Takeshi Niinami, chief executive officer of Suntory Holdings Ltd., believes that companies and governments need to offer proper training and education to their employees with a long-term view toward new technologies as people still lack the skills needed for "the fourth industrial revolution." "Technology will be the rule-changer in the future, and we need to maximize the sensitivity of our antenna for technological innovation," Niinami told The Japan Times in an interview during the World Economic Forum's annual meeting in Davos, Switzerland, last month. Niinami pointed out that the fourth industrial revolution, along with the growth of protectionist sentiment and female empowerment, were the topics that dominated discussions among business and political leaders. The fourth industrial revolution refers to the drastic social and industrial changes being caused by the recent emergence of disruptive technologies including the internet of things, robotics, virtual reality and artificial intelligence that will fundamentally alter the way we live and work. Last March, the WEF established the Center for the Fourth Industrial Revolution in San Francisco to bring together business leaders, governments, startups, academics and international organizations to accelerate cross-sector cooperation and to co-design policies for such emerging technologies as artificial intelligence and drones.

In our view, 2018 will be the year leading innovators begin to reveal -- and revel in -- the possibilities offered by Artificial Intelligence (AI) across all parts of the fashion value chain. Over the past couple of years, the potential of AI -- computer systems able to perform tasks normally requiring human intelligence -- has expanded considerably as a result of increasingly large and diverse data sets, advancement in key algorithms, and unprecedented levels of mathematical computing power. Although fashion has not thus far been a leader in this sphere, we expect to see fashion companies on the digital frontier demonstrate this potential as they start to deploy breakthrough AI innovations. Pioneers in this field will realise palpable returns from these efforts and demonstrate the potential advantages for companies that successfully marry creativity and AI. Many fashion executives regard AI as too mechanical to capture the creative core of fashion, and so are uncertain of what exactly it can do for them.

On Articial Intelligence Day on 13 December 2017, 600 artificial intelligence experts and enthusiasts gathered in Dipoli, Aalto University's newly-renovated main building. The organiser, the new Finnish Center for Artificial Intelligence FCAI established by Aalto University and the University of Helsinki, wished to promote matchmaking, information sharing and cross-border collaboration with the event. Representatives of over 180 companies were offered matchmaking opportunities during pitching, demo and poster sessions. One of the large Finnish companies present was Elisa, who's vice president in business development Kimmo Pentikäinen met up with Samuel Kaski, Professor at Aalto University and Head of FCAI, in the AI Day networking area. They discussed the needs of Elisa as an eager partner for research institutions.

She also cofounded Robot Garden, a robotics makerspace, and teaches Interaction Design and Theory. Andra has a particular interest in understanding diversity and representation in robotics and AI, and started the Women in Robotics community. Tolga Kurtoglu is CEO of PARC, a Xerox company, which is in "the business of breakthroughs." Practicing open innovation since being incorporated in 2002, PARC provides custom R&D services, technology, specialized expertise, best practices, and intellectual property to Xerox's business groups, Fortune 500 and Global 1000 companies, startups, and government. Dr. Kurtoglu oversees PARC's R&D investments for Xerox and its innovation portfolio for commercial clients and government agencies in a diverse set of focus areas and competencies including human-centered innovation services, intelligent agents and systems, clean energy, smart packaging, machine learning and analytics, security and privacy, printed electronics and digital manufacturing. Piero Scaruffi is an amateur human being and professional free thinker. He graduated in Mathematics (summa cum laude) in 1982 from University of Turin, where he did work in General Theory of Relativity. For a number of years he was the founding director of the Artificial Intelligence Center at Olivetti, based in Cupertino, California, and later joined IntelliCorp, one of the earliest companies specializing in Artificial Intelligence. He has been a visiting scholar at Harvard University and Stanford University conducting research on Artificial Intelligence and Cognitive Science, has lectured in three continents on "The Nature of Mind", and "History of Knowledge" (most recently at U.C. Berkeley), and has published a number of books, including "History of Silicon Valley" -- his bestseller in China, as well as hundreds of articles for magazines both in Italy and the U.S. Allen Saakyan is a polymath and science communicator hosting the worlds most thought-provoking conversations with global leaders.

