Dr Chaand Nagpaul, chairman of the British Medical Association's GP committee, said the system could add to pressures on hospitals, rather than reduce them. "Owing to the lack of input from a trained professional, this simplistic system could, like NHS 111, result in more people being sent to overstretched GP or A&E services who don't actually need treatment - or conversely serious conditions being missed," he said. Peter Walsh, chief executive of safety campaign group Action Against Medical Accidents, questioned who would be liable if computer error caused patient harm or death. "The NHS 111 algorthym has already proved prone to error - that risk could be even greater under a system like this," he said, calling for robust evaluation of the six-month scheme. Katherine Murphy, from the Patients Association, said: "The stakes here are very high, I would be concerned about the risks to patient safety; this needs to be very carefully evaluated because of the risks of misdiagnosis."

We introduce a multivariate stochastic volatility model for asset returns that imposes no restrictions to the structure of the volatility matrix and treats all its elements as functions of latent stochastic processes. When the number of assets is prohibitively large, we propose a factor multivariate stochastic volatility model in which the variances and correlations of the factors evolve stochastically over time. Inference is achieved via a carefully designed feasible and scalable Markov chain Monte Carlo algorithm that combines two computationally important ingredients: it utilizes invariant to the prior Metropolis proposal densities for simultaneously updating all latent paths and has quadratic, rather than cubic, computational complexity when evaluating the multivariate normal densities required. We apply our modelling and computational methodology to $571$ stock daily returns of Euro STOXX index for data over a period of $10$ years. MATLAB software for this paper is available at http://www.aueb.gr/users/mtitsias/code/msv.zip.

The 9,500-year-old Jericho Skull contains a human cranium packed with soil and covered with plaster to replicate individual facial features. Sea shells are used to represent the deceased's eyes. Researchers have reverse-engineered the ancient ritual practice that created one of The British Museum's most important artifacts--the Jericho Skull--revealing the face of a man whose remains were decorated and venerated some 9,500 years ago. The Jericho Skull is also considered the oldest portrait in the museum's collection, and, until recently, its most enigmatic: a truncated human skull covered in worn plaster, with eye sockets set with simple sea shells that stare out blindly from its display case. A facial reconstruction based on the human remains found inside the Jericho Skull.

IN A former leatherworks just off Euston Road in London, a hopeful firm is starting up. BenevolentAI's main room is large and open-plan. In it, scientists and coders sit busily on benches, plying their various trades. The firm's star, though, has a private, temperature-controlled office. That star is a powerful computer that runs the software which sits at the heart of BenevolentAI's business.

Upside Energy and Heriot-Watt University have been awarded a Knowledge Transfer Partnership (KTP) grant by Innovate UK to maximise the opportunities presented by the emerging energy demand response market. The project will use machine learning, and distributed artificial intelligence methods to manage a portfolio of storage assets to provide real-time energy reserves to the grid. Upside Energy's Virtual Energy Store aims to relieve stress on the grid by managing a number of distributed storage resources, thereby reducing the UK's reliance on the spinning reserve capacity provided by traditional power stations. Upside has developed an Advanced Algorithmic Platform (AAP) which allows a substantial ensemble of algorithms that manage demand response of different devices to be run in parallel. Upside will work with Heriot-Watt University to optimise their existing selection of control algorithms and how they are utilised in different scenarios using the University's specialist skills in machine learning, artificial intelligence and stochastic optimisation.

In late December 2016, I announced a small gift of 10 Deep Learning books to people interested in or working in AI. This is my way of paying back to the community which has been extremely generous with ideas and code. I asked people to send me an email letting me know their interest in AI and their contributions to the community. Here is the video of the announcement. I received nearly 300 emails from people from around the world -- pretty much every continent other than Antarctica!

Companies like Apple, Google, Samsung, and Microsoft have all vied for control over the phones we carry in our pockets, the computers at our desks, and even the watches on our wrists. But over the past year a new arms race has emerged in Silicon Valley: competing to develop the most sophisticated virtual assistant. That's the annual Las Vegas tech confab where gadget makers, auto manufacturers, and app developers gather to flaunt their latest and, they hope, greatest. Although Amazon didn't officially attend the conference, Alexa showed up in car infotainment systems, new smartphones, robots, lamps, and even laundry machines. Ford, Whirlpool, Lenovo, GE, and Dish are a few of the brands announcing plans to add Amazon's voice-activated helper to their hardware.

I describe here the projects that I worked on, as well as career progress, starting 25 years ago as a PhD student in statistics, until today, and the transformation from statistician to data scientist that occurred slowly and started more than 20 years ago. This also illustrates many applications of data science, most are still active. My interest in mathematics started when I was 7 or 8, I remember being fascinated by the powers of 2 in primary school, and later purchasing cheap russian math books (Mir publisher) translated in French, for my entertainement. In high school, I participated in the mathematical olympiads, and did my own math research during math classes, rather than listening to the very boring lessons. When I attended college, I stopped showing up in the classroom altogether - afterall, you could just read the syllabus, memorize the material before the exam and regurgitate it at the exam.

Lately, tech companies have gone absolutely crazy for machine learning. They say it solves the problems only people could crack before. Machine learning is of special interest in IT security, where the threat landscape is rapidly shifting and we need to come up with adequate solutions. Some go as far as calling machine learning'artificial intelligence' just for the sake of it. Technology comes down to speed and consistency, not tricks.

Yandex recently announced its new search algorithm Palekh, which improves how Yandex understands the meaning behind every search query by using its deep neural networks as a ranking factor among others. Ultimately, the new algorithm helps Yandex improve its search results across the board but especially for long-tail search queries. As most State of Digital readers know, long-tail search queries are categorized by searches that the search engine very rarely processes. There is a correlation between the rarity of a query and the length of it. Typically, the shorter the query the more common it is and the longer it is the more rare it is.