Households have spent £180m over the past three years on spare power capacity that was never used, according to a report that comes as MPs prepare to debate what can be done about rising energy bills. Power stations have been put on standby over the winter since 2014 as part of the National Grid's supplemental balancing reserve but the Energy and Climate Intelligence Unit (ECIU) has found that the scheme, which closed in February, was never used. The thinktank said a new scheme to be introduced this winter would see the annual cost jump to £387m. Jonathan Marshall, energy analyst at the ECIU, said: "The clear message from this report is that paying to boost spare capacity in Britain's electricity system can be very expensive and potentially unnecessary." He calculated that increasing the capacity margin, the percentage difference between electricity supply and demand, would be extremely costly. The margin was 6.6% last winter, prompting a Lords committee last month to call for it to be boosted to 10%.

The Google-owned star British artificial intelligence company DeepMind is in talks with the National Grid about a potential partnership, with the possibility of using the technology to make the supply of energy across the UK more efficient. "There's huge potential for predictive machine learning technology to help energy systems reduce their environmental impact," said a spokesperson for the company. "One really interesting possibility is whether we could help the National Grid maximise the use of renewables through using machine learning to predict peaks in demand and supply." DeepMind's AI technology, which became famous after beating a human player at the chess-like game Go, has already been put to work for Google, reducing the energy needed for cooling its data centres by 40 per cent last year and increasing efficiency by 15 per cent. And co-founder Mustafa Suleyman outlined last year his hopes that this same technique could be applied to the National Grid and other large scale infrastructure. Read more: Here's how Google's DeepMind is using blockchain-like technology Now that has developed into early-stage talks taking place more recently between DeepMind – named City A.M's most innovative company of the year at the City A.M. Awards – and the National Grid, although there is no guarantee of anything being agreed.

A key challenge in scaling up robot learning to many skills and environments is removing the need for human supervision, so that robots can collect their own data and improve their own performance without being limited by the cost of requesting human feedback. Model-based reinforcement learning holds the promise of enabling an agent to learn to predict the effects of its actions, which could provide flexible predictive models for a wide range of tasks and environments, without detailed human supervision. We develop a method for combining deep action-conditioned video prediction models with model-predictive control that uses entirely unlabeled training data. Our approach does not require a calibrated camera, an instrumented training set-up, nor precise sensing and actuation. Our results show that our method enables a real robot to perform nonprehensile manipulation -- pushing objects -- and can handle novel objects not seen during training.

The University of Manchester is leading a consortium to investigate advanced technologies, including robotics and artificial intelligence, for the operation and maintenance of offshore windfarms. The remote inspection and asset management of offshore wind farms and their connection to the shore is an industry which will be worth up to £2 billion annually by 2025 in the UK alone. Eighty to ninety percent of the cost of offshore operation and maintenance according to the Crown Estate is generated by the need to get site access - in essence get engineers and technicians to remote sites to evaluate a problem and decide what action to undertake. Such inspection takes place in a remote and hazardous environment and requires highly trained personnel of which there is likely to be a shortage in coming years. The £5m project will investigate the use of advanced sensing, robotics, virtual reality models and artificial intelligence to reduce maintenance cost and effort.

A robot might take your job. A robot might take mine. I work at the World Economic Forum, and last year some of my colleagues looked at what robots might do to jobs in 15 economies that account for two thirds of the world's workforce. Their report reckons robots will throw over 5 million people out of jobs by 2020. The world's richest man, Bill Gates, has a solution to the problems our employment Terminators will cause.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. Communication with a robot using brain activity from a human collaborator could provide a direct and fast feedback loop that is easy and natural for the human, thereby enabling a wide variety of intuitive interaction tasks. This paper explores the application of EEG-measured error-related potentials (ErrPs) to closed-loop robotic control.

At a Tokyo job fair for the atomic energy industry on March 4, Kenta Kakitani, a graduate student at the University of Tokyo, hopes to some day become a nuclear plant design engineer. But Kakitani may be a rare breed in Japan, where nuclear businesses have seen a serious shortage of new talent since the March 11, 2011, meltdowns at the Fukushima No. 1 power plant, the world's worst nuclear disaster since Chernobyl in 1986. "It seems that the nuclear power industry has lost much of its popularity because it is seen as in decline and is suffering a negative image from having to decommission crippled reactors," said Kakitani, 24, who majors in nuclear engineering. According to education ministry data, 298 students entered departments related to nuclear power study in fiscal 2015, a slight decline from 317 in fiscal 2010. Kakitani said that although the number may not have declined drastically, many talented students are majoring in the fields of artificial intelligence and aerospace engineering instead of nuclear engineering. The turnout at the job fair reflects the nuclear power industry's fall from grace.

User preference integration is of great importance in multi-objective optimization, in particular in many objective optimization. Preferences have long been considered in traditional multicriteria decision making (MCDM) which is based on mathematical programming. Recently, it is integrated in multi-objective metaheuristics (MOMH), resulting in focus on preferred parts of the Pareto front instead of the whole Pareto front. The number of publications on preference-based multi-objective metaheuristics has increased rapidly over the past decades. There already exist various preference handling methods and MOMH methods, which have been combined in diverse ways. This article proposes to use the Web Ontology Language (OWL) to model and systematize the results developed in this field. A review of the existing work is provided, based on which an ontology is built and instantiated with state-of-the-art results. The OWL ontology is made public and open to future extension. Moreover, the usage of the ontology is exemplified for different use-cases, including querying for methods that match an engineering application, bibliometric analysis, checking existence of combinations of preference models and MOMH techniques, and discovering opportunities for new research and open research questions.

The operator of the disaster-struck Fukushima No. 1 nuclear power plant said Thursday it will attempt to examine the inside of reactor 1 next Tuesday using a remote-controlled robot. The move follows a botched attempt by another self-propelled robot to take a look inside reactor 2, which had also sustained a meltdown after the March 11, 2011, Great East Japan Earthquake and tsunami. That robot became unable to move when it encountered debris and eventually could not be retrieved. These are the first attempts by Tokyo Electric Power Co. Holdings Inc. to examine the insides of the wrecked reactors since the nuclear disaster started. For the reactor 1 inspection, Tepco said the new robot will carry out a four-day probe inside the containment vessel.

The University of Manchester is leading a consortium to investigate advanced technologies, including robotics and artificial intelligence, for the operation and maintenance of offshore windfarms. The remote inspection and asset management of offshore wind farms and their connection to the shore is an industry which will be worth up to £2 billion annually by 2025 in the UK alone. Eighty to ninety percent of the cost of offshore operation and maintenance according to the Crown Estate is generated by the need to get site access - in essence get engineers and technicians to remote sites to evaluate a problem and decide what action to undertake. Such inspection takes place in a remote and hazardous environment and requires highly trained personnel of which there is likely to be a shortage in coming years. The £5m project will investigate the use of advanced sensing, robotics, virtual reality models and artificial intelligence to reduce maintenance cost and effort.