Every industry faces operational challenges on an everyday basis, whether it is due to machine downtime or equipment failure. However, the latest advents in technology like artificial intelligence; IoT, etc. help industries to tackle such challenges efficiently. After witnessing this, the oil and gas industry has finally started the integration of these technologies in its operations. There are various applications of artificial intelligence for different industries. Out of which, the main applications of AI for the oil and gas industry are machine learning (ML) and data science.

Houston, Texas-based Innowatts, is a startup that has developed an automated toolkit for energy monitoring and management. Innowatts says its machine learning algorithms can analyze the data to forecast several critical data points, including short- and long-term loads, variances, weather sensitivity, and more.

With all the buzz around digital currency and blockchain innovation, artificial intelligence (AI) has taken a lower priority somehow or another. In any case, this hasn't prevented the AI business from developing at an exponential rate. The artificial intelligence market is evaluated to develop a value to $191 billion by 2024 with a CAGR of 37%. Artificial intelligence is as yet a power. The capacity for machines to settle on decisions dependent on rationale, information, and data from the past is affecting various enterprises.

This piece has been published as part of Slate's partnership with Covering Climate Now, a global collaboration of more than 250 news outlets to strengthen coverage of the climate story. Artificial intelligence is getting smarter, but it isn't getting cleaner. In order to improve at predicting the weather, sorting your social media feeds, and hailing your Uber, it needs to train on massive datasets. A few years ago, an A.I. system might have required millions of words to attempt to learn a language, but today that same system could be processing 40 billion words as it trains, according to Roy Schwartz, who researches deep learning models at the Allen Institute for Artificial Intelligence and in the University of Washington's computer science and engineering department. All that processing takes a lot of energy.

The Azeri-Chirag-Deepwater Gunashli (ACG), a sprawling complex of offshore oil fields 60 miles off Azerbaijan's capital Baku, is causing somewhat of a headache for BP's head of technology. "We have huge production in Azerbaijan of wells that are quite prone to producing sand, and sand if it's produced in high quantities from our oil wells can do damage to the metalwork and also choke back the production," says David Eyton. The ACG, which pumps out an average of 584,000 barrels of oil per day, is a prized asset for BP, and any hold ups could cost the company dearly. But the man leading BP's technology revolution think he has a solution: artificial intelligence (AI).

Artificial intelligence, or rather things like machine learning and automation, which are often wrongly called artificial intelligence, is a big thing in oil and gas right now. The hype around AI spreads a lot further than the oil and gas industry, but in it, the technology is making the first splashes and it looks like they are fast multiplying. While "AI"--or more accurately predictive and analytic algorithms, and automation--in the upstream segment of the industry has garnered some attention already, there is a somewhat surprising part of the oil and gas industry that may be as ripe as exploration and production for some software help: permitting and environmental assessment. Researchers from the Environmental Defense Fund are working on a system using Natural Language Processing that could streamline what is now a very complex process to the benefit of all stakeholders involved. Here's how one of the researchers, Evan Patrick, puts it: "Natural Language Processing pulls out information similar to how humans get information from reading.

Artificial intelligence, or rather things like machine learning and automation, which are often wrongly called artificial intelligence, is a big thing in oil and gas right now. The hype around AI spreads a lot further than the oil and gas industry, but in it, the technology is making the first splashes and it looks like they are fast multiplying. While "AI"--or more accurately predictive and analytic algorithms, and automation--in the upstream segment of the industry has garnered some attention already, there is a somewhat surprising part of the oil and gas industry that may be as ripe as exploration and production for some software help: permitting and environmental assessment. Researchers from the Environmental Defense Fund are working on a system using Natural Language Processing that could streamline what is now a very complex process to the benefit of all stakeholders involved. Here's how one of the researchers, Evan Patrick, puts it: "Natural Language Processing pulls out information similar to how humans get information from reading.

The global shipping industry has changed more in the past few years than the previous half of a decade. Artificial intelligence is one of the biggest factors that is spearheading the evolution of this timeless industry. Mike Konstantinidis, The CEO of METIS Cybertechnology provided an especially insightful critique of the impact AI has had on the industry. Konstantinidis points out that the shipping industry was reluctant to invest in new communication technologies during the late 1990s and early 21st century. Artificial intelligence has made it easier for these companies to seamlessly integrate new shipping logistics and communication technology into their business models.

Artificial intelligence is powering more and more of the things we interact with every day, from our gadgets to our cars. But it's also playing a growing role in how society's undergirding resources -- energy, food, and water -- are sourced, secured, and delivered. In this three-part series, we'll consider how AI is being used to make those resources more environmentally and financially sustainable. AI is not a discrete technology, but rather a school of powerful and widely applicable data science tools, which include machine learning, pattern recognition, and natural language processing. All of these tools can squeeze far more useful information out of data, and more quickly than humans could reasonably do on their own.