The National Center for High-Performance Computing (NCHC) and Academia Sinica have developed an artificial intelligence (AI) model that could help researchers predict earthquakes one day in advance. The model could predict earthquakes based on precursors to tectonic activity, researchers said. The research team, led by Academia Sinica researcher Lee Lou-chuang (李羅權) and NCHC associate researcher Tsai Tsung-che (蔡宗哲), developed an AI model using total electron content (TEC) data and the Taiwania 2 supercomputer. The model could predict a magnitude 6 or higher earthquake one day in advance by analyzing data from the previous 30 days, they said. Past studies also found that atmospheric TEC within a 50km radius of the epicenter of an earthquake show signs of change prior to a large earthquake, the Central Weather Bureau's Seismological Center said, adding that TEC above Taiwan proper was low just before the 1999 Jiji earthquake.

Climate Change is an incredibly complicated problem that humanity faces. When many variables interact with each other, it can be difficult for humans to grasp the causes and effects of the very large-scale problem of climate change. The climate is a dynamical system, where small changes can have considerable and unpredictable repercussions in the long term. Understanding how to nudge this system in the right ways could help us find creative solutions to climate change. In this research, we combine Deep Reinforcement Learning and a World-Earth system model to find, and explain, creative strategies to a sustainable future. This is an extension of the work from Strnad et al. where we extend on the method and analysis, by taking multiple directions. We use four different Reinforcement Learning agents varying in complexity to probe the environment in different ways and to find various strategies. The environment is a low-complexity World Earth system model where the goal is to reach a future where all the energy for the economy is produced by renewables by enacting different policies. We use a reward function based on planetary boundaries that we modify to force the agents to find a wider range of strategies. To favour applicability, we slightly modify the environment, by injecting noise and making it fully observable, to understand the impacts of these factors on the learning of the agents.

Artificial intelligence, or machine learning, can increase the speed and accuracy of modeling for interconnection studies for large-scale renewables projects, said Xing Wang, global leader for grid modernization for Amazon Web Services (AWS) Energy and Utilities, in a panel discussion convened by the trade group ACORE. One type of interconnection study uses a model to evaluate how a new solar generating system will affect power flow on the grid. The model predicts power flow "but it doesn't solve," Wang said, meaning it doesn't provide a solution. "You need to find out where the issues are, and that requires years of engineering experience. We have a limited number of people who know how to do that."

Alphabet's Google Cloud empowers organisations to digitally transform themselves into smarter businesses. Its diverse solutions include cloud computing, data analytics, and the latest artificial intelligence (AI) and machine learning tools. Last week, many of the platform's latest advances were shared at Next '22, Google Cloud's annual developer and tech conference about digital transformation in the cloud. We've partnered with Google Cloud over the last few years to apply our AI research for making a positive impact on core solutions used by their customers. Here, we introduce a few of these projects, including optimising document understanding, enhancing the value of wind energy, and offering easier use of AlphaFold.

A new white paper from Veritone examines how artificial intelligence can help green cryptocurrency -- one of the dirtiest industries around. Cryptocurrency mining is one of the dirtiest industries there is, according to a new white paper from Veritone. The paper says that mining for Bitcoin alone, just one of many popular cryptocurrencies, consumes seven times the total amount of energy used by Google for all of its operations. This presents an enormous challenge: How can mining operations be both good corporate stewards and keep pace with industry growth? The answer, Veritone says, is artificial intelligence (AI).

With recent advances in artificial intelligence, machine learning (ML) approaches have become an attractive tool in petroleum engineering, particularly for reservoir characterizations. A key reservoir property is hydrocarbon recovery factor (RF) whose accurate estimation would provide decisive insights to drilling and production strategies. Therefore, this study aims to estimate the hydrocarbon RF for exploration from various reservoir characteristics, such as porosity, permeability, pressure, and water saturation via the ML. We applied three regression-based models including the extreme gradient boosting (XGBoost), support vector machine (SVM), and stepwise multiple linear regression (MLR) and various combinations of three databases to construct ML models and estimate the oil and/or gas RF. Using two databases and the cross-validation method, we evaluated the performance of the ML models. In each iteration 90 and 10% of the data were respectively used to train and test the models. The third independent database was then used to further assess the constructed models. For both oil and gas RFs, we found that the XGBoost model estimated the RF for the train and test datasets more accurately than the SVM and MLR models. However, the performance of all the models were unsatisfactory for the independent databases. Results demonstrated that the ML algorithms were highly dependent and sensitive to the databases based on which they were trained. Statistical tests revealed that such unsatisfactory performances were because the distributions of input features and target variables in the train datasets were significantly different from those in the independent databases (p-value < 0.05).

The building sector has been recognized as one of the primary sectors for worldwide energy consumption. Improving the energy efficiency of the building sector can help reduce the operation cost and reduce the greenhouse gas emission. The energy management system (EMS) can monitor and control the operations of built-in appliances in buildings, so an efficient EMS is of crucial importance to improve the building operation efficiency and maintain safe operations. With the growing penetration of renewable energy and electrical appliances, increasing attention has been paid to the development of intelligent building EMS. Recently, reinforcement learning (RL) has been applied for building EMS and has shown promising potential. However, most of the current RL-based EMS solutions would need a large amount of data to learn a reliable control policy, which limits the applicability of these solutions in the real world. In this work, we propose MetaEMS, which can help achieve better energy management performance with the benefits of RL and meta-learning. Experiment results showcase that our proposed MetaEMS can adapt faster to environment changes and perform better in most situations compared with other baselines.

Knowledge underpins reasoning. Recent research demonstrates that when relevant knowledge is provided as additional context to commonsense question answering (QA), it can substantially enhance the performance even on top of state-of-the-art. The fundamental challenge is where and how to find such knowledge that is high quality and on point with respect to the question; knowledge retrieved from knowledge bases are incomplete and knowledge generated from language models are inconsistent. We present Rainier, or Reinforced Knowledge Introspector, that learns to generate contextually relevant knowledge in response to given questions. Our approach starts by imitating knowledge generated by GPT-3, then learns to generate its own knowledge via reinforcement learning where rewards are shaped based on the increased performance on the resulting question answering. Rainier demonstrates substantial and consistent performance gains when tested over 9 different commonsense benchmarks: including 5 datasets that are seen during model training, as well as 4 datasets that are kept unseen. Our work is the first to report that knowledge generated by models that are orders of magnitude smaller than GPT-3, even without direct supervision on the knowledge itself, can exceed the quality of commonsense knowledge elicited from GPT-3.

Established in 2010, Knorex is a cutting-edge advertising technology MNC with offices across USA, Australia, China, Singapore, Vietnam, India, Thailand and Malaysia. Knorex provides Precision Performance Marketing products and solutions to the world's leading brands and media agencies. With its full-stack platform, Knorex XPO (Unify to Simplify - Knorex XPO - The Ultimate Universal Marketing Platform to Power Business Growth Knorex.com)

Centralizing domain expertise while having access to detailed inspection data from many remote sites provides BP a new level of operational efficiency. BP uses Formant to collect, consolidate, and visualize all the data obtained during an inspection in a single dashboard. Formant ingests the robot telemetry, video, audio, as well as any data that comes from additional sensors mounted on Spot, such as methane detectors, thermal, infrared, ultraviolet, and hyperspectral data. Viewing all data feeds together, in context, enables operators to compare data that would not normally be contained in a single location. Formant's timeline navigation provides a simple and easy way to review and dive into particular moments of interest, even tagging an event to share with team members.