Tech giants Apple (AAPL), Alphabet (GOOGL), Facebook (FB), and Microsoft (MSFT) have raced to apply artificial intelligence to their businesses, and the oil industry is starting to seize on AI's benefits too. The reason interest is surging now is because artificial intelligence is "actually doable," he said in an interview with IBD at CERAWeek, explaining that advancements in cloud computing and infrastructure have made AI more affordable and accessible. "The industrial world is waking up to best practices," he said. "They are all waking up to it." Several heavyweights in the energy industry are already investors in his company, including General Electric (GE), Chevron (CVX), Royal Dutch Shell (RDSA) and Saudi Aramco.

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Japanese farmers are using terrifying robotic wolves with beaming red LED eyes to scare off wild boars, deer and other pests from grazing on the country's rice and chestnut crops. The "Super Monster Wolf" stands at 50cm tall, is 65cm long and runs on rechargeable solar-batteries, using motion-sensors to detect when other mammals approach and letting out an alarming primal howl in response. The robo-wolf can cover distances of up to half a mile and has been used in trials to patrol fields near Kisarazu City, Chiba, as a deterrent to pests, effectively acting as a moving scarecrow. In addition to its satanic stare, the creature features a realistic fur hide and snarling rubber jaws. Chikao Umezawa of the Japan Agricultural Cooperative, the man who commissioned it, said he has seen a significant drop in the number of crops devoured by animals since the beast was unleashed.

Natural human flaws can have severe impacts on business with lasting damage – 82% of operational asset failures are attributed to human performance. Indeed, a recent study by ARC Advisory Group found that the global process industry loses up to $20 billion a year due to unscheduled downtime – or $12,500 hourly, on average. However, machine learning is helping eliminate these costly flaws and is helping transform the manufacturing industry. This technology, along with others like big data analytics, are able to predict if and when something will break – cancelling the possibility of costly downtime. See also: Anticipating downtime will be business' next competitive advantage Seth Page is a cognitive computing veteran and industrial IoT pioneer based in Washington DC, and is CEO and co-founder of DataRPM, a Progress company.

In 2010 I wrote that there were three sponsored research projects to solve the problem of safely inspecting and maintaining high voltage transmission lines using robotics. Existing 2010 methods ranged from humans crawling the lines, to helicopters flying close-by and scanning, to cars and jeeps with people and binoculars attempting to scan with the human eye. In 2014 I described the progress from 2010 including the Japanese start-up HiBot and their inspection robot Expliner which seemed promising. This project got derailed by the Fukushima disaster which took away the funding and attention from Tepco which was forced to refocus all its resources on the disaster. HiBot later sold their IP to Hitachi High-Tech which, thus far, hasn't reported any progress or offered any products.

A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the physical processes governing the dynamics to build an approximate mathematical model of the system. In contrast, machine learning techniques have demonstrated promising results for forecasting chaotic systems purely from past time series measurements of system state variables (training data), without prior knowledge of the system dynamics. The motivation for this paper is the potential of machine learning for filling in the gaps in our underlying mechanistic knowledge that cause widely-used knowledge-based models to be inaccurate. Thus we here propose a general method that leverages the advantages of these two approaches by combining a knowledge-based model and a machine learning technique to build a hybrid forecasting scheme. Potential applications for such an approach are numerous (e.g., improving weather forecasting). We demonstrate and test the utility of this approach using a particular illustrative version of a machine learning known as reservoir computing, and we apply the resulting hybrid forecaster to a low-dimensional chaotic system, as well as to a high-dimensional spatiotemporal chaotic system. These tests yield extremely promising results in that our hybrid technique is able to accurately predict for a much longer period of time than either its machine-learning component or its model-based component alone.

Dark matter in the universe evolves through gravity to form a complex network of halos, filaments, sheets and voids, that is known as the cosmic web. Computational models of the underlying physical processes, such as classical N-body simulations, are extremely resource intensive, as they track the action of gravity in an expanding universe using billions of particles as tracers of the cosmic matter distribution. Therefore, upcoming cosmology experiments will face a computational bottleneck that may limit the exploitation of their full scientific potential. To address this challenge, we demonstrate the application of a machine learning technique called Generative Adversarial Networks (GAN) to learn models that can efficiently generate new, physically realistic realizations of the cosmic web. Our training set is a small, representative sample of 2D image snapshots from N-body simulations of size 500 and 100 Mpc. We show that the GAN-produced results are qualitatively and quantitatively very similar to the originals. Generation of a new cosmic web realization with a GAN takes a fraction of a second, compared to the many hours needed by the N-body technique. We anticipate that GANs will therefore play an important role in providing extremely fast and precise simulations of cosmic web in the era of large cosmological surveys, such as Euclid and LSST.

There is growing evidence regarding the importance of spike timing in neural information processing, with even a small number of spikes carrying information, but computational models lag significantly behind those for rate coding. Experimental evidence on neuronal behavior is consistent with the dynamical and state dependent behavior provided by recurrent connections. This motivates the minimalistic abstraction investigated in this paper, aimed at providing insight into information encoding in spike timing via recurrent connections. We employ information-theoretic techniques for a simple reservoir model which encodes input spatiotemporal patterns into a sparse neural code, translating the polychronous groups introduced by Izhikevich into codewords on which we can perform standard vector operations. We show that the distance properties of the code are similar to those for (optimal) random codes. In particular, the code meets benchmarks associated with both linear classification and capacity, with the latter scaling exponentially with reservoir size.

With the increasing penetration of solar power into power systems, forecasting becomes critical in power system operations. In this paper, an hourly-similarity (HS) based method is developed for 1-hour-ahead (1HA) global horizontal irradiance (GHI) forecasting. This developed method utilizes diurnal patterns, statistical distinctions between different hours, and hourly similarities in solar data to improve the forecasting accuracy. The HS-based method is built by training multiple two-layer multi-model forecasting framework (MMFF) models independently with the same-hour subsets. The final optimal model is a combination of MMFF models with the best-performed blending algorithm at every hour. At the forecasting stage, the most suitable model is selected to perform the forecasting subtask of a certain hour. The HS-based method is validated by 1-year data with six solar features collected by the National Renewable Energy Laboratory (NREL). Results show that the HS-based method outperforms the non-HS (all-in-one) method significantly with the same MMFF architecture, wherein the optimal HS- based method outperforms the best all-in-one method by 10.94% and 7.74% based on the normalized mean absolute error and normalized root mean square error, respectively.

Increasing energy efficiency in buildings can reduce costs and emissions substantially. Historically, this has been treated as a local, or single-agent, optimization problem. However, many buildings utilize the same types of thermal equipment e.g. electric heaters and hot water vessels. During operation, occupants in these buildings interact with the equipment differently thereby driving them to diverse regions in the state-space. Reinforcement learning agents can learn from these interactions, recorded as sensor data, to optimize the overall energy efficiency. However, if these agents operate individually at a household level, they can not exploit the replicated structure in the problem. In this paper, we demonstrate that this problem can indeed benefit from multi-agent collaboration by making use of targeted exploration of the state-space allowing for better generalization. We also investigate trade-offs between integrating human knowledge and additional sensors. Results show that savings of over 40% are possible with collaborative multi-agent systems making use of either expert knowledge or additional sensors with no loss of occupant comfort. We find that such multi-agent systems comfortably outperform comparable single agent systems.