in learning performance is observed for the two strategies especially under low-performing initialization. Strikingly, the strategy that triggers structural self-adaptation in a more exploratory fashion is the most cost-effective.

KIEV - Ukraine's election has catapulted Volodymyr Zelenskiy, a 41-year-old stand-up comedian and television star with no political experience, into the nation's top job. As leader of a country dependent on international aid and battling separatists, Zelenskiy will have to deal with Russian President Vladimir Putin, deep economic problems and possibly rebellious elites. Here is a look at the main challenges facing Ukraine's sixth president: Voters expect the new commander-in-chief to end a five-year war with Moscow-backed separatists in the industrial east. The conflict has claimed some 13,000 lives since 2014 and is a huge burden on the economy and society. Despite numerous attempts to staunch the bloodletting, the conflict regularly claims the lives of soldiers and civilians, and a solution is nowhere in sight.

Earlier this month the University of Nottingham published a study in PloSOne about a new artificial intelligence model that uses machine learning to predict the risk of premature death, using banked health data (on age and lifestyle factors) from Brits aged 40 to 69. This study comes months after a joint study between UC San Francisco, Stanford, and Google, which reported results of machine-learning-based data mining of electronic health records to assess the likelihood that a patient would die in hospital. One goal of both studies was to assess how this information might help clinicians decide which patients might most benefit from intervention. Amitha Kalaichandran, M.H.S., M.D., is a resident physician based in Ottawa, Canada. Follow her on Twitter at @DrAmithaMD.

On the floor of the New York Auto Show this week, Genesis showed off its sweet little Mint concept, an electric two-seater with a very abbreviated sedan body. The Hyundai luxury arm does not, however, have any plans to put the adorable thing into production--perhaps because, as we learned this week, getting world-changing tech into the market takes a fair amount of elbow grease. Elon Musk's Boring Company is slowly making its way through the necessary paperwork to make its DC to Baltimore Loop concept a real, live thing. Uber is rounding up the oodles of cash it needs to develop self-driving vehicles. "Flying taxi" engineers are trying to get their concepts past now-nervous aviation regulators.

Scientists are constantly figuring out how to expand the field of use of this incredible invention, which enables computer software to progressively improve its actions by adopting knowledge gained from previous experience. Machine learning, also referred to as artificial intelligence due to its ability to perform tasks using its own judgment, has been the subject of both praise and controversy. However, the sophisticated algorithms that have served in providing you ads on social networks might have a grand future in philology, archaeology, and linguistics. According to Émilie Pagé-Perron, a Ph.D. candidate in Assyriology at the University of Toronto, we might be closer than we thought to deciphering numerous Middle-Eastern cuneiform tablets written in Sumerian and Akkadian languages, all of which are several thousand years old. Pagé-Perron is in charge of the project officially titled Machine Translation and Automated Analysis of Cuneiform Languages, which currently operates in Frankfurt, Toronto, and Los Angeles, using combined efforts to create a program capable of translating the clay tablets.

The current approach to AI and machine learning is great for big companies that can afford to hire data scientists. But questions remain as to how smaller companies, which often lack the hiring budgets to bring in high-priced data scientists, can tap into the potential of AI. One potential solution may lie in doing machine learning on edge devices. Gadi Singer, vice president of the Artificial Intelligence Products Group and general manager of architecture at Intel, said in an interview at the O'Reilly AI Conference in New York that even one or two data scientists are enough to manage AI integration at most enterprises. But will the labor force supply adequate amounts of trained data scientists to cover all enterprises' AI ambitions?

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Verv, the Google-mentored energy tech startup behind the smart energy hub and green electricity sharing platform, recently announced that it has raised over £6.5 million (€7.5 million) in its Series A round led by environmental fund Earthworm. Earthworm has invested £5 million in Verv's pioneering IoT and renewable energy trading technology that could drive down household electricity bills and carbon emissions by over 20%. Other investors in the round include European innovation engine for sustainable energy, InnoEnergy, Crowdcube and international energy and services company, Centrica. Earthworm's investment is an important backing of Verv's vision to make millions of homes more green with a global network of smart hubs that offer a real-time breakdown of key appliance use and spend, as well as enable the trading of domestic renewable energy between communities. At Earthworm we are driven by sustainability and Verv represents a brilliant example of'enabling' technology.

The fear of robots coming for your job is one of the many challenges confronting 21st-century workers, but the machines aren't ready to take on every industry just yet. Bridgewater Associates, the massive hedge fund founded by legendary investor Ray Dalio, just released a report on the changing relationship between labour and capital in the US. One of the big factors the Bridgewater authors highlighted was the ongoing rise in automation across industries, which they noted could be a support for corporate profits in the years to come as more efficient robots and software potentially replace slower and error-prone human labour. Bridgewater cited a 2016 report from consulting firm McKinsey & Company that looked at which industries in the US were most susceptible to being automated. The McKinsey report used data from the Department of Labour to estimate how much time workers in various industry sectors spent doing different types of tasks, and which of those tasks could, theoretically, be automated using present technology.