The virtual world is built on physical infrastructure. Every search that gets submitted, email sent, page served, comment posted, and video loaded passes through data centers that can be larger than a football field. Those thousands of racks of humming servers use vast amounts of energy; together, all existing data centers use roughly 2% of the world's electricity, and if left unchecked, this energy demand could grow as rapidly as Internet use. So making data centers run as efficiently as possible is a very big deal. Thankfully, despite skyrocketing demand for computing, data center electricity use has flattened over the past few years, largely due to enormous opportunities to improve efficiency as these facilities scale up.1 But capturing these opportunities can be a very complicated process.

The following is a contributed article by Peter Kirk, Business Operations Executive at GE Power Digital Solutions. With all of the press that neural networks have been getting recently, you may be asking yourself, "What is a neural network, and should I be intrigued or scared?" A neural network is a form of artificial intelligence (AI) that is loosely modeled after the human brain, and it can help solve real-world problems in the energy sector and beyond. Whether it's a threat or salvation depends on how it's used. In the 1990s, after the last AI hype cycle, a popular way to thumb one's nose at AI was to point out that neural models could generate a 24-hour weather forecast that is more accurate than a meteorologist -- it only takes 48 hours on a supercomputer to do so.

In this paper, a method for malfunctioning smart meter detection, based on Long Short-Term Memory (LSTM) and Temporal Phase Convolutional Neural Network (TPCNN), is proposed originally. This method is very useful for some developing countries where smart meters have not been popularized but in high demand. In addition, it is a new topic that people try to increase the service life span of smart meters to prevent unnecessary waste by detecting malfunctioning meters. We are the first people complete a combination of malfunctioning meters detection and prediction model based on deep learning methods. To the best our knowledge, our approach is the first method that achieves the malfunctioning meter detection of specific residential areas with their residents' data in practice. The procedure proposed creatively in this paper mainly consists of four components: data collecting and cleaning, prediction about electricity consumption based on LSTM, sliding window detection, and single user classification based on CNN. To make better classifying of malfunctioned user meters, we combine recurrence plots as image-input and combine them with sequence-input, which is the first work that applies one and two dimensions as two paths CNN's input for sequence data classification. Finally, many classical methods are compared with the method proposed in this paper. After comparison with classical methods, Elastic Net and Gradient Boosting Regression, the result shows that our method has higher accuracy. The average area under the Receiver Operating Characteristic (ROC) curve is 0.80 and the standard deviation is 0.04. The average area under the Precision-Recall Curve (PRC) is 0.84.

Next-generation cosmic microwave background (CMB) experiments will have lower noise and therefore increased sensitivity, enabling improved constraints on fundamental physics parameters such as the sum of neutrino masses and the tensor-to-scalar ratio r. Achieving competitive constraints on these parameters requires high signal-to-noise extraction of the projected gravitational potential from the CMB maps. Standard methods for reconstructing the lensing potential employ the quadratic estimator (QE). However, the QE performs suboptimally at the low noise levels expected in upcoming experiments. Other methods, like maximum likelihood estimators (MLE), are under active development. In this work, we demonstrate reconstruction of the CMB lensing potential with deep convolutional neural networks (CNN) - ie, a ResUNet. The network is trained and tested on simulated data, and otherwise has no physical parametrization related to the physical processes of the CMB and gravitational lensing. We show that, over a wide range of angular scales, ResUNets recover the input gravitational potential with a higher signal-to-noise ratio than the QE method, reaching levels comparable to analytic approximations of MLE methods. We demonstrate that the network outputs quantifiably different lensing maps when given input CMB maps generated with different cosmologies. We also show we can use the reconstructed lensing map for cosmological parameter estimation. This application of CNN provides a few innovations at the intersection of cosmology and machine learning. First, while training and regressing on images, we predict a continuous-variable field rather than discrete classes. Second, we are able to establish uncertainty measures for the network output that are analogous to standard methods. We expect this approach to excel in capturing hard-to-model non-Gaussian astrophysical foreground and noise contributions.

We consider the problem of learning an interpretable potential energy function from a Hamiltonian system's trajectories. We address this problem for classical, separable Hamiltonian systems. Our approach first constructs a neural network model of the potential and then applies an equation discovery technique to extract from the neural potential a closed-form algebraic expression. We demonstrate this approach for several systems, including oscillators, a central force problem, and a problem of two charged particles in a classical Coulomb potential. Through these test problems, we show close agreement between learned neural potentials, the interpreted potentials we obtain after training, and the ground truth. In particular, for the central force problem, we show that our approach learns the correct effective potential, a reduced-order model of the system.

Delegates will be given further information on the nature of these challenges, constraints, and the solutions being sought. Applications will be invited, and an explanation of the Game Changers process given. Following the event, a physical non-active demonstrator will be created at the Robotics for Extreme Environments Laboratory to mimic the environment in which robots will need to be deployed. Successful applicants will be invited in early 2020 to demonstrate their robots. The solutions which demonstrate the greatest potential will be progressed through the Game Changers process with funding available to further develop their technology.

At first glance, the Berlin startup doesn't seem so different from others: a factory floor in the rear courtyard of a building in the city's Neukölln district, stacked preserving jars filled with muesli in the kitchen, a discarded ping-pong surface repurposed as a conference table. The employees are young, relaxed and very international. The company's head and founder, Christian Kroll, is 35 years old, the same age as Mark Zuckerberg. The two men also share a quirk: To avoid wasting time in the mornings choosing an outfit, he always wears the same thing -- in his case, blank white T-shirts made from organic cotton. Zuckerberg's favorite color, by contrast, is gray.

It's 6 A.M., and the alarm clock is buzzing earlier than usual. It's not a malfunction: the smart clock scanned your schedule and adjusted because you've got that big presentation first thing in the morning. Your shower automatically turns on and warms to your preferred 103 F. The electric car is ready to go, charged by the solar panels or wind turbine on your roof. When you get home later, there's an unexpected package waiting, delivered by drone. You open it to find cold medicine.

The UK's forecasts for solar power generation have become far more accurate through the use of artificial intelligence, in a development that could lower energy bills and carbon emissions. The country's energy system is becoming more reliant sources of electricity with a variable output. Renewables like wind and solar, which depend on the weather, provided 36 per cent of our electricity at the start of this year, up from 7 per cent in 2009. "The growth in solar was much, much more fast-paced than anyone anticipated," says …

The promise of big data and artificial intelligence is everywhere. And, in all cases, so are the results. One almost gets the impression that there is no problem that cannot be solved with these new technologies. The answer to everything is'big data and artificial intelligence'. Open a web browser and you see advertising tuned to your latest online shopping.