Disruptive technology has had a profound impact on academic institutions. STEM subjects have benefitted hugely from new tech, but technological disruption has also changed the way that social studies and economics is taught. As well as working alongside companies, universities (as well as colleges and schools) train students to navigate the changing world. For example, Ohio State University and Carnegie Mellon University have both opened classes in smart city technology. Academia is a key resource for tech firms, providing hubs of innovation with the knowledge and talent to complete successful projects.

China, the most populous country in the world, has close to one billion people that speak Mandarin. Spanish is spoken by a less than half that number, primarily in Mexico, Spain and the countries in South America. English follows close behind, with Hindi in India and Arabic in the Middle East making up the top five. Or so you would think. The most common language in the world is actually not human at all.

Advances in Artificial Intelligence (AI) technology will enable simpler user interfaces, which will help banks create a more human-like customer experience. The technology has become efficient in gaining extensive data analytics and customer insights, which will help banks to create a more personalised customer experience, according to Aspect Software. Four in five bankers believe AI will "revolutionise" the way in which banks gather information as well as how they interact with their clients, said the Accenture Banking Technology Vision 2017 report as customers are looking for a fast, efficient solution to their queries. The new report revealed that AI will become the primary way banks interact with their customers within the next three years, according to three quarters of bankers surveyed and also found that, while the number of human interactions in bank branches or over the phone was falling and would continue to do so, the quality and importance of human contact would increase. Stephen Ball, SVP Sales, Europe and Africa at Aspect Software, suggests that as the banking world continues to change with the adoption of new technologies, and a growing number of'challenger banks' and fintech providers emerging to shake up the established order, traditional banks are responding by improving their customers' experience.

Material scientists from Duke University in Durham, North Carolina and Trinity College, Dublin, have developed a new computational method to quickly predict new magnetic materials. Putting the method to test, they also managed to create two new magnetic materials, pieced together atom-by-atom. Magnets are commonplace in a large number of everyday items. Electronics like computers use them for data storage and for displays, and speakers have them on the inside as well. They are used in the healthcare industry, in machines for MRI and X-ray scans.

Ask an economist or a technology expert and they will happily tell you that decades of data reliably show automation has created more jobs than it has destroyed. Far fewer of us now work on farms, for example, thanks to super-efficient machines that do the bulk of the work. Such technology has boosted productivity and, with it, living standards. As a result, more people work in leisure industries such as hospitality or hairdressing, serving all those people with higher disposable incomes and more free time. And were the pattern to continue, one could envisage the realisation at last of the prediction made by John Maynard Keynes in 1930 that the working week would eventually be cut, perhaps to just 15 hours.

A leading thinktank has urged the government to spend billions of pounds helping poorly skilled workers in the less prosperous parts of the UK cope with the threat of the looming robot revolution. The left-leaning Institute for Public Policy Research (IPPR) said in a new report that those most at risk from automation were concentrated in low-skill sectors of the economy and were least able to adapt to change. More than 10m jobs in the UK – a third of the total – are thought to be at risk from automation within the next two decades and the IPPR said the scale of the challenge required urgent action. There was also evidence to suggest that the impact of automation would be geographically concentrated and so widen the north-south divide. The IPPR research said that in four sectors alone – retail, hospitality, transport and manufacturing – 5m jobs were at risk, adding that a particular concern to ministers should be industries ripe for automation with a high proportion of workers least able to adapt.

Every few months it seems another study warns that a big slice of the workforce is about to lose their jobs because of artificial intelligence. Four years ago, an Oxford University study predicted 47% of jobs could be automated by 2033. Even the near-term outlook has been quite negative: A 2016 report by the Organization for Economic Cooperation and Development (OECD) said 9% of jobs in the 21 countries that make up its membership could be automated. And in January 2017, McKinsey's research arm estimated AI-driven job losses at 5%. My own firm released a survey recently of 835 large companies (with an average revenue of $20 billion) that predicts a net job loss of between 4% and 7% in key business functions by the year 2020 due to AI. Yet our research also found that, in the shorter term, these fears may be overblown.

The first generation of police body cameras was introduced in 2005 in Great Britain. Their primary task was, and still is, to record police interactions with the public as well as to gather evidence at crime scenes. To separate the irrelevant data from incriminating evidence, every recording needs to be reviewed and edited. Now imagine sifting through hundreds of hours of police footage, looking for a specific piece of information, such as a verbal exchange between an officer and a suspect, a clear shot of license plates, or a suspect entering a particular building or meeting a particular person. It's a rather time-consuming task, so a lot of this footage ends up archived to be reviewed later, forfeiting potentially valuable or time-sensitive evidence. The solution could be the use of artificial intelligence.

The idea that our ability to reflect has been outsourced to algorithms may seem hyperbolic. We assume we have agency regarding the choices we make, influenced by the paradigm of personalization but not subsumed within a Matrix of someone else's making. But how do you know? Have you created a list of activities you'd never delegate? Could you even discern where your moral boundaries end and codified biases begin? While welcoming the feedback that sensors, data and Artificial Intelligence provide, we're at a critical inflection point.

Monte Carlo (MC) sampling methods are widely applied in Bayesian inference, system simulation and optimization problems. The Markov Chain Monte Carlo (MCMC) algorithms are a well-known class of MC methods which generate a Markov chain with the desired invariant distribution. In this document, we focus on the Metropolis-Hastings (MH) sampler, which can be considered as the atom of the MCMC techniques, introducing the basic notions and different properties. We describe in details all the elements involved in the MH algorithm and the most relevant variants. Several improvements and recent extensions proposed in the literature are also briefly discussed, providing a quick but exhaustive overview of the current Metropolis-based sampling's world.