Oil and Gas Transactions Require Special Cybersecurity Considerations
One of the foremost threats companies face today is that posed by cybercriminals, and the unique vulnerabilities of companies in the oil and gas sector create heightened cybersecurity risks for those pursuing transactions in the sector.
As Oil and Gas Data Multiply, so Do the Cybersecurity Threats
Saudi Aramco, BP, and Schlumberger pride themselves on staying at the forefront of digital technology development and deployment. But an equally daunting challenge for the industry heavyweights is keeping their ever-expanding digital systems secure.
Internet of Things Strategies for the Energy Sector
Whether thinking about managing oil and gas or other infrastructure facilities or considering industrial efficiency, you may be pondering how the Internet of things can be used. Forward-thinking strategies include not just staying on top of regulatory changes but also influencing them.
Why Data Visualization Is the Most Important Skill in a Data Analyst's Arsenal
Visually displaying data makes it much more accessible, and this is critical for identifying the weaknesses of an organization, accurately forecasting trading volumes and sale prices, and making the right business choices.
Statistical Modeling vs. Machine Learning: What’s the Difference?
At times, it may seem that machine learning can be performed without a sound statistical background, but this does not take in to account many difficult nuances. Code written to make machine learning easier does not negate the need for an in-depth understanding of the problem.
IBM Reveals New Hack To Infiltrate Corporate Networks
Nicknamed “warshipping,” the hacking technique allows remote infiltration of corporate networks by hiding a remote-controlled scanning device designed to penetrate a wireless network inside a package.
Photogrammetry Offers a Snapshot of a Digitalized Future
Photogrammetry—stitching together images to create photorealistic 3D models—can be part of a larger industrial digitalization strategy that aims to liberate data from its silos, connect it to other relevant information, and make it available to the workers who need it.
Hamiltonian Neural Networks Show Benefits Over Regular Neural Networks
Hamiltonian neural networks draw inspiration from Hamiltonian mechanics, a branch of physics concerned with conservation laws and invariances. By construction, these models learn conservation laws from data, revealing major advantages over regular neural networks on a variety of physics problems.
Random Forests Vs. Neural Networks: Which Is Better and When?
Random Forest and Neural Network are the two widely used machine-learning algorithms. What is the difference between the two approaches? When should one use Neural Network or Random Forest?
Katy, Texas, Is Now Home to One of the World’s Fastest Supercomputers
The 21st century oil and gas industry thrives on hardcore computing power, crunching data derived from seismic testing to find oil deep in the ground and below the sea. Now, an Australian company will fire up a digital behemoth in a data center near the Houston suburb.
Data Science vs. Decision Science
Data science and decision science are related but still separate fields, so, at some points, it might be hard to compare them directly. This article attempts to show the commonalities, differences, and specific features of data science and decision science.
Bias Can Cause Machine Learning To Stumble
Machine learning (ML) finds patterns in data. "AI bias" means that it might find the wrong patterns. Meanwhile, the mechanics of ML might make this hard to spot.
Data as Prior/Innate Knowledge for Deep Learning Models
Rapid advances in deep learning continue to demonstrate the significance of end-to-end training with no a priori knowledge. However, when models need to do forward prediction, most AI researchers agree that incorporating prior knowledge with end-to-end training can introduce better inductive bias.
The Path to Artificial Super Intelligence
Dubbed the technology of the decade, AI has been the catchphrase on every futurist’s tongue. From customer support chatbots to smart assistants, AI has begun to transform numerous industry verticals.
Souped-Up Search Engines Wrangle Drilling, Completions Data
Fed by big data loads from big operators, a university consortium and software firm are each working to make upstream data access as quick and easy as a Google search.
Is the Cloud Mature Enough for High-Performance Computing?
Data volumes are growing at an exponential rate. How can high-performance computing solutions help operators manage these volumes? Will faster, stronger processors and cloud computing solutions be the answer?
What Difference Does 99% Accuracy Make Over 95%? Not Much
Instinctively, we feel that greater accuracy is better and all else should be subjected to this overriding goal. This is not so. While there are a few tasks for which a change in the second decimal place in accuracy might actually matter, for most tasks, this improvement will be irrelevant.
Telling a Good Data Story Through Visualization
While the visual element is key, the core strategic component of data visualization is the ability to unlock the story in the data.
Digital Transformation Is Changing the Face of Visual Inspection
As we move to digitize our visual inspections with a variety of image-capture devices, fully understanding the strengths and limitations of the approach is important to move truly from a qualitative to a quantitative assessment with confidence.
Statistics: P Values Are Just the Tip of the Iceberg
Ridding science of shoddy statistics will require scrutiny of every step, not merely the last one.
Machine Learning vs. Data Science: What's the Difference?
When you think of “data science” and “machine learning,” do the two terms blur together? This article will clarify some important and often-overlooked distinctions between the two to help better focus learning and hiring.
Functional Data Engineering: A Modern Paradigm for Batch Data Processing
Batch data processing is extremely challenging. It’s time-consuming, brittle, and often unrewarding. This story explores how applying the functional programming paradigm to data engineering can bring clarity to the process.
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16 October 2019
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11 October 2019