As technology tries to maintain its dizzying ascent, one dead weight has kept its altitude in check: the battery. Our chips keep getting faster and our data rates keep climbing, but at the end of the day--or worse, by mid-afternoon--those power meters on our screens inevitably turn to red. Every great device, gadget, electric car, and robot would be even greater if batteries didn't suck so badly. Despite a steady flow of rumors that transformative breakthroughs are just around the corner, progress has moved at the pace of a tar flow. Steven Levy is Backchannel's founder and Editor in Chief. Sign up to get Backchannel's weekly newsletter.

Probabilistic inversion within a multiple-point statistics framework is still computationally prohibitive for large-scale problems. To partly address this, we introduce and evaluate a new training-image based simulation and inversion approach for complex geologic media. Our approach relies on a deep neural network of the spatial generative adversarial network (SGAN) type. After training using a training image (TI), our proposed SGAN can quickly generate 2D and 3D unconditional realizations. A key feature of our SGAN is that it defines a (very) low-dimensional parameterization, thereby allowing for efficient probabilistic (or deterministic) inversion using state-of-the-art Markov chain Monte Carlo (MCMC) methods. A series of 2D and 3D categorical TIs is first used to analyze the performance of our SGAN for unconditional simulation. The speed at which realizations are generated makes it especially useful for simulating over large grids and/or from a complex multi-categorical TI. Subsequently, synthetic inversion case studies involving 2D steady-state flow and 3D transient hydraulic tomography are used to illustrate the effectiveness of our proposed SGAN-based probabilistic inversion. For the 2D case, the inversion rapidly explores the posterior model distribution. For the 3D case, the inversion recovers model realizations that fit the data close to the target level and visually resemble the true model well. Future work will focus on the inclusion of direct conditioning data and application to continuous TIs.

During reactive transport modeling, the computational cost associated with chemical reaction calculations is often 10-100 times higher than that of transport calculations. Most of these costs results from chemical equilibrium calculations that are performed at least once in every mesh cell and at every time step of the simulation. Calculating chemical equilibrium is an iterative process, where each iteration is in general so computationally expensive that even if every calculation converged in a single iteration, the resulting speedup would not be significant. Thus, rather than proposing a fast-converging numerical method for solving chemical equilibrium equations, we present a machine learning method that enables new equilibrium states to be quickly and accurately estimated, whenever a previous equilibrium calculation with similar input conditions has been performed. We demonstrate the use of this smart chemical equilibrium method in a reactive transport modeling example and show that, even at early simulation times, the majority of all equilibrium calculations are quickly predicted and, after some time steps, the machine-learning-accelerated chemical solver has been fully trained to rapidly perform all subsequent equilibrium calculations, resulting in speedups of almost two orders of magnitude. We remark that our new on-demand machine learning method can be applied to any case in which a massive number of sequential/parallel evaluations of a computationally expensive function $f$ needs to be done, $y=f(x)$. We remark, that, in contrast to traditional machine learning algorithms, our on-demand training approach does not require a statistics-based training phase before the actual simulation of interest commences. The introduced on-demand training scheme requires, however, the first-order derivatives $\partial f/\partial x$ for later smart predictions.

It's man and machine reasoning together – Paul Reeves said. At PocketConfidant we try to understand and regulate how humans and machines can help each other achieving Human goals. Human and Machines relationship may be viable if there is empathy.

Hello, I'm looking for some advice on school choices for someone from a non-traditional background (undergrad and current master in chemical engineering, focused on controls) for getting into the ML field. Currently doing 1st year of 2 in Master in chemical engineering, my research topic is applying reinforcement learning to optimal control problems in smart grid energy management/demand-side management. I've been learning ML and RL for the past 3 years, can currently keep up with papers, implement these papers in Tensorflow, Pytorch and working on some additional personal projects (Deep RL related). Ultimately I'd like to work in a ML/RL research or applied position (non-academic, in private company research labs). My current worry is that my chem eng background is a bit of a non-traditional background, and I'm not sure how much of that will hinder my goal for getting the jobs I'm aiming for.

The same technology that lets soldiers see in the dark can also help protect birds and bats near offshore wind turbines. Night vision goggles use thermal imaging, which captures infrared light that's invisible to the human eye, and now, researchers are using thermal imaging to help birds and bats near offshore wind farms. The thermal tracking software automatically detects birds and bats, which is useful for night tracking they're hard to spot - and it could help inform policymakers about where new and existing offshore wind turbines should be placed. The thermal tracking software automatically detects birds and bats, which is useful for tracking them at night when they're hard to spot . The thermal imaging software, developed by researchers at the Department of Energy's Pacific Northwest National Laboratory (PNNL), is called ThermalTracker.

There are giant, complex machines out there that we all rely on. Without them, civilisation as we know it would collapse. But these machines - power stations - are often pretty dumb, according to Peter Kirk, former chief executive of software company NeuCo. "Power plants," he says, "are just robots that don't have a brain yet." That is where his firm, acquired by GE Power last year, comes in. For years, NeuCo had been developing optimisation technologies - a form of artificial intelligence or AI - that can make power plants more efficient.

Jennifer Zhu Scott is the co-founder and principal of Radian Blockchain Ventures and Radian Partners, focusing on private investment in the Artificial Intelligence, Blockchain, and renewable energy sectors. Prior, she was head of business development and strategy in APAC for Thomson Reuters and led the firm's speech-to-text, deep search, and video-indexing project. She co-founded one of the first education companies in China and exited before moving to UK as a senior advisor to the education subsidiary of Daily Mail & General Trust. How is Blockchain technology currently applied? First of all, I think we should demystify it. The Blockchain essentially is a decentralised database with a variety of applications, but not universal applications.

In this special guest feature, Mike Brooks, Senior Business Consultant at AspenTech, discusses how companies can no longer rely solely on traditional equipment maintenance practices but must also incorporate operational behaviors in deploying data-driven solutions using machine learning. For example, a North American energy company was losing up to a million dollars in repairs and lost revenue from repeat breakdowns of electric submersible pumps. In another case, a leading railway freight firm operating across 23 states in the US used Machine Learning to address perennial locomotive engine failures costing millions in repairs, fines, and lost revenue. Companies can no longer rely solely on traditional maintenance practices but must also incorporate operational behaviors in deploying data-driven solutions.

Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to (partially) solve the resource allocation problem adaptively in the cloud computing system. However, a complete cloud resource allocation framework exhibits high dimensions in state and action spaces, which prohibit the usefulness of traditional RL techniques. In addition, high power consumption has become one of the critical concerns in design and control of cloud computing systems, which degrades system reliability and increases cooling cost. An effective dynamic power management (DPM) policy should minimize power consumption while maintaining performance degradation within an acceptable level. Thus, a joint virtual machine (VM) resource allocation and power management framework is critical to the overall cloud computing system. Moreover, novel solution framework is necessary to address the even higher dimensions in state and action spaces. In this paper, we propose a novel hierarchical framework for solving the overall resource allocation and power management problem in cloud computing systems. The proposed hierarchical framework comprises a global tier for VM resource allocation to the servers and a local tier for distributed power management of local servers. The emerging deep reinforcement learning (DRL) technique, which can deal with complicated control problems with large state space, is adopted to solve the global tier problem. Furthermore, an autoencoder and a novel weight sharing structure are adopted to handle the high-dimensional state space and accelerate the convergence speed. On the other hand, the local tier of distributed server power managements comprises an LSTM based workload predictor and a model-free RL based power manager, operating in a distributed manner.