TY - JOUR A1 - Kaack, Lynn A1 - Rolnick, David A1 - Donti, Priya L. A1 - Kochanski, Kelly A1 - Lacoste, Alexandre A1 - Sankaran, Kris A1 - Ross, Andrew S. A1 - Milojevic-Dupont, Nikola A1 - Jaques, Natasha A1 - Waldman-Brown, Anna A1 - Luccioni, Alexandra S. A1 - Maharaj, Tegan A1 - Sherwin, Evan D. A1 - Mukkavilli, Karthik A1 - Kording, Konrad P. A1 - Gomes, Carla P. A1 - Ng, Andrew Y. A1 - Hassabis, Demis A1 - Platt, John C. A1 - Creutzig, Felix A1 - Chayes, Jennifer A1 - Bengio, Yoshua T1 - Tackling Climate Change with Machine Learning JF - ACM Computing Surveys N2 - 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. Y1 - 2022 UR - https://dl.acm.org/doi/10.1145/3485128 U6 - https://doi.org/10.1145/3485128 VL - 55 IS - 2 SP - 1 EP - 96 ER -