Today, we can pull out our smart phones and use various apps to enhance our everyday lives. Digital assistants like Amazon's Alexa and Apple's Siri are able to complete a number of helpful tasks in- and outside the home. Powered by complex coding and algorithms, these technologies are affecting how we interact with things around us, and even each other. But tech experts are warning that while AI has some positive impacts, these new advances could harm our environment. "Like any new innovation, we need to consider and manage potential new risks," Jahda Swanborough, a global environmental leadership fellow and lead at the World Economic Forum, told DW.

Accurately predicting the future health of batteries is necessaryElectrochemical batteries, such as lithium-ion and leadacid to ensure reliable operation, minimise maintenance cells, experience degradation over time and during costs, and calculate the value of energy storage investments.usage, leading to decreased energy storage capacity and The complex nature of degradation renders datadrivenincreased internal resistance. Being able to predict the approaches a promising alternative to mechanistic rate of degradation and the remaining useful life (RUL) modelling. This study predicts the changes in batteryof a battery is important for performance and economic capacity over time using a Bayesian nonparametric reasons. For example, in an electric vehicle, the driveable approach based on Gaussian process regression. These range is directly related to the battery capacity. For energy changes can be integrated against an arbitrary input sequence storage asset valuation, depreciation, warranty, insurance to predict capacity fade in a variety of usage scenarios, and preventative maintenance purposes, predicting forming a generalised health model. The approach RUL at design stage and during operation is crucial, and naturally incorporates varying current, voltage and temperaturethe investment case is strongly dependent on the degradation inputs, crucial for enabling real world application.

In several real-world domains it is required to plan ahead while there are finite resources available for executing the plan. The limited availability of resources imposes constraints on the plans that can be executed, which need to be taken into account while computing a plan. A Constrained Partially Observable Markov Decision Process (Constrained POMDP) can be used to model resource-constrained planning problems which include uncertainty and partial observability. Constrained POMDPs provide a framework for computing policies which maximize expected reward, while respecting constraints on a secondary objective such as cost or resource consumption. Column generation for linear programming can be used to obtain Constrained POMDP solutions. This method incrementally adds columns to a linear program, in which each column corresponds to a POMDP policy obtained by solving an unconstrained subproblem. Column generation requires solving a potentially large number of POMDPs, as well as exact evaluation of the resulting policies, which is computationally difficult. We propose a method to solve subproblems in a two-stage fashion using approximation algorithms. First, we use a tailored point-based POMDP algorithm to obtain an approximate subproblem solution. Next, we convert this approximate solution into a policy graph, which we can evaluate efficiently. The resulting algorithm is a new approximate method for Constrained POMDPs in single-agent settings, but also in settings in which multiple independent agents share a global constraint. Experiments based on several domains show that our method outperforms the current state of the art.

Flying robots that can travel dozens of kilometres without stopping could be the next big thing for power companies. Utilities in Europe are looking to long-distance drones to scour thousands of miles of grids for damage and leaks in an attempt to avoid network failures that cost them billions of dollars a year. However the technology faces major safety and regulatory hurdles that are clouding its future in the sector. Snam and EDF's network subsidiary RTE have tested prototypes of long-distance drones that fly at low altitudes over pipelines and power lines. Italy's Snam, Europe's biggest gas utility, told Reuters it is trialling one of these machines - known as BVLOS drones because they fly'beyond the visual line of sight' of operators - in the Apennine hills around Genoa. It hopes to have it scouting a 20 km stretch of pipeline soon.

A group of roboticists at the University of Tokyo have created a flexible, flying "drone-robot" that could see a multitude of uses. The Dual-rotor embedded multilink Robot with the Ability of multi-deGree-of-freedom aerial transformatiON, is (thankfully) better known by its acronym, DRAGON. As illustrated in the video below, it can change its shape mid-flight and fly through tight spaces. The current version of DRAGON consists of four modules, each equipped with a set of maneuverable thrusters. Battery-powered hinged joints link the modules.

