After six years of study, researchers from the University of Science and Technology of China (USTC) have developed the world's first artificial intelligence seismic monitoring system. This AI earthquake tracking system can report all source parameters within two seconds. The team, led by Professor Zhao Cuiping at the Earthquake Prediction Institute of China Seismological Administration, said the system underwent testing at experimental fields in the provinces of Sichuan and Yunnan for a year, where all parameters were reported in one to two seconds. It can also operate in real-time to process huge seismic network data, mitigating labor pressure and lessening false alarms. When an earthquake occurs, the wave signal is transmitted to the seismic network.
According to a new market intelligence report by BIS Research titled'Global Artificial Intelligence (AI) in Energy Market – Analysis and Forecast, 2019-2024', the artificial intelligence in energy market is expected to reach $7.78 billion by 2024. The market is projected to witness a CAGR of 22.49% from 2019 to 2024. This growth is anticipated to be driven by the demand for increasing operational efficiency, rising concern for energy efficiency, growing market penetration of decentralized power generation, and rising concern for battery storage systems. Browse more than 60 Data Tables and 150 Figures spread through 259 Pages and in-depth ToC on "Global Artificial Intelligence (AI) in Energy Market". Artificial intelligence utilizes advanced algorithms and stacks of data accumulated from the source to provide systems and machines with the ability to perceive, think, calculate, and analyze information like a human brain.
A Regina-based tech startup says it's the first company to use artificial intelligence (AI) to detect leaks at oil wells and pump jacks. In the past, oil and gas companies have used staff to drive to oil wells to inspect them for any issues, such as leaks. One solution is using remote cameras to monitor oil wells, but it results in hundreds or thousands of photos being taken that have to be inspected by employees. Founded in 2018, Wave9 takes the arduous task of inspecting those photos and hands it off to AI. Cameras and sensors placed on pump jacks are processed by the software. The user can then be alerted to issues through apps that run on tablets and smartphones.
Automated translation, including translating one programming language into another one (for instance, SQL to Python - the converse is not possible) Spell checks, especially for people writing in multiple languages - lot's of progress to be made here, including automatically recognizing the language when you type, and stop trying to correct the same word every single time (some browsers have tried to change Ning to Nong hundreds of times, and I have no idea why after 50 failures they continue to try - I call this machine unlearning) Detection of earth-like planets - focus on planetary systems with many planets to increase odds of finding inhabitable planets, rather than stars and planets matching our Sun and Earth Distinguishing between noise and signal on millions of NASA pictures or videos, to identify patterns Automated piloting (drones, cars without pilots) Customized, patient-specific medications and diets Predicting and legally manipulating elections Predicting oil demand, oil ...
The Oil and Gas industry has seen volatile times and is affected by its own set of unique challenges ranging from commodity price fluctuations, a potential supply crunch, geo-political events, and energy policies including energy transition. Moreover, the challenges and requirements are distinct at different stages of operations – upstream, midstream and downstream. The industry has been an early adopter of a few emerging technologies and is looking to leverage them to remain competitive and better employee management. Oil and Gas companies are having to clean up old processes, as the market gets increasingly competitive. Ecosystm research finds that the top business priorities for Oil and Gas companies do not stop at cost reduction and revenue growth.
Shell has a broader strategy to embed AI across its operations, a move that has helped the oil giant lower costs and avoid downtime. Other oil-and-gas companies that have tapped AI to improve operations and reduce costs include Exxon Mobil Corp., BP PLC and Chevron Corp. "Artificial intelligence enables us to process the vast quantity of data across our businesses to generate new insights which can keep us ahead of the competition," said Yuri Sebregts, Shell's chief technology officer, in an email. The initiative at Shell expands a 2019 yearlong pilot program with Udacity, based in Mountain View, Calif., that included about 250 Shell data scientists and software engineers. They picked up AI skills such as reinforcement learning, a type of machine learning where algorithms learn the correct way to perform an action based on trial-and-error and observations. Shell employees could use AI expertise, for example, to better predict equipment failures and automatically identify areas within a facility to reduce carbon emissions, said Dan Jeavons, Shell's general manager of data science.
A fundamental objective in reinforcement learning is the maintenance of a proper balance between exploration and exploitation. This problem becomes more challenging when the agent can only partially observe the states of its environment. In this paper we propose a dual-policy method for jointly learning the agent behavior and the balance between exploration exploitation, in partially observable environments. The method subsumes traditional exploration, in which the agent takes actions to gather information about the environment, and active learning, in which the agent queries an oracle for optimal actions (with an associated cost for employing the oracle). The form of the employed exploration is dictated by the specific problem.
Efficient exploration remains a challenging research problem in reinforcement learning, especially when an environment contains large state spaces, deceptive local optima, or sparse rewards. To tackle this problem, we present a diversity-driven approach for exploration, which can be easily combined with both off- and on-policy reinforcement learning algorithms. We show that by simply adding a distance measure to the loss function, the proposed methodology significantly enhances an agent's exploratory behaviors, and thus preventing the policy from being trapped in local optima. We further propose an adaptive scaling method for stabilizing the learning process. We demonstrate the effectiveness of our method in huge 2D gridworlds and a variety of benchmark environments, including Atari 2600 and MuJoCo.
The design of flow control systems remains a challenge due to the nonlinear nature of the equations that govern fluid flow. However, recent advances in computational fluid dynamics (CFD) have enabled the simulation of complex fluid flows with high accuracy, opening the possibility of using learning-based approaches to facilitate controller design. We present a method for learning the forced and unforced dynamics of airflow over a cylinder directly from CFD data. The proposed approach, grounded in Koopman theory, is shown to produce stable dynamical models that can predict the time evolution of the cylinder system over extended time horizons. Finally, by performing model predictive control with the learned dynamical models, we are able to find a straightforward, interpretable control law for suppressing vortex shedding in the wake of the cylinder.
Exploration is a fundamental challenge in reinforcement learning (RL). Many current exploration methods for deep RL use task-agnostic objectives, such as information gain or bonuses based on state visitation. However, many practical applications of RL involve learning more than a single task, and prior tasks can be used to inform how exploration should be performed in new tasks. In this work, we study how prior tasks can inform an agent about how to explore effectively in new situations. We introduce a novel gradient-based fast adaptation algorithm – model agnostic exploration with structured noise (MAESN) – to learn exploration strategies from prior experience.