National Grid is to examine how artificial intelligence can be used to make the UK's power distribution infrastructure more efficient. The company admitted over the weekend that it is in talks with Google's DeepMind artificial intelligence unit, which it acquired for $400m in January 2014, as well as a number of other AI specialists. "We are in the very early stages of looking at the potential of working with DeepMind and exploring what opportunities they could offer for us," a spokesperson for National Grid told City AM. "There's huge potential for predictive machine learning technology to help energy systems reduce their environmental impact," they added. The news was broken on Saturday when DeepMind co-founder and CEO Demis Hassabis claimed in an interview with the Financial Times. "We're [in] early stages talking to National Grid and other big providers about how we could look at the sorts of problems they have.

Stephen Hawking, widely considered to be the most accomplished theoretical physicist in the entire world has made no secret of his fears that the technology behind artificial intelligence is developing faster than human beings can keep up with, and could eventually lead to a destruction of the human race. Now he has proposed a solution to this terrifying potential problem although he believes the solution could prove to be an even greater threat to the world. Speaking to The Times, Professor Hawking explained his fears about the threat posed by artificial intelligence. "Since civilization began, aggression has been useful since it has definite survival advantages, " he said, "It is hard-wired into our genes by Darwinian evolution. Now, however, technology has advanced at such a pace that this aggression may destroy us all by nuclear or biological war. We need to control this inherited instinct by our logic and reason."

In the consumer report, we are number one once again and just like the Q7, in the consumer report it also occupies the first position as the best luxury SUV. And I think this power of the brand makes it possible for us to grow significantly. There are couple of models which have not even be launched yet in this market, models which we already know here, for instance the S4, the A5, and the entirely new A5 Sportback. They are now being launched in the United States. All new models for this market, and I assume that this year once again we are going to experience very solid growth in the United States. And the question so whether we spend more money for this? I can tell you we even spend less money in form of sales discounts because of the powerful brand and the relatively young product portfolio. So you would take the second part?

After years of banning Japan's fish and agriculture, many countries might be willing to give the nation a second chance and import its goods. The Fukushima Daiichi nuclear disaster in 2011 and the resulting radiation in the region caused 54 countries and regions to implement restrictions on certain Japanese goods. That number has shrunken to 33, with more nations likely to follow suit and lift the ban, the Japan Times reported Wednesday. Read: 'Unimaginable' Radiation At Fukushima So Destructive, Not Even Robots Can Survive "We are looking forward to the lifting of the South Korean import ban," Masao Atsumi, a sea-squirt farmer in Miyagi prefecture, told the Japan Times. An aerial view shows the Fukushima power plant in Fukushima, Japan, Feb. 26, 2012.

There are three equivalent ways of representing two jointly observed real-valued signals: as a bivariate vector signal, as a single complex-valued signal, or as two analytic signals known as the rotary components. Each representation has unique advantages depending on the system of interest and the application goals. In this paper we provide a joint framework for all three representations in the context of frequency-domain stochastic modeling. This framework allows us to extend many established statistical procedures for bivariate vector time series to complex-valued and rotary representations. These include procedures for parametrically modeling signal coherence, estimating model parameters using the Whittle likelihood, performing semi-parametric modeling, and choosing between classes of nested models using model choice. We also provide a new method of testing for impropriety in complex-valued signals, which tests for noncircular or anisotropic second-order statistical structure when the signal is represented in the complex plane. Finally, we demonstrate the usefulness of our methodology in capturing the anisotropic structure of signals observed from fluid dynamic simulations of turbulence.

Active search (AS) on graphs focuses on collecting certain labeled nodes (targets) given global knowledge of the network topology and its edge weights under a query budget. However, in most networks, nodes, topology and edge weights are all initially unknown. We introduce selective harvesting, a variant of AS where the next node to be queried must be chosen among the neighbors of the current queried node set; the available training data for deciding which node to query is restricted to the subgraph induced by the queried set (and their node attributes) and their neighbors (without any node or edge attributes). Therefore, selective harvesting is a sequential decision problem, where we must decide which node to query at each step. A classifier trained in this scenario suffers from a tunnel vision effect: without recourse to independent sampling, the urge to query promising nodes forces classifiers to gather increasingly biased training data, which we show significantly hurts the performance of AS methods and standard classifiers. We find that it is possible to collect a much larger set of targets by using multiple classifiers, not by combining their predictions as an ensemble, but switching between classifiers used at each step, as a way to ease the tunnel vision effect. We discover that switching classifiers collects more targets by (a) diversifying the training data and (b) broadening the choices of nodes that can be queried next. This highlights an exploration, exploitation, and diversification trade-off in our problem that goes beyond the exploration and exploitation duality found in classic sequential decision problems. From these observations we propose D3TS, a method based on multi-armed bandits for non-stationary stochastic processes that enforces classifier diversity, matching or exceeding the performance of competing methods on seven real network datasets in our evaluation.

The operator of the crippled Fukushima No. 1 nuclear complex said Tuesday it suspended its plan to start examining the inside of reactor 1 with a self-propelled robot after having camera trouble. Tokyo Electric Power Company Holdings Inc. is checking the cause of the problem and hoping to resume the survey Wednesday in its latest attempt at ascertaining the condition of melted fuel debris in order to extract it. While preparation was underway to send the robot inside the containment vessel after launching the day's work shortly after 10 a.m., a camera monitoring inside a box containing the robot, cables and other related equipment was found to be showing no images, it said. The box is located just outside the containment vessel. The malfunctioning camera is separate from one attached to the survey robot and is used to check whether the devices are functioning properly, according to the utility. Tepco has been hoping to view the debris through a camera embedded in the shape-shifting robot in order to decide how to extract the deposits of fuel presumed to have penetrated the reactor pressure vessel and melted through the containment vessel, which is supposed to hold the fuel.

When I first heard of predictive analytics, machine learning, and cognitive security, I was skeptical. I am an engineer at heart and condition-based maintenance was the only way, I thought, to effectively look at predictive maintenance. You start with the physical asset, you deploy sensors to monitor critical components, and you analyze the data. The thing is, this approach is expensive and time-consuming. Sensors need to be deployed and installed on existing equipment, software to collect, store, and process the data needs to be integrated, O&M teams need to be trained on the technology, and the software needs to be constantly updated.

The National Grid has confirmed that it is in the "earliest stages" of discussions exploring the use of Artificial Intelligence (AI), which could potentially maximise the use of renewable energy by predicting peaks in demand across the UK. The National Grid, which operates and owns the infrastructure that transports electricity across the UK, has seen its ability in balancing and stabilising the grid challenged in recent years as intermittent renewables such as solar and wind have been fed into the energy mix. While the introduction of renewables into the mix forms a key role in both national and European legislation to decarbonise the grid, concerns have been raised as to the National Grid's ability to deal with fluctuating wind and solar resources, which can sometimes produce more energy than the system can cope with. Energy storage and demand response initiatives, whereby businesses either store surplus energy or increase or reduce energy consumption based on demand, are being incorporated by the National Grid, which is now "exploring what opportunities" AI could offer to balance the situation. The National Grid revealed that it is in discussions with the UK-based AI company DeepMind about introducing new technologies to help balance the grid and improve the use of renewables.

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. Machine learning is applied in various fields such as computer vision, speech recognition, NLP, web search, biotech, risk management, cyber security, and many others.