Language Models for Geoscience Applications

Course Description

This course will explore the potential of Generative AI (Gen-AI) for geoscience. By examining the key concepts of large language models, and real-world applications of them, participants will gain insights into how these cutting-edge technologies are being used to solve complex geoscience challenges. The course material is aimed at geoscientists that are looking to use AI applications and want a better understanding of how they work, how to get the best out of them and how to critically evaluate their performance.

The course will begin by covering the basic concepts for understanding generative AI and Large Language Models (LLMs), including data embedding, benchmarking, and the mechanics of transformer architectures. The second section of the course will take a deeper look into advanced techniques and methodologies, including retrieval augmented generation (RAG), agents, and improving model results through prompting and grounding. 

Finally, the participants will apply the course content to examine critical discussions for the ethical use of generative AI, cybersecurity concerns, and the necessary regulatory frameworks governing AI deployment in geoscience.

Two group discussion sessions during the day include problem-solving tasks that apply the course material to real-world problems. 

In the expanded two day course, we will spend more time covering the fundamental concepts behind LLMs, delve deeper into several of the topics and expand with further details around data types and data processing.