An inanimate glob of ion-laced jelly can play the computer game Pong and even improve over time. Researchers plan further experiments to explore whether it can handle more complex computations and hope it could eventually be used to control robots. Inspired by previous research that used brain cells in a dish to play Pong, Vincent Strong and his colleagues at the University of Reading, UK, decided to try playing the tennis-like game with an even simpler material. They took a polymer material containing water and laced it with ions to make it responsive to electrical stimuli. When electricity is passed through the material, those ions move to the source of the current, dragging water with them and causing the gel to swell.

READING, England and SHANNON, Ireland, September 16, 2019 ― Extreme Networks, a software-driven networking company, announced today that it will invest 3 million Euro to expand its R&D program in Shannon, Ireland. The investment will create 20 new jobs in engineering, data science, and software engineering over the next two years, with the Irish government providing additional funding in the form of research, development, and innovation grant worth over 500,000 Euro. The newly established R&D team will focus on developing a next-generation cloud portal for network applications and services, as well as a state-of-the-art AI-based security system for the Internet of Things (IoT). The Shannon R&D base is part of a long-term strategy by Extreme Networks that will result in the creation of a substantial number of highly specialized engineering jobs in the region over the next five years. With the network industry currently experiencing clear shifts in spending patterns as enterprises focus more on applications and security over physical hardware, this support for the program in Shannon is the next step in building a new'Cloud and AI Centre of Excellence' for Extreme Networks.

Climate projections continue to be marred by large uncertainties, which originate in processes that need to be parameterized, such as clouds, convection, and ecosystems. But rapid progress is now within reach. New computational tools and methods from data assimilation and machine learning make it possible to integrate global observations and local high-resolution simulations in an Earth system model (ESM) that systematically learns from both. Here we propose a blueprint for such an ESM. We outline how parameterization schemes can learn from global observations and targeted high-resolution simulations, for example, of clouds and convection, through matching low-order statistics between ESMs, observations, and high-resolution simulations. We illustrate learning algorithms for ESMs with a simple dynamical system that shares characteristics of the climate system; and we discuss the opportunities the proposed framework presents and the challenges that remain to realize it.

Spatio-temporal data and processes are prevalent across a wide variety of scientific disciplines. These processes are often characterized by nonlinear time dynamics that include interactions across multiple scales of spatial and temporal variability. The data sets associated with many of these processes are increasing in size due to advances in automated data measurement, management, and numerical simulator output. Non- linear spatio-temporal models have only recently seen interest in statistics, but there are many classes of such models in the engineering and geophysical sciences. Tradi- tionally, these models are more heuristic than those that have been presented in the statistics literature, but are often intuitive and quite efficient computationally. We show here that with fairly simple, but important, enhancements, the echo state net- work (ESN) machine learning approach can be used to generate long-lead forecasts of nonlinear spatio-temporal processes, with reasonable uncertainty quantification, and at only a fraction of the computational expense of a traditional parametric nonlinear spatio-temporal models.

SLOUGH, England--(BUSINESS WIRE)--Global business outsourcing provider Arvato has entered a strategic partnership with Blue Prism to offer Robotic Process Automation (RPA) to help councils deliver back-office transformation. The partnership will see Arvato use the cutting-edge automation software to provide local authorities with an end-to-end solution of identifying, designing, building and monitoring automated processes, providing RPA-as-a-service and consultancy and training. Arvato will use the innovative technology to help current and future clients in local government automate transactional back office functions, such as revenues and benefits, HR, payroll and finance, increasing process speed and efficiency while freeing up employees to deliver front-line services. RPA uses software to create an agile, virtual workforce which mimics human processing of repetitive labour-intensive tasks. It follows rule-based business processes and interacts with systems in the same way that people do.

THE French Riviera is lovely at this time of year. The steering wheel spins to take the car round a bend – but my hands stay in my lap. And since there's no need to keep my eyes on the road, I'm free to enjoy the beachfront view. An oddly pixelated man with a two-dimensional windsurfer under his arm gives me the eye. Sadly, my Riviera is being projected on a large wrap-around screen in a room-sized simulator in Wokingham, UK.

This technology was developed with funding from the National Sony, the Japanese consumer electronics Science Foundation. Working with experts from Armco Steel (Middletown, company, has developed OH), Carnegie Group developed a prototype system to diagnose an intelligent system to improve chatter in a coldrolling mill. In the and consulting company, has developed a PCbased virtual reality system company's semiconductor group, to provide financial planners a visual metaphor for viewing large The system allows the user to "fly" over the The expert system is installed in Meiji's Tokyo service two-thirds. With Domain Dynamics Ltd. (Windsor, England) has developed a PCbased the system, technical support neural network application to automate the recognition of data from the Currently available in days to solve with a text retrieval the form of two circuit boards, TESPAR (which stands for Time Encoded system now take just afew minutes. Signal Processing and Recognition) is capable of being converted to a single piece of silicon.

In R. Engelmore and T. Morgan (Eds.), Blackboard systems. Wokingham, England: Addison-Wesley

This chapter from the Mycin book is a brief overview of van Melle's Ph.D. dissertation (Stanford, Computer Science), and is a shortened and edited version of a paper appearing in Pergamon-lnfotech state of the art report on machine intelligence, pp. 249-263. Maidenhead, Berkshire, U.K.: Infotech Ltd., 1981. Mycin Book (1984)