The success of IT Sligo in securing competitive funding under the Erasmus Programme will not only lead to enhanced knowledge and expertise in an area of critical need but is a tangible example of how higher education benefits citizens. The approach of the Institute also demonstrates its willingness to draw from and contribute to the knowledge base in Europe with partners from five other EU member states as well as NUI Galway involved in the project. International collaborations of this type are essential to ensure that the Irish higher education sector maintains and enhances its capacity to deliver education and training relevant to our needs.

Collaboration will enable the way forward as the insurance sector adopts and deploys new technologies, according to two company CEOs who stressed partnerships Wednesday at the InsureTech Connect Conference in Las Vegas. "Regardless of the challenge, I believe there's a better way," said Tony Kuczinski, president and CEO of Munich Reinsurance America Inc. in Princeton, New Jersey. "I believe that by joining forces, we have a better chance a leveraging new ways of thinking, new technology and data, and other areas." "We have more partnerships now than at any time in our history," said Dan Glaser, president CEO of Marsh & McLennan Cos. Inc. Munich Re's own effort to accelerate the claims process in the wake of a catastrophe event involved pulling in expertise from within and outside the company, and even included a seasoned claims adjuster using his field experience to help inform an artificial intelligence system, according to Mr. Kuczinski. Mr. Glaser also said progress was a matter of priorities and focus.

Helsinki University is offering a free online Artificial Intelligence course in English to anyone, anywhere. This free Artificial Intelligence (AI) online course is made for non-technical people so no special knowledge or skills are needed to take the course. AI is embedded in so many part of our lives - this course is meant to dispel any mystery around the technology that AI uses, the impact it has on our lives and how AI will develop in the coming years. Do you wonder what AI really means? Are you thinking about the kind of impact AI might have on your job or life?

The Bank of England and the World Bank Chief are only two voices, in a growing network, that are concerned by the rise of AI and the loss of jobs. This continuing question is not surprising, according to John Gikopoulos, Global Head for automation and artificial intelligence AI at Infosys Consulting: fear and uncertainty make for good headlines. "The short answer is that no-one can accurately predict what tomorrow's job market will look like, any more than the first observers of Stephenson's Rocket could imagine the huge numbers of people employed by the global, multi-billion dollar railway industry of today," explains Gikopoulos. "We prefer to take our cue from other industrial revolutions to which we've somehow managed to adapt. And the most likely outcome of AI will be a positive shift in the nature of the work we do."

Learning from data has led to a paradigm shift in computational materials science. In particular, it has been shown that neural networks can learn the potential energy surface and interatomic forces through examples, thus bypassing the computationally expensive density functional theory calculations. Combining many-body techniques with a deep learning approach, we demonstrate that a fully-connected neural network is able to learn the complex collective behavior of electrons in strongly correlated systems. Specifically, we consider the Anderson-Hubbard (AH) model, which is a canonical system for studying the interplay between electron correlation and strong localization. The ground states of the AH model on a square lattice are obtained using the real-space Gutzwiller method. The obtained solutions are used to train a multi-task multi-layer neural network, which subsequently can accurately predict quantities such as the local probability of double occupation and the quasiparticle weight, given the disorder potential in the neighborhood as the input.

This paper discusses predictive inference and feature selection for generalized linear models with scarce but high-dimensional data. We argue that in many cases one can benefit from a decision theoretically justified two-stage approach: first, construct a possibly non-sparse model that predicts well, and then find a minimal subset of features that characterize the predictions. The model built in the first step is referred to as the \emph{reference model} and the operation during the latter step as predictive \emph{projection}. The key characteristic of this approach is that it finds an excellent tradeoff between sparsity and predictive accuracy, and the gain comes from utilizing all available information including prior and that coming from the left out features. We review several methods that follow this principle and provide novel methodological contributions. We present a new projection technique that unifies two existing techniques and is both accurate and fast to compute. We also propose a way of evaluating the feature selection process using fast leave-one-out cross-validation that allows for easy and intuitive model size selection. Furthermore, we prove a theorem that helps to understand the conditions under which the projective approach could be beneficial. The benefits are illustrated via several simulated and real world examples.

Technology has shaped the way on which we compose and produce music: Notably, the invention of microphones, magnetic tapes, amplifiers and computers pushed the development of new music styles in the 20th century. In fact, several artistic domains have been benefiting from such technology developments; for instance, Experimental music, nonlinear music, Electroacoustic music, and interactive music. Experimental music is composed in such a way that its outcome is often unforeseeable; for instance, it may contain random generated tones, computer-generated content, variable-duration notes and "free" content. It may also include atonal melodies and microtones. Another domain is nonlinear music, in which the scenario is divided in parts whose order can be chosen at execution time. We will use the term "nonlinear" music in that sense. Nonlinear music exists from many centuries ago; for instance, Mozart's minuets in which the order of work's musical material was determined by coin-tosses. Electroacoustic music was originated by the incorporation of electronic sound production into compositional practice.

The results also provide further evidence that embeddings have a major impact on the performance of such architectures.

These numbers help explain why so many organizations are turning to virtual assistants and other AI tools to close the gap. One third of companies in the U.S. and Europe have introduced AI technologies in customer service, according to our survey. We found that 74% of those organizations are now able to provide always‑on customer support. Additional benefits include freeing up the time of human agents and processing high‑volume, repetitive tasks more efficiently. Juniper Research estimates that AI‑powered chatbots will save companies $8 billion annually by 2022, which helps explain why the chatbot market is growing by 25% a year.

On the face of it, the connection between the internet of things (IoT) and animals is not an obvious one. However, a number of trials and larger-scale implementations of IoT use with household pets and on farms are showing that connecting Fido and Daisy to sensors could provide real benefits. This should not be surprising: animals are big business, both in terms of personal ownership and commercial farming. Mintel, a market research company, expects UK pet insurance premiums to grow from £976m in 2015 to £1.6bn by 2021. Anything that makes it easier and more efficient to care for and farm animals is therefore likely to be of interest.