Information about the built environment at scale provides actionable insight that can transform a broad spectrum of industries, by enabling companies to deliver better, more targeted services to customers, telecommunications operators to perform accurate infrastructure planning, and government agencies to conduct regional planning and management. This is the vision behind Geoscape, a location-intelligence product conceived by PSMA Australia and developed in partnership with DigitalGlobe, a Maxar company, that delivers detailed information about buildings and their attributes, trees and land cover for every address in Australia. The data for each building in Geoscape includes the presence or absence of solar panels. This information is important for the insurance industry because solar panels may pose a fire hazard and thus affect insurance premiums. For firefighters, knowing if a structure has solar panels can also help them stay safe.

The non-stationarity characteristic of the solar power renders traditional point forecasting methods to be less useful due to large prediction errors. This results in increased uncertainties in the grid operation, thereby negatively affecting the reliability and increased cost of operation. This research paper proposes a unified architecture for multi-time-horizon predictions for short and long-term solar forecasting using Recurrent Neural Networks (RNN). The paper describes an end-to-end pipeline to implement the architecture along with the methods to test and validate the performance of the prediction model. The results demonstrate that the proposed method based on the unified architecture is effective for multi-horizon solar forecasting and achieves a lower root-mean-squared prediction error compared to the previous best-performing methods which use one model for each time-horizon. The proposed method enables multi-horizon forecasts with real-time inputs, which have a high potential for practical applications in the evolving smart grid.

Ready or not, autonomous robots are leaving laboratories to be tested in real-world contexts. With more and more people living in cities, these technologies offer ways to cope with ageing populations and poorly maintained infrastructures, while promoting safer transport, productive manufacturing and secure energy supplies. Urban "living labs" are one way scientists are trying to understand how autonomous robots – or Robotics and Autonomous Systems (RAS), to give them their full title – will affect our everyday lives. Autonomous robots are interconnected, interactive, cognitive and physical tools, which can perceive their environments, reason about events, make or revise plans and control their own actions. These technologies are designed to draw on big data and connect with the Internet of Things, to make our lives easier by increasing accuracy and efficiency.

Artificial Intelligence (A.I.) will soon be at the heart of every major technological system in the world including: cyber and homeland security, payments, financial markets, biotech, healthcare, marketing, natural language processing, computer vision, electrical grids, nuclear power plants, air traffic control, and Internet of Things (IoT). While A.I. seems to have only recently captured the attention of humanity, the reality is that A.I. has been around for over 60 years as a technological discipline. In the late 1950's, Arthur Samuel wrote a checkers playing program that could learn from its mistakes and thus, over time, became better at playing the game. MYCIN, the first rule-based expert system, was developed in the early 1970's and was capable of diagnosing blood infections based on the results of various medical tests. The MYCIN system was able to perform better than non-specialist doctors. While Artificial Intelligence is becoming a major staple of technology, few people understand the benefits and shortcomings of A.I. and Machine Learning technologies. Machine learning is the science of getting computers to act without being explicitly programmed.

Artificial Intelligence (A.I.) will soon be at the heart of every major technological system in the world including: cyber and homeland security, payments, financial markets, biotech, healthcare, marketing, natural language processing, computer vision, electrical grids, nuclear power plants, air traffic control, and Internet of Things (IoT). While A.I. seems to have only recently captured the attention of humanity, the reality is that A.I. has been around for over 60 years as a technological discipline. In the late 1950's, Arthur Samuel wrote a checkers playing program that could learn from its mistakes and thus, over time, became better at playing the game. MYCIN, the first rule-based expert system, was developed in the early 1970's and was capable of diagnosing blood infections based on the results of various medical tests. The MYCIN system was able to perform better than non-specialist doctors. While Artificial Intelligence is becoming a major staple of technology, few people understand the benefits and shortcomings of A.I. and Machine Learning technologies. Machine learning is the science of getting computers to act without being explicitly programmed.