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Cost-effective and practical solutions
Much of the discussion around large language models ( LLMs ) is currently focused on their huge requirements for power and hardware , which makes both deploying and running them incredibly expensive .
Yet there are alternative ways . Implementing the appropriate language model does not always require a huge amount of investment in time and resources . Many AI models have much smaller parameter counts and are trained with specific domain expertise in mind , making them much easier to integrate with current technological infrastructures , so they are both cost-effective and scalable . This ease of integration means that even smaller research teams can leverage AI tools without the need for significant capital investment , democratizing access to cutting-edge technology .
Being tailored to specific domains also means these models can prioritize accuracy and reliability , unlike LLMs that are trained on broad corpora of data from across the internet . The power of LLMs can be combined with the cost efficiency of smaller models , and with a variety of techniques can prioritize facts and prevent hallucinations .
Researchers need to be able to trust the summarizations they are reading are valid representations of the literature as a whole . There are techniques for cross referencing factuality , such as data citation and knowledge graphs , that are not available on popular LLMs alone .
The future impact
As the technology evolves , the potential applications of GenAI in the energy , oil , and gas industry will expand . Future developments could include more sophisticated predictive models for resource exploration , automated safety monitoring systems , and real-time environmental impact assessments . The key to these advancements lies in the continuous refinement of AI algorithms and the integration of ever-more complex datasets .
While McKinsey highlights the potential for revolutionary applications of AI in the energy sector , the practical , current impacts of GenAI are both profound and transformative . As these tools become more integrated into everyday research activities , their ability to deliver timely , actionable insights will undoubtedly make them indispensable to researchers within the sector . The ongoing collaboration between AI experts and industry researchers will be crucial in realizing the full potential of GenAI , ensuring that the energy , oil , and gas sectors can meet future challenges with the best tools at their disposal . ■
For a list of the sources used in this article , please contact the editor .
Anita Schjøll Abildgaard www . iris . ai
Anita Schjøll Abildgaard is CEO and Co-founder of Iris . ai , one of the world ’ s leading start-ups in the research and development of artificial intelligence ( AI ) technologies . Founded in 2015 , the start-up offers an award-winning AI engine for scientific text understanding . The company uses natural language processing / machine learning to review massive collections of research papers or patents : find the right documents , extract all their key data , or identify the most precise pieces of knowledge . Iris . ai collaborates both with innovation-oriented universities and corporate customers and contributes to many joint research projects fostering open science and innovation .
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