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arXiv 1906.05433cs.CYcs.AIcs.LGstat.ML
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用机器学习应对气候变化

Tackling Climate Change with Machine Learning

University of Pennsylvania(宾夕法尼亚大学);Carnegie Mellon University(卡内基梅隆大学);ETH Zürich(苏黎世联邦理工学院);University of Colorado Boulder(科罗拉多大学博尔德分校);Element AI;Mila;Université de Montréal(蒙特利尔大学);Harvard University(哈佛大学);Mercator Research Institute on Global Commons and Climate Change(墨卡托全球公域与气候变化研究所);Technische Universität Berlin(柏林工业大学);Massachusetts Institute of Technology(麻省理工学院);École Polytechnique de Montréal(蒙特利尔高等理工学院);Cornell University(康奈尔大学);Stanford University(斯坦福大学);DeepMind;Google AI(谷歌AI)
David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, An… 展开作者

David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Luccioni, Tegan Maharaj, Evan D. Sherwin, S. Karthik Mukkavilli, Konrad P. Kording, Carla Gomes, Andrew Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer Chayes, Yoshua Bengio

摘要:

气候变化是人类面临的最大挑战之一,作为机器学习专家,我们可能想知道如何提供帮助。在这里,我们描述了机器学习如何成为减少温室气体排放和帮助社会适应气候变化的强大工具。从智能电网到灾害管理,我们确定了高影响力的问题,其中现有的差距可以通过机器学习与其他领域合作来填补。我们的建议包括令人兴奋的研究问题以及有前途的商业机会。我们呼吁机器学习社区加入全球应对气候变化的努力。

英文摘要:

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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