气候变化
奇迹
计算机科学
温室气体
连接(拓扑)
人性
全球变暖
数据科学
政治学
心理学
生态学
数学
社会心理学
生物
组合数学
法学
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 Sasha Luccioni,Tegan Maharaj,Evan David Sherwin,S. Karthik Mukkavilli,Konrad P. Körding,Carla P. Gomes,Andrew Y. Ng,Demis Hassabis,John Platt,Felix Creutzig
出处
期刊:Zuse Institute Berlin - OPUS 4
日期:2022-02-07
被引量:712
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI