Название: Artificial Intelligence and Systems of the Earth
Автор: Michel Speiser
Издательство: CRC Press
Год: 2024
Страниц: 114
Язык: английский
Формат: pdf (true), epub
Размер: 64.2 MB

Artificial Intelligence and Systems of the Earth is a book about the potential and capabilities of Artificial Intelligence (AI) and Machine Learning (ML) for studying the Earth. It aims to serve as an eye-opener on new avenues of scientific research that can be enabled by AI/ML. This is not meant to be a ‘how to’ book but is written to answer the question ‘what if’. It explains how these tools are currently being applied, and the new opportunities they have opened. Through many examples and application ideas from outside the Earth Sciences, the book discusses some of the most prevalent types of AI in current use, the future of AI hardware, and how AI/ML bring about change. Features Provides accessible and compact coverage on the many uses AI in Earth Science. Covers AI, Deep Learning, and causal modeling concepts in an easy-to-understand language. Contains a chapter on Generative AI and its specific strengths and challenges. Includes descriptions of computer hardware for AI and where it is headed. Offers a companion website with regularly updated content. This book is an excellent resource for researchers, academics, graduate, and senior undergraduate students in Earth Science and Environmental Science and Engineering, who wish to learn how AI and ML can benefit them, its potential applications, and capabilities.

Artificial Intelligence, Machine Learning, and Deep Learning are terms that many scientists have seen appear and grow in the practice of their discipline, including those who work to understand the Earth and its many systems and processes. The reasons for this AI ‘wave’ are numerous, chief among them the fast progress in predictive ability achieved by Deep Learning since the early 2010s, which can deal with images, text, as well as numerical and other data types. This versatility also extends to the set of problem domains; AI/ML techniques are almost as widely applicable as computing itself, and wherever there is data to be learnt from, the odds are that some Deep Learning model is learning from it. We cover key concepts and definitions of these new technologies in Chapter 2.




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