Letting your dog get away with disobedience won't benefit it during its old age, a new study has found. Researchers at the University of Veterinary Medicine in Vienna discovered that, instead, having your elderly dog play games on a touch screen might stave off cognitive decline. The scientists found that brain training and problem solving can slow the pace of brain deterioration. But the older dogs that could benefit from the training are rarely introduced to it. The study explains: 'Unlike puppies or young dogs, old dogs are almost never trained or challenged mentally.

The team at ClearBrain has a big goal: "Our mission is to democratize AI for marketers." That's how co-founder and CEO Bilal Mahmood put it, though Mahmood (a former product manager at Optimizely) and his co-founder Eric Pollman (a former engineer on Google's ad team) aren't trying to do all that democratizing at once. Instead, they're tackling a more specific challenge -- helping companies target ads toward the users most likely to (say) sign up for a subscription, buy a product or cancel their account. Mahmood said that this kind of targeting has been available to larger companies, but was too expensive for everyone else, regardless of whether they wanted to buy or build it internally. With ClearBrain, on the other hand, pricing starts at $499 per month, and it's taking advantage of what Mahmood described as "this growing trend in terms of different API data layers" -- namely, the rise of tools like Segment, Optimizely and Heap.

Consider a number of wind turbines scattered throughout the North Sea. Each turbine is equipped with a Query processing over data streams is a key aspect of Big sensor, which continuously records temperature levels of key Data applications. For instance, algorithmic trading relies on devices within the turbine and sends those readings to a data real-time analysis of stock tickers and financial news items centre monitoring the functioning of the turbines. Temperature (Nuti et al. 2011); oil and gas companies continuously monitor levels are streamed by sensors using a ternary predicate and analyse data coming from their wellsites in order Temp, whose arguments identify the device, the temperature to detect equipment malfunction and predict maintenance level, and the time of the reading. A monitoring task in the needs (Cosad et al. 2009); network providers perform realtime data centre is to track the activation of cooling measures in analysis of network flow data to identify traffic anomalies each turbine, record temperature-induced malfunctions and and DoS attacks (Münz and Carle 2007).

Pay-per-click advertising includes various formats (e.g., search, contextual, and social) with a total investment of more than 140 billion USD per year. An advertising campaign is composed of some subcampaigns-each with a different ad-and a cumulative daily budget. The allocation of the ads is ruled exploiting auction mechanisms. In this paper, we propose, for the first time to the best of our knowledge, an algorithm for the online joint bid/budget optimization of pay-per-click multi-channel advertising campaigns. We formulate the optimization problem as a combinatorial bandit problem, in which we use Gaussian Processes to estimate stochastic functions, Bayesian bandit techniques to address the exploration/exploitation problem, and a dynamic programming technique to solve a variation of the Multiple-Choice Knapsack problem. We experimentally evaluate our algorithm both in simulation-using a synthetic setting generated from real data from Yahoo!-and in a real-world application over an advertising period of two months.

Feature engineering is a crucial step in the process of predictive modeling. It involves the transformation of given feature space, typically using mathematical functions, with the objective of reducing the modeling error for a given target. However, there is no well-defined basis for performing effective feature engineering. It involves domain knowledge, intuition, and most of all, a lengthy process of trial and error. The human attention involved in overseeing this process significantly influences the cost of model generation. We present a new framework to automate feature engineering. It is based on performance driven exploration of a transformation graph, which systematically and compactly captures the space of given options. A highly efficient exploration strategy is derived through reinforcement learning on past examples.

Credit card transactions predicted to be fraudulent by automated detection systems are typically handed over to human experts for verification. To limit costs, it is standard practice to select only the most suspicious transactions for investigation. We claim that a trade-off between exploration and exploitation is imperative to enable adaptation to changes in behavior (concept drift). Exploration consists of the selection and investigation of transactions with the purpose of improving predictive models, and exploitation consists of investigating transactions detected to be suspicious. Modeling the detection of fraudulent transactions as rewarding, we use an incremental Regression Tree learner to create clusters of transactions with similar expected rewards. This enables the use of a Contextual Multi-Armed Bandit (CMAB) algorithm to provide the exploration/exploitation trade-off. We introduce a novel variant of a CMAB algorithm that makes use of the structure of this tree, and use Semi-Supervised Learning to grow the tree using unlabeled data. The approach is evaluated on a real dataset and data generated by a simulator that adds concept drift by adapting the behavior of fraudsters to avoid detection. It outperforms frequently used offline models in terms of cumulative rewards, in particular in the presence of concept drift.