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Tackling climate change with machine learning

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

Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML 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 ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.
Published in: ACM Computing Surveys, 10.1145/3485128, Association for Computing Machinery