The UK's network of motorway services and petrol stations will be required to install chargers for electric cars, under plans announced by Transport Minister John Hayes. The Automated and Electric Vehicles Bill, which was first announced during the Queen's Speech in June but yesterday had its first reading in Parliament, outlines new powers that will help boost the uptake of electric vehicles across the UK. According to the government, the new network of charging stations will need to be "smart," which means they can interact with the grid in order to manage demand across the UK. Operators will also be required to provide clear information on the location and operating hours of their points, as well as the available charging options, how much they cost and whether they are working order or already in use. Ministers are clear that "all UK motorway services and large petrol retailers" will need to be on board and that the government will be given powers to "make it compulsory for chargepoints to be installed across the country."

The BBC wants to leverage machine learning to improve its online services and the programmes it commissions every year. Today, the broadcaster announced a five-year research partnership with eight universities from across the UK. Data scientists will help the best and brightest at the BBC set up the "Data Science Research Partnership," tasked with being "at the forefront of the machine learning in the media industry." It will tackle a range of projects not just with the BBC, but media and technology organisations from across Europe. The larger aim is to take the results, or learnings, and apply them directly to the BBC's operations in Britain.

Today, the company introduces the Blast and Megablast, the network-capable and Alexa-enabled siblings of UE's Boom 2 and Megaboom. This new line was built explicitly for the Amazon Alexa ecosystem of apps and features, and along with the Power Up charging base, offers always-on functionality to cater to your verbal demands. Unlike many other options though, this one also includes an on-board battery for portability. Fans of the Boom 2 and Megaboom don't have to worry, as those products will continue to exist (along with the tiny Wonderboom), but the new models eschew some Bluetooth features in exchange for a focus on WiFi connectivity. The main difference (aside from Alexa) is that the Blast and Megablast will only work on their own or in tandem over a WiFi network.

The emergence of big data has been transformational in many areas in science and engineering – biology, health sciences, material science, physics, and so on. At the heart of this transformation is statistical machine learning, subfield of computer science that aims at studying and developing algorithms that can analyze large volumes of data. The goal of this workshop is to bring together researchers, both from USA and Armenia, who work on machine learning (ML) and other scientific disciplines that are poised to benefit from the recent advances in ML. During the Soviet times, Armenia was one of the main hubs of cybernetics research in ex-Soviet Union, where centers such as the Mergelyan Institute (one of the three major producers of computer equipment in former USSR), and the Computing Center of the National Armenian Academy of Science (currently the Institute for Informatics and Automation Problems) conducted cutting edge research on topics ranging from robotics to algorithmic game theory to automated machine translation. Currently, Armenia has a small but vibrant research community in machine learning and data science, some members of which have participated in past and present DoD-sponsored research projects in collaboration with US colleagues.

The Global Service Robotics Market is a strategy report from Berg Insight analysing the latest developments on this market covering floor cleaning robots, robot lawn mowers, milking robots, humanoid robots, telepresence robots, powered human exoskeletons, surgical robots, AGVs, AMRs and UAVs. This strategic research report from Berg Insight provides you with 240 pages of unique business intelligence including 5-year industry forecasts and expert commentary on which to base your business decisions. The future is here: Service robotics will change our lives Robots are now being increasingly adopted for service applications, both by consumers and professionals. The service robot market comprises many different types of robots, most of which can be used for applications in multiple industries. At a consumer level service robots are commonly used for tedious and repetitive tasks such as domestic chores, or for leisure and entertainment purposes.

Ensembles of classifier models typically deliver superior performance and can outperform single classifier models given a dataset and classification task at hand. However, the gain in performance comes together with the lack in comprehensibility, posing a challenge to understand how each model affects the classification outputs and where the errors come from. We propose a tight visual integration of the data and the model space for exploring and combining classifier models. We introduce a workflow that builds upon the visual integration and enables the effective exploration of classification outputs and models. We then present a use case in which we start with an ensemble automatically selected by a standard ensemble selection algorithm, and show how we can manipulate models and alternative combinations.

This paper aims to analyse adolescents' road accidents in Tuscany. The analysis is based on the Database Edit of Osservatorio di Epidemiologia della Toscana. Complexity and heterogeneity of Edit's data represet an interesting scope to apply Machine Learning methods. In particular, in this paper is proposed an analysis based on a Bayesian probabilistic network, used to discover relationships between adolescents' characteristics and behaviours that are more often associated with an audacious driving style. The probabilistic network developed by this study can be considered a useful starting point for follow up reasearches, aiming to develop a causal network, a tool to limit this phenomenon.

This tutorial provides a gentle introduction to the particle Metropolis-Hastings (PMH) algorithm for parameter inference in nonlinear state-space models together with a software implementation in the statistical programming language R. We employ a step-by-step approach to develop an implementation of the PMH algorithm (and the particle filter within) together with the reader. This final implementation is also available as the package pmhtutorial in the CRAN repository. Throughout the tutorial, we provide some intuition as to how the algorithm operates and discuss some solutions to problems that might occur in practice. To illustrate the use of PMH, we consider parameter inference in a linear Gaussian state-space model with synthetic data and a nonlinear stochastic volatility model with real-world data.

Anticipation, scepticism and fear are holding more British citizens than Americans back from embracing artificial intelligence (AI) in the workplace, according to a new study by CITE Research for SugarCRM. The research on business executives in the US and UK revealed that the British are lagging behind when it comes to adopting artificial intelligence (AI) technologies into their work and personal lives. The survey also found that 47% of those in the UK are currently using technology powered by AI in the workplace, compared with 55% of Americans. The research further highlighted that when looking ahead, Brits are less open to embracing AI in the future – 69% of American respondents plan to deploy AI in the next two years, compared to 57% in the UK. Those in Britain were twice as likely not to ever want to use AI, with one in five respondents (20%) opposing the technology, compared with 1 in 10 Americans.

Though it probably doesn't feel this way to those who spend their lives running between meetings, dealing with customers, or negotiating with suppliers, the UK isn't working hard enough. UK productivity--how much all of us produce over a year divided by how many hours we spend doing it--lags France, Germany, and the U.S. by up to 30%, according to the Office of National Statistics. And it's not just the G7's most productive three countries that outperform the UK. Irish, Spanish, Belgian, and Dutch workers all significantly outperform their UK counterparts. We should be freeing people to focus on adding value, not restricting them to transactional tasks that are better and more quickly performed by automation.