Emails are ubiquitous in a modern, globalised workforce. However, a well-crafted email can make the sender appear approachable and competent, while a poorly constructed one is less persuasive, and leaves recipients less willing to comply with the request. Alongside making requests and providing information, emails help us build rapport in the workplace and long-term business relationships. An internet search for "email etiquette" generates 433,000 results, while a search for books on email etiquette fetches 76 titles (on Amazon.com). However, the advice we get is often hazy, lacking justification, and may even be contradictory at times. Emoticons are displayed on the Touch Bar on a new Apple MacBook Pro laptop during a product launch event on Oct. 27, 2016, in Cupertino, California.

Despite the rise in digital banking, an advantage of branch banking continues to be the value of personal interaction. But, could the benefits of face-to-face interaction be incorporated into augmented reality (AR) and virtual reality (VR) solutions? The traditional bank branch network is in a massive state of transition. Offices are shrinking or closing altogether, with digital tools being introduced to support both transactional and advisory roles. But, while more than 8,000 U.S. bank branches have closed over the past decade, and as many as 90% of transactions taking place online, the number of bank employees has remained relatively stable.

A few years back, cloud computing transformed customer management, giving every small and medium business access to unified data and communication platforms without the need to make heavy investments in IT infrastructure and staff. This time around, the next revolution in the space is being driven by artificial intelligence algorithms that help businesses automate customer outreach and make optimal use of data. Beneath the surface of the roiling sea of data we're generating hide exceptional business and sales opportunities. But the problem is there's now more information available than limited human resources and legacy tools can handle. Fortunately, making sense of data, both structured and unstructured, is something that artificial intelligence is becoming increasingly proficient at.

Machines won't become intelligent unless they incorporate certain features of the human brain. Europe's massive €1 billion project has shifted focus from simulation to informatics By Megan Scudellari Large-scale brainlike systems are possible with existing technology--if we're willing to spend the money By Jennifer Hasler Researchers in this specialized field have hitched their wagon to deep learning's star By Lee Gomes Running algorithms that mimic a rat's navigation neurons, heavy machines will soon plumb Australia's underground mines By Jean Kumagai Artificial intelligence might endow some computers with self-awareness.

Summary: This is the second in our multi-part series on Quantum computing. How Fast?" we laid out the case that Quantum computing is commercially available today and that companies are already beginning to use it in operations. We talked a little about who is out in front (D-Wave, IBM) and who is coming soon (Microsoft, Google, University of New South Wales). We also spoke briefly about how it might be applied to deep learning and have an impact on artificial intelligence. In commercial operation today there are two distinct types of Quantum computers and several entirely different types due within a year or two.

Along with the recent advances in scalable Markov Chain Monte Carlo methods, sampling techniques that are based on Langevin diffusions have started receiving increasing attention. These so called Langevin Monte Carlo (LMC) methods are based on diffusions driven by a Brownian motion, which gives rise to Gaussian proposal distributions in the resulting algorithms. Even though these approaches have proven successful in many applications, their performance can be limited by the light-tailed nature of the Gaussian proposals. In this study, we extend classical LMC and develop a novel Fractional LMC (FLMC) framework that is based on a family of heavy-tailed distributions, called $\alpha$-stable L\'{e}vy distributions. As opposed to classical approaches, the proposed approach can possess large jumps while targeting the correct distribution, which would be beneficial for efficient exploration of the state space. We develop novel computational methods that can scale up to large-scale problems and we provide formal convergence analysis of the proposed scheme. Our experiments support our theory: FLMC can provide superior performance in multi-modal settings, improved convergence rates, and robustness to algorithm parameters.

So you think that robots are best suited for menial jobs? You couldn't be far from wrong. The research in artificial intelligence (AI) has moved to an all-new level with scientists at the University of Adelaide in Australia announcing that robots can predict mortality. By analysing CT scans from 48 patients, the deep learning algorithms could predict whether they would die within five years with 69% accuracy, which is broadly similar to the scores from human diagnosticians, the paper says. It will open up new avenues for the application of AI in medical image analysis, offering hope for early detection of serious illness that requires specific medical interventions.

A new tool that could allow us to easily identify fake online profiles has been developed. Researchers trained computer models to spot Pornhub users who made up information about themselves, commonly known as catfish. The system is designed to identify people who are dishonest about their age or gender by studying their writing style and how they comment on posts. It works with more than 90 per cent accuracy, and scientists believe it could help to ensure the safety of social networks. Researchers trained computer models to spot users of Pornhub who made up information about themselves, commonly known as catfish.

USA Today reporter Rachel Sandler asked people in San Francisco if apps are as popular as Apple says. Do people even care about apps anymore? Let's step back in time for a moment and imagine going back to using foldable maps or Web printouts for directions, hailing cabs (or even taking a bus!), and no social tools to share those daily self-portraits. Shazam is the most recent. Apple kicked off its big Worldwide Developers Conference in San Jose this week with a humorous video showing what that app-less world might be like.

Scientists, data scientists that is, from the University of Adelaide in Australia have announced that they have managed to build an Artificial Intelligence (AI) that can predict when people are going to die, and it's 70 percent accurate, but unlike the AI's I've talked about before that can predict how long people who have had heart attacks have left to live, more accurately than human doctors, this one is different – it can predict when you're going to die irrespective of the state of your current health because it uses deep learning to analyse a range of different scans, such as CT scans, to search for the signs, and assess the severity of, heart disease, cancer, and other diseases. For example, look at it this way – if I asked you how long you thought you were going to live, and you exercised every day, had the perfect diet and had no history of hereditary diseases in your family's lineage then it's likely you'd be able to tell me, with some degree of confidence, that your chances of living beyond eighty years old were good. On the other hand though, if you never exercised, had an awful diet of alcohol, lard and sugar, and your family had a history of hereditary diseases then you might tell me that the chances of you living beyond eighty could be slim. See, in one fell swoop you've assessed the state of your overall health, roughly assessed the risk factors in your head and calculated the rough odds of how long you think you have left to live. And that's what this AI is being trained to do, except for the fact that rather than relying on gut instinct it's analysing real time scans and correlating the patterns it's seeing against a huge dataset of patient information.