Energy

Enthought instructors have doctorates in scientific fields such as physics, engineering, computer science, and mathematics, and all have extensive experience through research and consulting in applying Python to solve complex problems across a range of industries, allowing them to bring their real world experience to the classroom every day. Enthought instructors possess professional, first-hand experience with the tools and technologies covered in our courses.

Artificial intelligence, or rather things like machine learning and automation, which are often wrongly called artificial intelligence, is a big thing in oil and gas right now. The hype around AI spreads a lot further than the oil and gas industry, but in it, the technology is making the first splashes and it looks like they are fast multiplying.

BOGOTA, Colombia – The exiled opposition leader accused by Venezuelan authorities of directing a failed plot to assassinate President Nicolas Maduro says the greatest threat to the embattled socialist leader may be his detractors in uniform standing quietly behind him. Julio Borges, who once led Venezuela's opposition-controlled National Assembly, said Tuesday that the arrests of two high-ranking military officers in connection with the attack using drones loaded with plastic explosives is yet another signal that fractures within the nation's armed forces are growing. "The conflict today is within the government -- not just at the political level, but more importantly within the armed forces," Borges said in an interview with The Associated Press in Colombia's capital. His comments came hours after Venezuela's chief prosecutor announced the arrest of Gen. Alejandro Perez and Col. Pedro Zambrano from Venezuela's National Guard as part of the investigation into the Aug. 4 attack. Their alleged roles were not described.

HPE announced Tuesday that it's building a supercomputer to accelerate the federal government's basic R&D into energy efficiency. The supercomputer, dubbed "Eagle," will facilitate research within the National Renewable Energy Laboratory (NREL) -- the only federal lab dedicated to researching energy efficiency and renewable energies. NREL is funded through the US Department of Energy but run by private contractors. NREL expects to install Eagle in one of its data centers this summer and put into production in January. The machine should be more energy efficient and 3.5 times more powerful than its existing system, HPE says.

As artificial intelligence (AI) makes a "powerful impact" on other sectors, more North Sea players are expected to deploy digital innovations. Louise Sayers is head of natural resources at advisory firm BDO, whose comments come as Shell announced its commitment to the North Sea yesterday. The energy giant said it hopes to be in the region for another 50 years, as it celebrates five decades of North Sea production. Ms Sayers said this was welcome news, and said now is the time for the North Sea to "grasp digital innovation". She said: "Oil and gas companies were forced to ruthlessly cut costs and sharpen their investment filters to survive the oil price crash in 2014.

Artificial intelligence, or rather things like machine learning and automation, which are often wrongly called artificial intelligence, is a big thing in oil and gas right now. The hype around AI spreads a lot further than the oil and gas industry, but in it, the technology is making the first splashes and it looks like they are fast multiplying. While "AI"--or more accurately predictive and analytic algorithms, and automation--in the upstream segment of the industry has garnered some attention already, there is a somewhat surprising part of the oil and gas industry that may be as ripe as exploration and production for some software help: permitting and environmental assessment. Researchers from the Environmental Defense Fund are working on a system using Natural Language Processing that could streamline what is now a very complex process to the benefit of all stakeholders involved. Here's how one of the researchers, Evan Patrick, puts it: "Natural Language Processing pulls out information similar to how humans get information from reading.

The time series classification literature has expanded rapidly over the last decade, with many new classification approaches published each year. The research focus has mostly been on improving the accuracy and efficiency of classifiers, while their interpretability has been somewhat neglected. Classifier interpretability has become a critical constraint for many application domains and the introduction of the 'right to explanation' GDPR EU legislation in May 2018 is likely to further emphasize the importance of explainable learning algorithms. In this work we analyse the state-of-the-art for time series classification, and propose new algorithms that aim to maintain the classifier accuracy and efficiency, but keep interpretability as a key design constraint. We present new time series classification algorithms that advance the state-of-the-art by implementing the following three key ideas: (1) Multiple resolutions of symbolic approximations: we combine symbolic representations obtained using different parameters; (2) Multiple domain representations: we combine symbolic approximations in time (e.g., SAX) and frequency (e.g., SFA) domains; (3) Efficient navigation of a huge symbolic-words space: we adapt a symbolic sequence classifier named SEQL, to make it work with multiple domain representations (e.g., SAX-SEQL, SFA-SEQL), and use its greedy feature selection strategy to effectively filter the best features for each representation. We show that a multi-resolution multi-domain linear classifier, SAX-SFA-SEQL, achieves a similar accuracy to the state-of-the-art COTE ensemble, and to a recent deep learning method (FCN), but uses a fraction of the time required by either COTE or FCN. We discuss the accuracy, efficiency and interpretability of our proposed algorithms. To further analyse the interpretability aspect of our classifiers, we present a case study on an ecology benchmark.

Neuronal circuits formed in the brain are complex with intricate connection patterns. Such a complexity is also observed in the retina as a relatively simple neuronal circuit. A retinal ganglion cell receives excitatory inputs from neurons in previous layers as driving forces to fire spikes. Analytical methods are required that can decipher these components in a systematic manner. Recently a method termed spike-triggered non-negative matrix factorization (STNMF) has been proposed for this purpose. In this study, we extend the scope of the STNMF method. By using the retinal ganglion cell as a model system, we show that STNMF can detect various biophysical properties of upstream bipolar cells, including spatial receptive fields, temporal filters, and transfer nonlinearity. In addition, we recover synaptic connection strengths from the weight matrix of STNMF. Furthermore, we show that STNMF can separate spikes of a ganglion cell into a few subsets of spikes where each subset is contributed by one presynaptic bipolar cell. Taken together, these results corroborate that STNMF is a useful method for deciphering the structure of neuronal circuits.

See part three, where we covered how data science fits in and why data scientists are so important. The impact of incorporating new technologies is huge. Across eight industrial sectors, the value at stake is upward of $17 trillion. When machine learning is applied to industrial data, it opens up new doors for today's businesses to improve their operations and save time and money. They can predict and prevent equipment failures before they happen, improve the availability and reliability of their critical assets, and cost-optimize their maintenance programs.

Artificial intelligence, or rather things like machine learning and automation, which are often wrongly called artificial intelligence, is a big thing in oil and gas right now. The hype around AI spreads a lot further than the oil and gas industry, but in it, the technology is making the first splashes and it looks like they are fast multiplying. While "AI"--or more accurately predictive and analytic algorithms, and automation--in the upstream segment of the industry has garnered some attention already, there is a somewhat surprising part of the oil and gas industry that may be as ripe as exploration and production for some software help: permitting and environmental assessment. Researchers from the Environmental Defense Fund are working on a system using Natural Language Processing that could streamline what is now a very complex process to the benefit of all stakeholders involved. Here's how one of the researchers, Evan Patrick, puts it: "Natural Language Processing pulls out information similar to how humans get information from reading.