Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop

National Academies Of Sciences, Engineering, And Medicine; Division On Engineering And Physical Sciences; Division On Earth And Life Studies; Computer Science And Telecommunications Board; Board On Mathematical Sciences And Analytics; Ocean Studies Board; Board On Earth Sciences And Resources; Board On Atmospheric Sciences And Climate

ISBN 10: 0309688531 / ISBN 13: 9780309688536

Editore: National Academies Press, 2022

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Codice articolo mon0003395846

Dati bibliografici

• Titolo: Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop
• Editore: National Academies Press
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 0309688531
• ISBN 13: 9780309688536
• Rilegatura: paperback
• Condizione: Very Good

Descrizione articolo

Riassunto

The Earth system - the atmospheric, hydrologic, geologic, and biologic cycles that circulate energy, water, nutrients, and other trace substances - is a large, complex, multiscale system in space and time that involves human and natural system interactions. Machine learning (ML) and artificial intelligence (AI) offer opportunities to understand and predict this system. Researchers are actively exploring ways to use ML/AI approaches to advance scientific discovery, speed computation, and link scientific communities.

To address the challenges and opportunities around using ML/AI to advance Earth system science, the National Academies convened a workshop in February 2022 that brought together Earth system experts, ML/AI researchers, social and behavioral scientists, ethicists, and decision makers to discuss approaches to improving understanding, analysis, modeling, and prediction. Participants also explored educational pathways, responsible and ethical use of these technologies, and opportunities to foster partnerships and knowledge exchange. This publication summarizes the workshop discussions and themes that emerged throughout the meeting.

Table of Contents

• Front Matter
• Overview
• Introduction
• Emerging Approaches for Using, Interpreting, and Integrating ML/AI for Earth System Science
• Challenges and Risks of Using ML/AI for Earth System Science
• Identifying Future Opportunities to Accelerate Progress
• Closing Thoughts
• References
• Appendix A: Statement of Task
• Appendix B: Planning Committee Biographies
• Appendix C: Workshop Agenda