Our focus is on realistically modeling and forecasting dynamic networks of face-to-face contacts among individuals. Important aspects of such data that lead to problems with current methods include the tendency of the contacts to move between periods of slow and rapid changes, and the dynamic heterogeneity in the actors' connectivity behaviors. Motivated by this application, we develop a novel method for Locally Adaptive DYnamic (LADY) network inference. The proposed model relies on a dynamic latent space representation in which each actor's position evolves in time via stochastic differential equations. Using a state space representation for these stochastic processes and P\'olya-gamma data augmentation, we develop an efficient MCMC algorithm for posterior inference along with tractable procedures for online updating and forecasting of future networks. We evaluate performance in simulation studies, and consider an application to face-to-face contacts among individuals in a primary school.

Finland is one of the first countries to try out the minibuses on city roads thanks to its laws allowing cars to roam without a driver. Dubai had signed a deal with the company back in April to test the EasyMile vehicles, while a Japanese mall began using them to shuttle shoppers around this month. But neither of those will likely rival the live-traffic demands of the Helsini experiment. The buses won't be doing extensive hauls: the EZ10 model is built for short-range travel, say for ferrying folks between a metro station and bus stop, at a max speed of a little over six miles per hour. If all goes well, the vehicles will supplement but not replace existing mass transit networks.

Apple, Google, Facebook, and Microsoft are worldwide technology powerhouses, but when it comes to the adoption of virtual digital assistants (VDAs) like Siri, Google Assistant, and Cortana, scale only takes you so far. In this particular business, players who successfully cater to the nuances of individual countries will conquer the global VDA market. The same principle will apply to enterprises looking to automate customer interactions like customer service and e-commerce with enterprise VDAs. The challenges facing VDA providers were brought to light recently by the plight of Jibo, the crowdfunded smart home VDA robot that received pre-orders from consumers in 47 countries. On August 9, the company announced that product rollouts would be limited to the United States and Canada only, and that all orders for Jibo outside those markets will be refunded.

The era of big data has witnessed a paradigm shift into analytics. Today, it's no longer sufficient to simply gather data from social media, IoT, and wearable devices, and be unable to manage or filter it. It is more about delivering the right data to the right person, at the right time. This trend is growing crucial as data is multiplying every day and pouring in from various devices and smart machines including wearables, electronic gadgets, and other devices. Such factors call for the treatment of vast pools of structured and unstructured data with care and precision. This is precisely where invisible analytics come in.

Drone footage is offering a first look at a site where treasure hunters are digging for a buried Nazi train that could contain 250million worth of gold and other riches. With the dig gaining headlines around the world, the site in Walbrzych, Poland, has been sealed off to the prying eyes of the public, and the only way to get a glimpse of it is from high above. There is no guarantee the so-called'Nazi gold train' is buried there or even exists, but two explorers believe it is nearly 30ft below ground in a railway tunnel. Workers have set up fences and privacy screens to keep the public's prying eyes away In addition to gold and gems, Piotr Koper and Andreas Richter believe the site may have been used to hide the bodies of thousands of forced labourers. It could take up to 10 days for the site to be excavated.

At Harvard Business School (HBS), MBA students are pondering a future when robots rule the road. The pioneers of the driverless car movement -- such as Google and Tesla -- are mapping the MBAs a future in which artificial intelligence and robotics will likely impact the entire job market and global economy. David Yoffie, professor of international business administration at HBS, believes such disruptive technologies are now an "essential" part of the b-school landscape. "What I'm trying to teach students is: What can these technologies deliver? And what are the challenges and opportunities for a company that does AI?" he says. David's offered his MBAs two cases on artificial intelligence (or AI) and deep learning, and reckons that many of his colleagues at HBS are bringing robots into the curriculum too: "It's a capability that MBAs need to know about," he says.

Man vs. Machine: 10 years ago, I would never have guessed that I would be writing about this topic with such serious concern. Yet, some people are predicting that machine learning technology will produce a jobless future for certain professions, including actuaries. And with news headlines like "Google's AlphaGo AI beats Lee Sedol again to win Go series 4-1" and "Meet Ross, the IBM Watson-Powered Lawyer"; you have to wonder what the future holds for the actuarial profession and just how much computers can take over the human role within actuarial departments. With the tremendous advancements in machine learning, many financial institutions are already making extensive use of these technologies to do all types of (traditional and new) actuarial work including competitor rating reconstruction and intelligent claims handling. Let's take a moment to see what people are thinking about when it comes to the impact of machine learning on the development of their actuarial teams: Yes, give it time, machine learning will take over!

Machine intelligence capable of learning complex procedural behavior, inducing (latent) programs, and reasoning with these programs is a key to solving artificial intelligence. The problems of learning procedural behavior and program induction have been studied from different perspectives in many computer science fields such as program synthesis [1], probabilistic programming [2], inductive logic programming [3], reinforcement learning [4], and recently in deep learning. However, despite the common goal, there seems to be little communication and collaboration between the different fields focused on this problem. Recently, there have been many success stories in the deep learning community related to learning neural networks capable of using trainable memory abstractions. This has led to the development of neural networks with differentiable data structures such as Neural Turing Machines [5], Memory Networks [6], Neural Stacks [7, 8], and Hierarchical Attentive Memory [11], among others. Simultaneously, neural program induction models like Neural Program-Interpreters [9] and the Neural Programmer [10] have created much excitement in the field, promising induction of algorithmic behavior, and enabling inclusion of programming languages in the processes of execution and induction, while remaining trainable end-to-end. Trainable program induction models have the potential to make a substantial impact on many problems involving long-term memory, reasoning, and procedural execution, such as question answering, dialog, and robotics. The aim of the NAMPI workshop is to bring together researchers and practitioners from both academia and industry, in the areas of deep learning, program synthesis, probabilistic programming, inductive programming and reinforcement learning, to exchange ideas on the future of program induction with a special focus on neural network models and abstract machines. Through this workshop we look to identify common challenges, exchange ideas and lessons learned from the different fields, as well as establish a (set of) standard evaluation benchmark(s) for approaches that learn with abstraction and/or reason with induced programs.

Two driverless buses will be hitting the streets in Helsinki for some real-world testing for the next month or so, reports Finnish news outlet YLE. The EasyMile minibuses have previously been tested on public roads in Finland and elsewhere, but this is the first time they'll be mixing it up with everyday traffic there. It may seem old hat to Valley-dwellers who see Google's self-driving cars zooming around the streets of Mountain View, but different vehicles, with different purposes, in different countries, following different traffic rules may as well be a whole different industry. I've asked EasyMile for more information on what specifically the company will be testing for, and will update this post when I hear back.

The second-oldest confirmed shipwreck in the Great Lakes, an American-built, Canadian-owned sloop that sank in Lake Ontario more than 200 years ago, has been found, a team of underwater explorers said Wednesday. The three-member western New York-based team said it discovered the shipwreck this summer in deep water off Oswego, in central New York. Images captured by a remotely operated vehicle confirmed it is the Washington, which sank during a storm in 1803, team member Jim Kennard said. "This one is very special. We don't get too many like this," said Mr. Kennard, who along with Roger Pawlowski and Roland "Chip" Stevens has found numerous wrecks in Lake Ontario and other waterways.