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Herausgeber: 
  • Rachel Silvern
    Autor(en): 
  • Ocean Studies Board
  • Board on Earth Sciences and Resources
  • Division on Earth and Life Studies
  • Division on Engineering and Physical Sciences
  • Computer Science and Telecommunications Board
  • National Academies of Sciences Engineering and Med
  • Board on Atmospheric Sciences and Climate
  • Board on Mathematical Sciences and Analytics
  • Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunities and Challenges: Proceedings of a Workshop 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  Juli 2022  
    Genre:  Naturwissensch., Medizin, Technik 
     
    Computers - General Information / COMPUTERS / Information Technology / SCIENCE / Earth Sciences / General
    ISBN:  9780309688536 
    EAN-Code: 
    9780309688536 
    Verlag:  National Academies Press 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 249 mm / B 173 mm / D 5 mm 
    Gewicht:  159 gr 
    Seiten:  68 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:

    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.

      



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