Oil & Gas



AI for business: What's going wrong, and how to get it right ZDNet

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Despite years of hype (and plenty of worries) about the all-conquering power of Artificial Intelligence (AI), there still remains a significant gap between the promise of AI and its reality for business. Tech firms have pitched AI's capabilities for years, but for most organisations, the benefits of AI remain elusive. It's hard to gauge the proportion of businesses that are effectively using artificial intelligence today, and to what extent. Adoption rates shown in recent reports fall anywhere between 20% and 30%, with adoption typically loosely defined as "implementing AI in some form". A survey led by KPMG among 30 of the Global 500 companies found that although 30% of respondents reported using AI for a selective range of functions, only 17% of the companies were deploying the technology "at scale" within the enterprise.


Artificial intelligence sustains critical infrastructure during COVID-19

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The adoption of artificial intelligence and machine learning technologies has never been more critical. Due to COVID-19, many organizations need to find a new way of working. Ensuring production rates are reliable, if not increased, while limiting the number of personnel - in some cases down to 50%. Many asset heavy industries, such as water, transportation & energy are considered critical infrastructure. Every effort needs to be made to maintain these.


The FUDIPO Project: AI systems in process industries

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FUDIPO is a project funded by the European Commission under H2020 programme, SPIRE-02-2016: "Plant-wide monitoring and control of data-intensive processes", which started on October 1st, 2016 and ends on 30th September 2020. Mälardalen University coordinates the project, and the consortium is composed of energy experts, applied mathematicians, and software engineering experts to face the SPIRE topic. The goal with FUDIPO project is to introduce AI systems into process industries. The special demands for industry are to have very robust functions and a good possibility to keep control of all functions to avoid causing new problems! This demands a structured work, but still utilising the most advanced functions to benefit from this new world, and see that European industry really stay in the forefront of production development.


The journey to edge computing for oil and gas companies

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The oil and gas industry is massive and highly-diversified in its operational characteristics between the upstream, mid-stream and downstream sectors of the industry. Even within each sector, there are distinct differences; offshore gas/oil rigs have a completely different set of requirements to onshore well pads in the fracking industry. However, every sector is susceptible to the boom and bust cycles that have traditionally characterised the oil and gas industry. All of this makes oil and gas ideal for adopting IOT technologies to address a whole range of problems and risks, and to smooth out the ups and downs of the business cycle. Where are oil and gas companies today with edge computing adoption?


Promising artificial intelligence startup ideas for 2020

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AI startups are an area that has been growing for the past several years. This tech has applications in dozens of professions and niches the world over. As reported by Statista, the market research firm Tractica stated that in 2019, the global AI software market was expected to increase 154 per cent, with a forecast worth approximately 14.7 billion US dollars. This is just one of many stats indicating that an AI startup would be a smart enterprise in which you might invest. If you're wondering about the benefits of AI companies, there are many.


Council Post: How AI Makes Big Data Smarter

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The artificial intelligence (AI) road from unlimited (yet largely nonspecific) potential to concrete, specific business benefits, is like taking a long road trip with kids -- palpable excitement alternated with restless tension and cries of "Are we there yet?" So, are we "there" yet? Well, no, but we are certainly closer than we have been, and by further examining the data that underlies these systems, we can progress closer to "there" by recognizing measurable ROI from the ability to make better decisions with AI-powered analytics technologies. Most companies that say they are using AI have yet to gain any value from their investment, according to the 2019 Artificial Intelligence Global Executive Study and Research Report from MIT Sloan Management Review and Boston Consulting Group (BCG). They continue to plug away, however, even though the payoff -- new products, increased revenues and optimized efficiencies -- is likely further out than previously imagined.


RetinaNet: how Focal Loss fixes Single-Shot Detection

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Neural networks can be used to solve classification problems (predict classes) and regression problems (predict continuous values). Today we will be doing both at the same time. We start with a simplified task: detect and classify one single object in an image instead of several objects. How does an object detection dataset look like? Well, the inputs to our model are of course images and the labels are typically four values that describe a ground truth bounding box plus a category the object in this box belongs two.


C3.ai lands IBM partnership and more customers for its artificial intelligence and IoT platform ZDNet

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There are plenty of tools and point solutions that address bits and pieces of the challenge of delivering artificial intelligence (AI) and Internet of things (IoT) applications. C3.ai's focus is on delivering an end-to-end platform for developing, deploying and running these applications in production at scale. Whether customers use every aspect of the C3.ai platform or not, big enterprise-scale companies seem to be attracted by that promise of quickly developing and running innovative, data-driven applications at scale. There was plenty of evidence of that fact at C3.ai's February 25-27 Transform conference in San Francisco, where customers including Bank of America, Shell, 3M and Engie detailed their deployments. C3.ai's cloud-first platform is comprehensive, addressing the needs of developers, data engineers and data scientists, and the operational teams challenged with bringing applications into production at scale.


Promising artificial intelligence startup ideas for 2020

#artificialintelligence

AI startups are an area that has been growing for the past several years. This tech has applications in dozens of professions and niches the world over. As reported by Statista, the market research firm Tractica stated that in 2019, the global AI software market was expected to increase 154 per cent, with a forecast worth approximately 14.7 billion US dollars. This is just one of many stats indicating that an AI startup would be a smart enterprise in which you might invest. If you're wondering about the benefits of AI companies, there are many.