Oil & Gas


NASA's InSight lander has likely detected its first 'marsquake,' seismologists say

Los Angeles Times

It sounds like a subway train rushing by. But it's something much more exotic: in all likelihood, the first "marsquake" ever recorded by humans. NASA's InSight mission detected the quake on April 6, four months after the lander's highly sensitive seismometer was installed on the Martian surface. The instrument had previously registered the howling winds of the red planet and the motions of the lander's robotic arm. But the shaking picked up this month is believed to be the first quake from Mars' interior.


Total Plans to Use Artificial Intelligence to Cut Drilling Costs

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Total SA plans to start a digital factory in the coming weeks to tap artificial intelligence in a bid to save hundreds of millions of dollars on exploration and production projects, according to an executive. The use of artificial intelligence to screen geological data will help identify new prospects, and shorten the time to acquire licenses, drill and make discoveries, Arnaud Breuillac, head of E&P, said at a conference organized by IFP Energies Nouvelles in Paris on Friday. It will also help optimize the use of equipment and reduce maintenance costs, he said. The digital factory will employ between 200 and 300 engineers and build on successful North Sea pilot projects, Chief Executive Officer Patrick Pouyanne said at the same event. It will also be a way to attract "young talent" to the industry.


Slow burn? The long road to a zero-emissions UK

Guardian Energy

It is the near future. You wake in a house warmed by a heat pump that extracts energy from deep below the ground and delivers it to your home. You rise and make yourself a cup of tea – from water boiled on a hydrogen-burning kitchen stove. Then you head to work – in a robot-driven electric car directed by central control network to avoid traffic jams. At midday, you pause for lunch: a sandwich made of meat grown in a laboratory.


Technology Is A Huge Driver Of The U.S. Oil And Gas Boom

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A natural gas fired turbine, manufactured by Caterpillar Inc. subsidiary Solar Turbines Inc., runs a compressor at a Williston Basin Interstate Pipeline Co., a subsidiary of MDU Resources Group Inc., natural gas compression station in Bismarck, North Dakota. In the world of oil and natural gas, engineers, geologists, and drilling and production departments tend to get the lion's share of the credit when good things happen, and most of the blame when they don't. That's fair, given the crucial roles these groups of employees play within the thousands of companies that make up the U.S. oil and gas industry. But in recent years, as overall domestic production has risen at a pace no one could have foreseen even five years ago, the credit has begun to shift. These human resources remain indispensable to the success of any company, but the deployment of a raft of advancing technologies has played an ever-advancing role over time in enabling companies to maximize recoveries and profits.


Microsoft Azure CTO Russinovich sees an AI world that sounds a bit like Visual Basic

ZDNet

People who should know better light up cigarettes next to gasoline pumps. That is one surprising discovery in Microsoft's deployment of its machine learning capabilities to what's known as the "edge" of computing, in this case, at gas stations. It's conceivable the lighting of a cigarette could trigger a complex web of activity that would all be managed via functions that are akin to Microsoft's Visual Basic programming language. That reality is taking shape, as explained last week by Mark Russinovich, chief technologist for Microsoft's Azure cloud computing service. Russinovich, who has been in the CTO spot at Azure for nearly five years, and who is a 13-year veteran of the software giant, was in New York last week and spent some time talking with me about how a web of artificial intelligence and machine learning can ultimately be tied together via something that looks like VB. Russinovich has been at Microsoft for thirteen years and is chief technologist for the company's Azure cloud computing service.


fmfn/BayesianOptimization

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This is a constrained global optimization package built upon bayesian inference and gaussian process, that attempts to find the maximum value of an unknown function in as few iterations as possible. This technique is particularly suited for optimization of high cost functions, situations where the balance between exploration and exploitation is important. With the release of version 1.0.0 a number of API breaking changes were introduced. I understand this can be a headache for some, but these were necessary changes that needed to be done and ultimately made the package better. If you have used this package in the past I suggest you take the basic and advanced tours (found in the examples folder) in order to familiarize yourself with the new API.


Artificial Intelligence Can Now Manipulate Medical Images Well...

