Mattias Ulbrich, CIO of Audi, discusses technology trends in the automotive industry and how his team is enabling next-generation manufacturing practices. Many companies are embedding sensors in product components and equipment to streamline manufacturing processes, improve yields, and create new business opportunities.1 1. "Digitizing manufacturing environments," McKinsey on Business Technology, 2016, forthcoming on McKinsey.com. The digitization of manufacturing, sometimes referred to as Industry 4.0., has begun in earnest, and Mattias Ulbrich, CIO of Audi, has witnessed the shift firsthand. "Years of experience in the automotive industry have made it clear to me that interdisciplinary collaboration--with business and IT working together--will be critical in this time of digital transformation. IT has a central role to play as change partner," he says.

AK: We just launched the cloud face recognition software platform, which is available for every business to plug into and use for their own recognition tasks. But we will thoroughly monitor its usage and ban those organisations and people who try to use it inappropriately. AK: Facial recognition technology can simplify our lives. It can be used for security, as part of ID checks at e-gates, or realtime criminal CCTV searches; it can be used in retail to display targeted advertising; in dating services to let users search by photo; or in banking to improve credit and ID checks. It can even be used in entertainment – by casinos who let you have your own gambling profiles, or amusement parks who want to send you personalised photos of you on rides.

Nintendo on Thursday released the name and video image of its yet-to-be launched video game product. To be called Switch, and launched next March, the product is aimed at breaking the boundary between portable and console games. It had previously been known by the code name NX. Users will be able to play Switch by connecting it to a TV, or using it as a portable gadget, like the Nintendo DS or Game Boy. Although details have yet to be announced, the three-minute video uploaded on Nintendo's corporate website at 11 p.m. Wednesday shows an actor playing the gaming system at home via TV, and converting it into a portable device using a controller called the Joy-Con. When played without a TV screen, a user needs to attach two small controller pieces -- Joy-Con (R) and Joy-Con (L) -- on both sides of a hand-sized monitor.

A Tokyo-based nonprofit organization will begin offering online Japanese-language classes this month to children from abroad who need help to keep up in class at Japanese elementary and junior high schools. Youth Support Center's YSC Global School in Fussa, western Tokyo, is set to offer instruction provided by language education experts via personal computers or tablets to young foreign nationals living anywhere in Japan. The NPO will cooperate with municipalities and schools without sufficient resources to teach Japanese to such children. Yuran Nakajima, 16, watched a lecture on a PC monitor at YSC's office in Fussa during a trial session in September. Three other students sat in the classroom elsewhere in the city, where the lesson was being taught.

A diverse range of exciting technology is currently being displayed in Tokyo for Japan Robot Week, an annual event that offers a glimpse into the gadgets of the future. Held in the East Hall of Tokyo Big Sight, the event showcases a range of robots designed for different activities and uses, primarily focusing on service robotics. It features exhibitions from 193 companies and organisations, with 465 booths. As well as that, this year features the seventh Robot Awards ceremony and exhibition, joint displays from universities and laboratories, and a range of programs and talks related to furthering the field of study. Intriguing technologies showcased at this year's exhibition include a range of humanoid robots designed to assist in domestic and commercial tasks, robotic cats and dogs, powered suits, trousers and backpacks with innovative technology and a range of communication robots. One of the robots exhibited at the show is Paro, the robotic seal designed as a therapeutic assistant to help at nursing homes or hospitals where real animals are not allowed.

In the foothills of the Appalachian Mountains in a corner of South Carolina sits a town that should be economically dead. For decades, Greenville was the heart of the state's textile industry--and its economic engine. First attracted by the area's fast-moving rivers as a way to power looms, textile manufacturers employed tens of thousands of people here. Beginning in the 1970s, however, facing competition from lower-cost manufacturing regions like Mexico and Southeast Asia, these companies began to struggle. Over the next decades, many factories closed.

Japanese team plans AI medical supercomputer to rival Watson Dr. Watson, your Japanese brother should be arriving soon. An artificial intelligence system that can accurately diagnose a patient and suggest the best treatment is being developed by Kyoto University and Fujitsu Ltd. The hope is that it will emulate IBM's Watson supercomputer, which is famed for AI use in medicine using a big data system, and is named after Thomas J. Watson, the founder of IBM, rather than Sherlock Holmes' sidekick. The new system will analyze the genetic codes of patients to make its assessments. To run different simulations on relationships between diseases and a number of genes with integrated various data, it will be fed databases of worldwide medical records as well as gene information.

From driverless cars to online financial infrastructure payments, Artificial Intelligence obviously will be the heart of the next industrial revolution. The wave of globalisation and democracy cannot be overlooked regarding their contributions steering policy integration. Both concepts will play vital roles towards integrating the African continent under an umbrella perceived to end humanity (Artificial Intelligence). The rise of machines and robotics in high-income economies has been a contested discourse by philosophers, economists, tech geeks and policy makers. There is a rising belief it would steal jobs and render humanity useless and even economists seem not to be certain about the relevance of labour in this period. Immediately Japan was announced as the host of 2020 Olympics, what would strike one's mind is the presence of robotics during the famous sporting event.

Many real-world control applications, from economics to robotics, are characterized by the presence of multiple conflicting objectives. In these problems, the standard concept of optimality is replaced by Pareto-optimality and the goal is to find the Pareto frontier, a set of solutions representing different compromises among the objectives. Despite recent advances in multi-objective optimization, achieving an accurate representation of the Pareto frontier is still an important challenge. In this paper, we propose a reinforcement learning policy gradient approach to learn a continuous approximation of the Pareto frontier in multi-objective Markov Decision Problems (MOMDPs). Differently from previous policy gradient algorithms, where n optimization routines are executed to have n solutions, our approach performs a single gradient ascent run, generating at each step an improved continuous approximation of the Pareto frontier. The idea is to optimize the parameters of a function defining a manifold in the policy parameters space, so that the corresponding image in the objectives space gets as close as possible to the true Pareto frontier. Besides deriving how to compute and estimate such gradient, we will also discuss the non-trivial issue of defining a metric to assess the quality of the candidate Pareto frontiers. Finally, the properties of the proposed approach are empirically evaluated on two problems, a linear-quadratic Gaussian regulator and a water reservoir control task.

We focus on the distribution regression problem: regressing to vector-valued outputs from probability measures. Many important machine learning and statistical tasks fit into this framework, including multi-instance learning and point estimation problems without analytical solution (such as hyperparameter or entropy estimation). Despite the large number of available heuristics in the literature, the inherent two-stage sampled nature of the problem makes the theoretical analysis quite challenging, since in practice only samples from sampled distributions are observable, and the estimates have to rely on similarities computed between sets of points. To the best of our knowledge, the only existing technique with consistency guarantees for distribution regression requires kernel density estimation as an intermediate step (which often performs poorly in practice), and the domain of the distributions to be compact Euclidean. In this paper, we study a simple, analytically computable, ridge regression-based alternative to distribution regression, where we embed the distributions to a reproducing kernel Hilbert space, and learn the regressor from the embeddings to the outputs. Our main contribution is to prove that this scheme is consistent in the two-stage sampled setup under mild conditions (on separable topological domains enriched with kernels): we present an exact computational-statistical efficiency trade-off analysis showing that our estimator is able to match the one-stage sampled minimax optimal rate [Caponnetto and De Vito, 2007; Steinwart et al., 2009]. This result answers a 17-year-old open question, establishing the consistency of the classical set kernel [Haussler, 1999; Gaertner et. al, 2002] in regression. We also cover consistency for more recent kernels on distributions, including those due to [Christmann and Steinwart, 2010].