AI helping Oil And Gas Sector navigate sustainability

Posted on by Jen Stirrup
AI robot arm with globe and human hands

The importance of clean and sustainable energy solutions has become increasingly prominent in the oil and gas sector. Technology is an integral factor in enabling this transformation and artificial intelligence (AI) has emerged as a critical enabler. The implementation of AI can help companies optimize production, streamline operations, reduce waste, and enhance worker safety. Utilizing data analytics and predictive models through AI can significantly improve decision-making abilities, leading to increased efficiency and better outcomes overall. Applying AI across the entire energy value chain - from upstream exploration and production to midstream transportation and downstream refining and marketing - can drive significant value creation. Attend Gastech 2023 to explore these exciting opportunities and gain valuable insights from industry leaders, tech experts, and visionaries. Be a part of the global movement driving sustainable practices and innovation for a greener and healthier future. Don't miss this global event!

Attend ADIPEC 23 to collaborate on partnerships and initiatives to decarbonize together, faster

Posted on by Jen Stirrup
Decarbonisation bricks

Join innovators, investors and energy professionals from different areas within the energy ecosystem will come together at the ADIPEC from 2-5 October in Abu Dhabi. At the nexus of different innovation and investment points, the conference will give you a view into latest techniques and creations representing tomorrow's energy to decarbonize and future-proof our energy system faster, together.

Schrodinger’s Automation in AI and the Automation Bias

Posted on by Jen Stirrup
Automation and AI

Automation is one way in which we can have human-centered Artificial Intelligence. It can relieve us from repetitive and mundane tasks so that we can make the most of our human skills and talents. That said, we may have to use those human skills and talents as a check on Artificial Intelligence and its outcomes, or perhaps to solve the problems that appear when we don't open the box. We can be said to have an Automation Bias, where we assume that technology just works.

New Year’s Resolutions for your Business Intelligence, Data Science and Artificial Intelligence teams

Posted on by Jen Stirrup

What are your New Year's Resolutions for your Business Intelligence, Data Science and AI teams? Check out this post for recommendations on getting ahead for the New Year. I'll share some New Year Resolutions that I think Business Intelligence, Data Engineering, Artificial Intelligence and Data Science teams start doing from today.

AzureML and CRISP-DM – a Framework to help the Business Intelligence professional move to AI

Posted on by Jen Stirrup

Azure ML can become a part of the data ecosystem in an organization, but this requires a mindshift from working with Business Intelligence to more advanced analytics. How can we can adopt a mindshift from Business Intelligence to advanced analytics using Azure ML? Although CRISP-DM is not perfect, the CRISP-DM framework offers a pathway for machine learning using AzureML.