Already busy dealing with customers in phone stores, train stations, and departments stores, Pepper the robot has now been put to work in two hospitals in Belgium. The android, which can understand and respond to a range of human emotions, started assisting visitors at two health facilities in Ostend and Liege on Monday. Pepper launched to great fanfare in Japan exactly a year ago, with the first batch of 1,000 units snapped up in just 60 seconds. The creation of Japanese telecom giant SoftBank and French robotics company Aldebaran SAS, the robot is being marketed as an assistant for businesses and also as a companion for families and those living alone. Standing 120-cm tall, Pepper can converse in a number of languages and also communicate via its torso-based tablet.

Another big part of the food supply comes from ranches and farms that raise and slaughter various livestock. While ranching is sometimes bundled with agriculture, I discussed farming in Big Data in Agriculture, so we'll focus on ranching this time around. Somewhat surprising is that big data usage in ranching appears more limited than in farming. That said, there are a number of novel uses of technology and data in animal husbandry. At a high level, the goals of ranching and farming are the same as any business: increase yields and lower costs. Production maximization has long played a role in large operations.

If you write marketing or advertising text for a living, you may want to get a second job skill. That's because software that writes text is here, and it is tackling a growing list of assignments. Several companies offer software that regularly churns out thousands of stories and reports based on structured data, like financial results. Ads that literally write themselves emerged last week, as IBM announced a new service based on its Watson supercomputer. A program called Quakebot has generated earthquake stories for the LA Times.

Akihiro Kishimoto is a research staff member at IBM Research – Ireland working on a range of projects in artificial intelligence, parallel and distributed computing and search. His interest in these technical fields grew from his passion for board games. And while a student at the University of Tokyo, he and three of his fellow classmates designed ISshogi, a program to play the incredibly complex (and ancient) Japanese board game, Shogi. ISshogi won the World Computer Shogi Championships four times from 1997-2005. While studying AI at the University of Alberta, Akihiro was a member of the GAMES group (Game-playing, Analytical methods, Minimax search and Empirical Studies) in the Department of Computing Science, and worked with Jonathan Schaeffer and others to solve Checkers.

London: An intelligent and highly sophisticated robot developed by a research team of University of Birmingham in the UK is joining the world of work as a trainee office manager. The robot, named Betty, will greet guests at reception and carry out tasks at the Transport Systems Catapult, based in Milton Keynes, in the UK, for a two-month trial period. Betty's duties will include patrolling the offices, assessing how many staff members are in the office outside working hours and monitoring the environment by collating data on clutter, office temperature, humidity and noise. She will also check fire doors are closed and desks are clear. A highly sophisticated robot, Betty runs Artificial Intelligence-driven software developed by an international research team led by the University of Birmingham.

Japan's electronics makers are beefing up efforts to recruit hard-to-find artificial intelligence experts -- a critical resource as connected technologies and services loom large on the industry's path forward. Growth in the number of college graduates with AI expertise -- typically math whizzes or engineers with additional knowledge of programming languages and data analysis -- has failed to keep pace with rising demand for such talent. The global supply is only in the tens of thousands, pitting companies expanding AI research operations against each other in the search for top recruits. Sony will next spring begin bulking up its ranks of such new graduates with a specialized recruiting framework for research and development in AI and machine learning. No limit will be set on the number of staff that can be hired, unlike under the company's normal recruiting system.

In fact, unit shipments of artificial intelligence (AI) systems used in infotainment and ADAS systems are expected to rise from just 7 million in 2015 to 122 million by 2025, according to IHS Inc. (NYSE: IHS), the leading global source of critical information and insight. The attach rate of AI-based systems in new vehicles was 8 percent in 2015, and the vast majority were focused on speech recognition. However, that number is forecast to rise to 109 percent in 2025, as there will be multiple AI systems of various types installed in many cars. "An artificial-intelligence system continuously learns from experience and by its ability to discern and recognize its surroundings," said Luca De Ambroggi, principal analyst-automotive semiconductors, IHS Technology. "It learns, as human beings do, from real sounds, images, and other sensory inputs. The system recognizes the car's environment and evaluates the contextual implications for the moving car." Specifically in ADAS, deep learning -- which mimics human neural networks -- presents several advantages over traditional algorithms; it is also a key milestone on the road to fully autonomous vehicles.

This paper studies two issues concerning relevance in structured argumentation in the context of the ASPIC+ framework, arising from the combined use of strict and defeasible inference rules. One issue arises if the strict inference rules correspond to classical logic. A longstanding problem is how the trivialising effect of the classical Ex Falso principle can be avoided while satisfying consistency and closure postulates. In this paper, this problem is solved by disallowing chaining of strict rules, resulting in a variant of the ASPIC+ framework called ASPIC*, and then disallowing the application of strict rules to inconsistent sets of formulas. Thus in effect Rescher & Manor's paraconsistent notion of weak consequence is embedded in ASPIC*. Another issue is minimality of arguments. If arguments can apply defeasible inference rules, then they cannot be required to have subset-minimal premises, since defeasible rules based on more information may well make an argument stronger. In this paper instead minimality is required of applications of strict rules throughout an argument. It is shown that under some plausible assumptions this does not affect the set of conclusions. In addition, circular arguments are in the new ASPIC* framework excluded in a way that satisfies closure and consistency postulates and that generates finitary argumentation frameworks if the knowledge base and set of defeasible rules are finite. For the latter result the exclusion of chaining of strict rules is essential. Finally, the combined results of this paper are shown to be a proper extension of classical-logic argumentation with preferences and defeasible rules.

This paper considers global optimization with a black-box unknown objective function that can be non-convex and non-differentiable. Such a difficult optimization problem arises in many real-world applications, such as parameter tuning in machine learning, engineering design problem, and planning with a complex physics simulator. This paper proposes a new global optimization algorithm, called Locally Oriented Global Optimization (LOGO), to aim for both fast convergence in practice and finite-time error bound in theory. The advantage and usage of the new algorithm are illustrated via theoretical analysis and an experiment conducted with 11 benchmark test functions. Further, we modify the LOGO algorithm to specifically solve a planning problem via policy search with continuous state/action space and long time horizon while maintaining its finite-time error bound. We apply the proposed planning method to accident management of a nuclear power plant. The result of the application study demonstrates the practical utility of our method.

- An Autonomous Underwater Vehicle (AUV) needs to possess a certain degree of autonomy for any particular underwater mission to fulfil the mission objectives successfully and ensure its safety in all stages of the mission in a large scale operating fi e ld . In this paper, a novel combinatorial conflict - free - task ass ignment strategy consisting of an interactive engagement of a local path planner and an adaptive global route planner, is introduced. The method takes advantage of the heuristic search potency of the Particle Swarm Optimization (PSO) algorithm to address t he discrete nature of routing - task assignment approach and the complexity of NP - hard path planning problem. The proposed hybrid method, is highly efficient as a consequence of its reactive guidance framework that guarantees successful completion of mission s particularly in cluttered environments. To examine the performance of the method in a context of mission productivity, mission time management and vehicle safety, a series of simulation studies are undertaken. The results of simulations declare that the proposed method is reliable and robust, particularly in dealing with uncertainties, and it can significantly enhance the level of a vehicle's autonomy by relying on its reactive nature and capability of providing fast feasible solutions.