Supporters can get information on a range of topics, including where to eat and drink or the nearest place to buy a Wimbledon towel. Three public wi-fi hotspots - also new for 2017 - will facilitate the process, and Wimbledon and IBM will monitor the questions Fred is asked to provide an improved service for next year. Supporters not at the All England Club but following the tournament will also be offered an improved experience this year. Wimbledon 2017 will see the launch of automated highlights packages, generated using IBM Watson and other video and audio technologies. An Artificial Intelligence system will pick out the key moments of the match based on analysis of crowd noise, players' facial expressions and a knowledge of when the clutch moments like set and break points took place.

The probability density function of a probability distribution is a fundamental concept in probability theory and a key ingredient in various widely used machine learning methods. However, the necessary framework for compiling probabilistic functional programs to density functions has only recently been developed. In this work, we present a density compiler for a probabilistic language with failure and both discrete and continuous distributions, and provide a proof of its soundness. The compiler greatly reduces the development effort of domain experts, which we demonstrate by solving inference problems from various scientific applications, such as modelling the global carbon cycle, using a standard Markov chain Monte Carlo framework.

We consider the binary classification problem when data are large and subject to unknown but bounded uncertainties. We address the problem by formulating the nonlinear support vector machine training problem with robust optimization. To do so, we analyze and propose two bounding schemes for uncertainties associated to random approximate features in low dimensional spaces. The proposed techniques are based on Random Fourier Features and the Nystr\"om methods. The resulting formulations can be solved with efficient stochastic approximation techniques such as stochastic (sub)-gradient, stochastic proximal gradient techniques or their variants.

Similarity-based approaches represent a promising direction for time series analysis. However, many such methods rely on parameter tuning, and some have shortcomings if the time series are multivariate (MTS), due to dependencies between attributes, or the time series contain missing data. In this paper, we address these challenges within the powerful context of kernel methods by proposing the robust \emph{time series cluster kernel} (TCK). The approach taken leverages the missing data handling properties of Gaussian mixture models (GMM) augmented with informative prior distributions. An ensemble learning approach is exploited to ensure robustness to parameters by combining the clustering results of many GMM to form the final kernel. We evaluate the TCK on synthetic and real data and compare to other state-of-the-art techniques. The experimental results demonstrate that the TCK is robust to parameter choices, provides competitive results for MTS without missing data and outstanding results for missing data.

This paper presents a unified framework for smooth convex regularization of discrete optimal transport problems. In this context, the regularized optimal transport turns out to be equivalent to a matrix nearness problem with respect to Bregman divergences. Our framework thus naturally generalizes a previously proposed regularization based on the Boltzmann-Shannon entropy related to the Kullback-Leibler divergence, and solved with the Sinkhorn-Knopp algorithm. We call the regularized optimal transport distance the rot mover's distance in reference to the classical earth mover's distance. We develop two generic schemes that we respectively call the alternate scaling algorithm and the non-negative alternate scaling algorithm, to compute efficiently the regularized optimal plans depending on whether the domain of the regularizer lies within the non-negative orthant or not. These schemes are based on Dykstra's algorithm with alternate Bregman projections, and further exploit the Newton-Raphson method when applied to separable divergences. We enhance the separable case with a sparse extension to deal with high data dimensions. We also instantiate our proposed framework and discuss the inherent specificities for well-known regularizers and statistical divergences in the machine learning and information geometry communities. Finally, we demonstrate the merits of our methods with experiments using synthetic data to illustrate the effect of different regularizers and penalties on the solutions, as well as real-world data for a pattern recognition application to audio scene classification.

Twice in the space of six weeks, the world has suffered major attacks of ransomware - malicious software that locks up photos and other files stored on your computer, then demands money to release them. It's clear that the world needs better defenses, and fortunately those are starting to emerge, if slowly and in patchwork fashion. When they arrive, we may have artificial intelligence to thank. Employees watch electronic boards to monitor possible ransomware cyberattacks at the Korea Internet and Security Agency in Seoul, South Korea. Unable to rely on good human behavior, computer security experts are developing software techniques to fight ransomware. But getting these protections in the hands of users is challenging.

The fast paced and fragmented online discussion is changing the world and not always to the better. Media is struggling with moderation demands and major news sites are closing down commenting on their articles, because they are being used to drive an unrelated political agenda, or just for trolling. Moderation practice cannot rely on humans anymore, because a single person can easily generate copious amounts of content, and moderation needs to be done with care. It's simply much more time consuming than cut and pasting your hate or ads all across the internet. Anonymity adds to the problem, as it seems to bring out the worst in people.

Just because Grindelwald and Dumbledore had a deadly brawl during their quest to revolutionise magic doesn't mean two great powers cannot be used in concert to change the world. This could be the worst way to start an important conversation about financial technology, but stick with me, it gets more interesting. We are speaking about the world-altering technology of Artificial Intelligence as the first superpower coupled with the financial system disruptive technology of cryptocurrency -- a decentralised payment system that circumvents government manipulation of currency and is forcing us to redefine the concept of money. The question is: Can these two technologies be used together to change the way ordinary people like you and me invest our money -- without expiring in a shower of blue sparks? But first, let's take a step back and look into them as individual concepts, with respect to their relationships to investment and trading.

Paralyzed from the waist down after a BMX accident, Steven Sanchez rolled into SuitX's Berkeley, California, office in a wheelchair. A half-hour later he was standing and walking thanks to the Phoenix--a robotic exoskeleton now available for around $40,000. The suit returns movement to wearers' hips and knees with small motors attached to standard orthotics. Wearers can control the movement of each leg and walk at up to 1.1 miles per hour by pushing buttons integrated into a pair of crutches. At 27 pounds, the Phoenix is among the lightest and cheapest medical exoskeletons.

'The robot at the remote site has different force, humidity and temperature sensors, all capturing information that a doctor would get when they are directly palpating (physically examining) a patient,' explains Professor Angelika Peer, a robotics researcher at the University of the West of England, UK. Prof. Peer is also the project coordinator of the EU-funded ReMeDi project, which is developing the robotic doctor to allow medical professionals to examine patients over huge distances. Through a specially designed surface mounted on a robotic arm, stiffness data of the patient's abdomen is displayed to the human, allowing the doctor to feel what the remote robot feels. This is made possible thanks to a tool called a haptic device, which has a soft surface reminiscent of skin that can recreate the sense of touch through force and changing its shape. During the examination, the doctor sits at a desk facing three screens, one showing the doctor's hand on the faraway patient and a second for teleconferencing with the patient, which will remain an essential part of the exchange.