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Training a single AI model can emit as much carbon as five cars in their lifetimes – MIT Technology Review

#artificialintelligence

The artificial-intelligence industry is often compared to the oil industry: once mined and refined, data, like oil, can be a highly lucrative commodity. Now it seems the metaphor may extend even further. Like its fossil-fuel counterpart, the process of deep learning has an outsize environmental impact. In a new paper, researchers at the University of Massachusetts, Amherst, performed a life cycle assessment for training several common large AI models. They found that the process can emit more than 626,000 pounds of carbon dioxide equivalent--nearly five times the lifetime emissions of the average American car (and that includes manufacture of the car itself). It's a jarring quantification of something AI researchers have suspected for a long time.


Preprocessing approaches in machine-learning-based groundwater potential mapping: an application to the Koulikoro and Bamako regions, Mali

#artificialintelligence

Abstract. Groundwater is crucial for domestic supplies in the Sahel, where the strategic importance of aquifers will increase in the coming years due to climate change. Groundwater potential mapping is a valuable tool to underpin water management in the region and, hence, to improve drinking water access. This paper presents a machine learning method to map groundwater potential. This is illustrated through its application in two administrative regions of Mali. A set of explanatory variables for the presence of groundwater is developed first. Scaling methods (standardization, normalization, maximum absolute value and max–min scaling) are used to avoid the pitfalls associated with reclassification. Noisy, collinear and counterproductive variables are identified and excluded from the input dataset. A total of 20 machine learning classifiers are then trained and tested on a large borehole database (n=3345) in order to find meaningful correlations between the presence or absence of groundwater and the explanatory variables. Maximum absolute value and standardization proved the most efficient scaling techniques, while tree-based algorithms (accuracy >0.85) consistently outperformed other classifiers. The borehole flow rate data were then used to calibrate the results beyond standard machine learning metrics, thereby adding robustness to the predictions. The southern part of the study area presents the better groundwater prospect, which is consistent with the geological and climatic setting. Outcomes lead to three major conclusions: (1) picking the best performers out of a large number of machine learning classifiers is recommended as a good methodological practice, (2) standard machine learning metrics should be complemented with additional hydrogeological indicators whenever possible and (3) variable scaling contributes to minimize expert bias.

What's behind the Houthis' attack on the UAE?

Al Jazeera

At least three killed in drone attacks on oil facilities near Abu Dhabi airport.


Brent oil jumps to highest level in 7 years

Al Jazeera

Brent oil surged to the highest level in seven years as robust demand and strained supplies make physical markets run hot in the world's largest consuming region. Futures in London surged to $88.13 a barrel, the highest since October 2014. Traders are paying higher and higher premiums for cargoes in Asia, as fears fade over the demand impact from omicron, while supplies are tightened by a range of outages from Libya to North America. A drone attack on oil facilities in the UAE on Monday flared geopolitical risks. Goldman Sachs Group Inc. raised its Brent forecasts through 2022 and 2023 and predicted $100 oil in the third quarter.


Deadly drone strikes on UAE raise Gulf tensions and roil oil market

The Japan Times

Iran-backed Yemeni fighters launched drone strikes on the United Arab Emirates that caused explosions and a deadly fire outside the capital, Abu Dhabi, ratcheting up security risks in the major oil-exporting region at a critical time. One of the biggest attacks to date on UAE soil ignited a fire at Abu Dhabi's main international airport on Monday and set fuel tanker trucks ablaze in a nearby industrial area. It took place days after Yemen's Houthi fighters warned Abu Dhabi against intensifying its air campaign against them. Crude extended gains to the highest level in seven years on Tuesday after the assaults in the UAE, OPEC's third biggest oil producer. Iran's longtime support of the Houthis means the incidents could roil regional diplomatic efforts to ease frictions and separate talks to restore Tehran's 2015 nuclear deal with world powers.


Suspected drone attack in Abu Dhabi kills 3 and wounds 6

FOX News

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A possible drone attack may have sparked an explosion that struck three oil tankers in Abu Dhabi and another fire at an extension of Abu Dhabi International Airport on Monday that killed three people and wounded six, police said. Abu Dhabi police identified the dead as two Indian nationals and one Pakistani. It did not identify the wounded, who police said suffered minor or moderate wounds.


10 Ways Computer Vision is Used in Smart Cities in 2022

#artificialintelligence

Smart cities use a mix of low-power sensors, cameras, and AI algorithms to continuously monitor the city's efficiency. Governments benefit greatly from the use of computer vision and other related technologies. These technologies allow city administrators to easily integrate and manage assets. As the'eyes' of the city, computer vision plays an important role in smart city management. Greater urban density usually means more automobiles, which means more traffic congestion, longer travel times, accidents, local air pollution, and carbon emissions – not to mention a general sensation of exhaustion, tension, and anxiety.


Artificial-Intelligence and Machine-Learning Technique for Corrosion Mapping

#artificialintelligence

The complete paper discusses risk reduction and increased fabric-maintenance (FM) efficiency using artificial-intelligence (AI) and machine-learning (ML) algorithms to analyze full-facility imagery for atmospheric corrosion detection and classification. With this tool, a comprehensive and objective analysis of a facility's health is achievable in a matter of weeks from the time of data collection. This application of AI and ML is a novel approach aimed at gaining a comprehensive understanding of facility-coating integrity and external corrosion threats. Atmospheric corrosion is the most-significant asset-integrity threat in the Gulf of Mexico (GOM). Offshore facilities require constant inspection and FM--and the significant financial obligation of these activities--to stay ahead of rapid equipment degradation.


Balancing Collective Exploration and Exploitation in Multi-Agent and Multi-Robot Systems: A Review

#artificialintelligence

Multi-agent systems and multi-robot systems have been recognized as unique solutions to complex dynamic tasks distributed in space. Their effectiveness in accomplishing these tasks rests upon the design of cooperative control strategies, which is acknowledged to be challenging and nontrivial. In particular, the effectiveness of these strategies has been shown to be related to the so-called exploration--exploitation dilemma: i.e., the existence of a distinct balance between exploitative actions and exploratory ones while the system is operating. Recent results point to the need for a dynamic exploration--exploitation balance to unlock high levels of flexibility, adaptivity, and swarm intelligence. This important point is especially apparent when dealing with fast-changing environments. Problems involving dynamic environments have been dealt with by different scientific communities using theory, simulations, as well as large-scale experiments. Such results spread across a range of disciplines can hinder one's ability to understand and manage the intricacies of the exploration--exploitation challenge. In this review, we summarize and categorize the methods used to control the level of exploration and exploitation carried out by an MAS. Lastly, we discuss the critical need for suitable metrics and benchmark problems to quantitatively assess and compare the levels of exploration and exploitation, as well as the overall performance of a system with a given cooperative control al...


Reinforcement Learning: Learn from your mistakes

#artificialintelligence

Reinforcement learning stands for the training of a system by rewarding its best responses. Due to its elegance, this algorithm has been becoming more and more popular in the past decade. Reinforcement learning (RL) is the trial-and-error process of finding out how to act in an environment to maximize cumulative reward. The trial consists of some action performed by the agent followed by feedback about the effects of the action (reward or punishment). Reinforcement learning differs from standard supervised machine learning problems like classification. A training set provides input samples and desired output labels; no such target outputs are specified for reinforcement learning problems.