Collaborating Authors


The Amalgamation of Human Brain and Artificial Intelligence


The human brain has advanced over time in countering survival instincts, harnessing intellectual curiosity, and managing authoritative ordinances of nature. When humans got an idea about the dynamics of the environment, we started with our quest to replicate nature. While the human brain discovers ways to go beyond our physical capabilities, the combination of mathematics, algorithms, computational methods, and statistical models accumulated momentum after Alan Mathison Turing built a mathematical model for biological morphogenesis, and published a seminal paper on computing intelligence. Today, AI has developed from data models for problem-solving to artificial neural networks, a computational model predicated on the structure and functions of human biological neural networks. The brain, customarily perceived as an organ of the human body, should be understood as a biologically predicated form of artificial intelligence (AI).

Top Artificial Intelligence (AI) Companies 2019 and their success stories


Artificial Intelligence (AI) is now enjoying massive acceptance from consumers and organisations worldwide. Hence, more and more companies are stepping up their game by adopting Artificial Intelligence into their functionalities. In this article, we will discuss the absolute wins of the year 2019 in terms of breakthrough AI solutions and their impact. Here are some of the AI success stories and top news for the year 2019. In May 2019, Samsung created a system that could transform facial images into a video sequence.

Can We Achieve Early Earthquake Prediction And Warning?


Earthquakes claimed thousands of lives every decade. Of all-natural calamities, earthquake is the one which is most hard to predict. Even if a man succeeded in doing so, his predictions are vaguely based on the behavior of animals' minutes before the seismic waves hit that geographic region. However, with artificial intelligence algorithms can help us in receiving early warnings of a potential earthquake and be prepared accordingly. Using machine-learning models, seismologists can analyze hordes data on thousands of earthquakes.

California's earthquake 'swarm' triggered by fluid, scientists say

Daily Mail - Science & tech

A strange'swarm' of small earthquakes in California that lasted nearly four years was triggered by fluid spilling into the fault system from underground reservoirs, scientists say. The naturally occurring injection of underground fluid drove the earthquake swarm near Cahuilla in Southern California, which occurred in bursts around the region from early 2016 to late 2019. US scientists have made their conclusions based on earthquake detection algorithms that catalogued more than 22,000 individual seismic events that made up the'swarm'. Using machine learning to plot the location, depth and size of the tremors, the researchers generated a 3D representation of the underlying fault zone. The results suggested dynamic pressure changes from natural fluid injections deep below the surface largely controlled the prolonged evolution of the Cahuilla swarm.

Why Machine Learning is the Future of Predictive & Industrial Maintenance


There's no arguing that preventing failures and accidents is critical for industry. Unexpected incidents can grind operations to a halt for extended periods of time and necessitate expensive repairs. Just 12 hours of downtime for an oil production platform could cost six to eight million dollars in lost production opportunity alone. Because of these disruptions, industrial sectors are always on the lookout for newer, better maintenance methods, and the approach on everyone's lips right now is predictive maintenance. While everyone agrees on the name, there is less consensus on what it means or how to implement it.

Seismic waves reveal giant structures deep beneath Earth's surface

New Scientist

Seismic wave data has revealed giant structures 2900 kilometres beneath the surface of Earth, at the boundary between Earth's molten core and solid mantle. The structure, known as an ultra-low velocity (ULV) zone, is about 1000 kilometres in diameter and 25 kilometres thick, says Kim. These structures are called ULV zones because seismic waves pass through them at slower velocities, but what they are made of is still a mystery. They might be chemically distinct from Earth's iron–nickel alloy core and silicate rock mantle, or have different thermal properties. The researchers discovered the structure while analysing 7000 records of seismic activity from earthquakes that occurred around the Pacific Ocean basin between 1990 and 2018.

Using Artificial Neural Networks to Predict the Quality and Performance of Oil-Field Cements

AI Magazine

Inherent batch-to-batch variability, aging, and contamination are major factors contributing to variability in oil-field cement-slurry performance. Of particular concern are problems encountered when a slurry is formulated with one cement sample and used with a batch having different properties. Such variability imposes a heavy burden on performance testing and is often a major factor in operational failure. We describe methods that allow the identification, characterization, and prediction of the variability of oil-field cements. Our approach involves predicting cement compositions, particle-size distributions, and thickening-time curves from the diffuse reflectance infrared Fourier transform spectrum of neat cement powders.

Google pledges not to make custom software for oil and gas extraction


Google says that it will not "build custom AI/ML algorithms to facilitate upstream extraction in the oil and gas industry," the company announced on Tuesday. This represents a small but significant win for climate activists. Google's comment coincided with the release of a new Greenpeace report highlighting the role of the three leading cloud-computing services--Google Cloud, Amazon Web Services, and Microsoft Azure--in helping companies find and extract oil and gas. Greenpeace notes that extracting known fossil fuel reserves would already be sufficient to push the world over 2 degrees of warming. Uncovering additional reserves will ultimately lead to even more warming.

Machine learning picks out hidden vibrations from earthquake data


Over the last century, scientists have developed methods to map the structures within the Earth's crust, in order to identify resources such as oil reserves, geothermal sources, and, more recently, reservoirs where excess carbon dioxide could potentially be sequestered. They do so by tracking seismic waves that are produced naturally by earthquakes or artificially via explosives or underwater air guns. The way these waves bounce and scatter through the Earth can give scientists an idea of the type of structures that lie beneath the surface. There is a narrow range of seismic waves -- those that occur at low frequencies of around 1 hertz -- that could give scientists the clearest picture of underground structures spanning wide distances. But these waves are often drowned out by Earth's noisy seismic hum, and are therefore difficult to pick up with current detectors.