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Sometime in the early 2000s, while sitting in my dentist's chair, I began to wonder about the potential real-world pain that someone could potentially inflict on another human being simply by hacking the new digital x-ray system that the dentist had installed. Would it be possible, for example, for a hacker to modify the digital images from the x-rays so that the dentist would not be able to find and repair painful cavities, or to cause the dentist to perform an unnecessary root canal, filling, or other procedure? How certain could I be that the images of my own teeth were not tampered with? Several years later, when I had my a digital MRI after an auto accident, I wondered even further – could hackers modify images in such a manner so as to cause a person to have his head cut open to remove a tumor when, in fact, he had no tumors? Or to cause a scan to appear normal when the victim actually had a life threatening condition requiring immediate attention?


What Boston Dynamics' Rolling 'Handle' Robot Really Means

WIRED

For internet-goers, Boston Dynamics is that company that uploads insane videos of the humanoid Atlas robot doing backflips, of four-legged SpotMini opening doors and fighting off stick-wielding men, and as of last week, of a Segway-on-mescaline called Handle jetting around picking up and stacking boxes with a vacuum arm. For journalists and industry watchers, however, Boston Dynamics is that company that almost never talks about where all of this work is ultimately headed. The company is now teasing its ambitions as the four-legged SpotMini nears its commercial release. Today, Boston Dynamics is getting even more explicit about its vision with an announcement that it's acquired a Silicon Valley startup called Kinema Systems, which builds vision software that helps industrial robot arms manipulate boxes. This acquisition is giving the Handle robot the gray matter it needs to follow SpotMini to market.


AI Weekly: Will Amazon's cosponsored NSF solicitation help or warp AI research?

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This week, in what might have been construed as a gesture of goodwill, Amazon announced that it would partner with the National Science Foundation (NSF) to commit up to $10 million in research grants over the next three years to develop systems focused on "fairness in AI." But the intervening days brought debate rather than praise as researchers questioned the Seattle company's true motives -- and its methods. They quickly pointed out that Amazon would only contribute a part of the $7.6 million in total rewards, and that this portion might be provided via an "agreement" or "contract." And they noted that, before Amazon signs on the dotted line, it'll be afforded a chance to review the budget and to negotiate terms, and to provide access to Amazon researchers who would act as project advisors. But Amazon doesn't have a sterling reputation when it comes to AI fairness.


Natural, incidental, and engineered nanomaterials and their impacts on the Earth system

Science

Nanomaterials have been part of the Earth system for billions of years, but human activities are changing the nature and amounts of these materials. Hochella Jr. et al. review sources and impacts of natural nanomaterials, which are not created directly through human actions; incidental nanomaterials, which form unintentionally during human activities; and engineered nanomaterials, which are created for specific applications. Knowledge of the properties of all three types as they cycle through the Earth system is essential for understanding and mitigating their long-term impacts on the environment and human health. Natural nanomaterials have always been abundant during Earth's formation and throughout its evolution over the past 4.54 billion years. Incidental nanomaterials, which arise as a by-product from human activity, have become unintentionally abundant since the beginning of the Industrial Revolution. Nanomaterials can also be engineered to have unusual, tunable properties that can be used to improve products in applications from human health to electronics, and in energy, water, and food production. Engineered nanomaterials are very much a recent phenomenon, not yet a century old, and are just a small mass fraction of the natural and incidental varieties. As with natural and incidental nanomaterials, engineered nanomaterials can have both positive and negative consequences in our environment. Despite the ubiquity of nanomaterials on Earth, only in the past 20 years or so have their impacts on the Earth system been studied intensively. This is mostly due to a much better understanding of the distinct behavior of materials at the nanoscale and to multiple advances in analytic techniques. This progress continues to expand rapidly as it becomes clear that nanomaterials are relevant from molecular to planetary dimensions and that they operate from the shortest to the longest time scales over the entire Earth system. Nanomaterials can be defined as any organic, inorganic, or organometallic material that present chemical, physical, and/or electrical properties that change as a function of the size and shape of the material. This behavior is most often observed in the size range between 1 nm up to a few to several tens of nanometers in at least one dimension. These materials have very high proportions of surface atoms relative to interior ones. Also, they are often subject to property variation as a function of size owing to quantum confinement effects.