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Artificial Intelligence and Systems of the Earth◆JpGU-AGU Joint Meeting 2026 セール開催中！：2026年6月30日(火)ご注文分まで
※上記表示の販売価格は割引適用後の価格です 出版済み 3-5週間でお届けいたします。 Title: Artificial Intelligence and Systems of the Earth Author: Speiser, Michel (International Centre for Earth Simulation (ICES) Foundation) Publisher: Taylor & Francis ISBN: 9781032710501 Cover: HARDCOVER Date: 2024年10月 こちらの商品は学校・法人様向け（機関契約）のオンラインブック版がございます。 オンラインブックの価格、納期につきましては弊社営業員または当ECサイトよりお問い合わせください。  DESCRIPTION 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 generat ive 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. The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons (CC-BY) 4.0 license. TABLE OF CONTENTS 1. Introduction. 2. AI refresher. 3. Current and future applications of AI in Earth-related sciences. 4. AI and challenges in Earth-related sciences. 5. AI hardware and quantum computing. 6. Why believe AI? The role of machine learning in science. 7. Generative AI. 8. Causal models: AI that asks ‘why’ and ‘what if’. 9. Conclusion.
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