Artificial Intelligence, as a true change agent, is coming, and in many ways, its early rumblings are already being felt. It's clear that some people will eagerly adopt and integrate the new tools and ways of working it makes possible, while others will be more cautious or even oppose the changes it brings to their life or work. AI is poised to have a transformative effect on consumer, enterprise, and government markets around the world. While there are certainly obstacles to overcome, consumers believe that AI has the potential to assist in medical breakthroughs, democratise costly services, elevate poor customer service, and free up an overburdened workforce. Some tech optimists believe AI could create a world where human abilities are amplified as machines help mankind process, analyse, and evaluate the abundance of data that creates today's world, allowing humans to spend more time engaged in high-level thinking, creativity, and decision-making workforce.

Inventor Sir James Dyson is set to reveal plans to build electric cars in Britain today, it is understood. His hi-tech firm, famed for its vortex vacuum cleaner, hand-dryers and bladeless fans, has been recruiting talent from the car and technology industry. Today it is thought Dyson will unveil the early stages of an electric vehicle which could be built at its headquarters in Malmesbury, Wiltshire. Expectations about what Dyson was developing mounted after mainstream car makers accelerated their plans for electric vehicles. Mini revealed its own all-electric vehicle ahead of this month's Frankfurt Motor Show.

The Compunet Research Line at Istituto Italiano di Tecnologia (IIT) in Genoa is opening a postdoctoral position (2 years) with focus on bioinformatics and machine learning: in particular deep learning for genomics for disease/pharmaceutical predictions. Knowledge of computational drug discovery will be considered a plus. Knowledge of Caffe, Theano, TensorFlow or related deep learning GPU enabled/ machine learning software libraries is also a desirable skill. The salary will be internationally competitive and commensurate with the candidate's experience. Applicants are invited to submit a cover letter stating research goals, a curriculum vitae and contact information of 2 referees to panagiota.dimopoulou@iit.it

At the beginning of 2016, the world's most sophisticated artificial intelligence (AI) beat World Champion Lee Sedol at a game called'Go' – a chess-like board game with more move combinations than there are atoms in the universe. Before this defeat, Go had been considered too complicated for even the most complex computers to beat the top humans. It was a landmark moment in the development of ever-more sophisticated AI technology. But the future of AI holds more than simply board game victories. It is rapidly finding its way into all aspects of modern life, prompting the promise of a'Fourth Industrial Revolution'. One of the areas AI has huge potential is in our energy system.

The company announced it has received €30 million ($36 million) funding from the European Investment Bank, the lending arm of the European Union. "We're proud to receive this stamp of approval from the EIB. It's the type of offer you can't refuse and it will allow us to further accelerate our growth and continue to level the playing field for small businesses, giving them access to tools to take on the big corporations," said Jacob de Geer, CEO and co-founder of iZettle, in a statement. The funding follows the startup's most recent round, which was earlier this year, when it raised $63 million at the same $500 million valuation it had in its last equity round. It appears the valuation is staying the same with this latest round: the money, as with earlier this year, is coming in the form of debt funding and will be distributed over the next three years, the company said.

Worldwide spending on artificial intelligence and big data will reach the tens of billions by 2025. Michael Finnigan finds out what family-run operations need to know about the rise of the robots. The county of Wiltshire in the United Kingdom might seem like an unlikely setting for one of the world's most advanced artificial intelligence (AI) laboratories. It is best-known for its Neolithic monuments and iconic stone circles, most famously Stonehenge, but beneath its prehistoric landscape the future is unfolding. At family-run technology design firm Dyson, a team of engineers are using artificial intelligence to get a leg up on the competition.

This paper presents a new approach to non-parametric cluster analysis called Adaptive Weights Clustering (AWC). The idea is to identify the clustering structure by checking at different points and for different scales on departure from local homogeneity. The proposed procedure describes the clustering structure in terms of weights \( w_{ij} \) each of them measures the degree of local inhomogeneity for two neighbor local clusters using statistical tests of "no gap" between them. % The procedure starts from very local scale, then the parameter of locality grows by some factor at each step. The method is fully adaptive and does not require to specify the number of clusters or their structure. The clustering results are not sensitive to noise and outliers, the procedure is able to recover different clusters with sharp edges or manifold structure. The method is scalable and computationally feasible. An intensive numerical study shows a state-of-the-art performance of the method in various artificial examples and applications to text data. Our theoretical study states optimal sensitivity of AWC to local inhomogeneity.

Telling cause from effect from observational data is one of the fundamental problems in science [26], [18]. We consider the problem of inferring the most likely direction between two univariate numeric random variables X and Y. That is, we are interested in identifying whether X causes Y, whether Y causes X, or whether they are merely correlated. Traditional methods, that rely on conditional independence tests, cannot decide between the Markov equivalent classes of X Y and Y X [18]. Recently, it has been postulated however that if X Y, there exists an independence between the marginal distribution of the cause, P (X), and the conditional distribution of the effect given the cause, P (Y X) [25], [9]. The state of the art exploits this asymmetry in various ways, and overall obtain up to 70% accuracy on a well-known benchmark of cause-effect pairs [24], [8], [20], [10], [17]. In this paper we break this barrier, and give an elegant score that is computable in linear-time and obtains over 82% accuracy on the same benchmark.

The availability of massive data about sports activities offers nowadays the opportunity to quantify the relation between performance and success. In this study, we analyze more than 6,000 games and 10 million events in six European leagues and investigate this relation in soccer competitions. We discover that a team's position in a competition's final ranking is significantly related to its typical performance, as described by a set of technical features extracted from the soccer data. Moreover we find that, while victory and defeats can be explained by the team's performance during a game, it is difficult to detect draws by using a machine learning approach. We then simulate the outcomes of an entire season of each league only relying on technical data, i.e. excluding the goals scored, exploiting a machine learning model trained on data from past seasons. The simulation produces a team ranking (the PC ranking) which is close to the actual ranking, suggesting that a complex systems' view on soccer has the potential of revealing hidden patterns regarding the relation between performance and success.

Refactoring6 is something software developers like to do. But do they refactor as much as they would like? Are there barriers that prevent them from doing so? Refactoring is an important tool for improving quality. Many development methodologies rely on refactoring, especially for agile methodologies but also in more plan-driven organizations. If barriers exist, they would undermine the effectiveness of many product-development organizations. We conducted a large-scale survey in 2009 of 3,785 practitioners' use of object-oriented concepts,7 including questions as to whether they would refactor to deal with certain design problems. We expected either that practitioners would tell us our choice of design principles was inappropriate for basing a refactoring decision or that refactoring is the right decision to take when designs were believed to have quality problems. However, we were told the decision of whether or not to refactor was due to non-design considerations. It is now eight years since the survey, but little has changed in integrated development environment (IDE) support for refactoring, and what has changed has done little to address the barriers we identified.