%0 Journal Article %1 10.1145/3485128 %A Rolnick, David %A Donti, Priya L. %A Kaack, Lynn H. %A Kochanski, Kelly %A Lacoste, Alexandre %A Sankaran, Kris %A Ross, Andrew Slavin %A Milojevic-Dupont, Nikola %A Jaques, Natasha %A Waldman-Brown, Anna %A Luccioni, Alexandra Sasha %A Maharaj, Tegan %A Sherwin, Evan D. %A Mukkavilli, S. Karthik %A Kording, Konrad P. %A Gomes, Carla P. %A Ng, Andrew Y. %A Hassabis, Demis %A Platt, John C. %A Creutzig, Felix %A Chayes, Jennifer %A Bengio, Yoshua %C New York, NY, USA %D 2022 %I Association for Computing Machinery %J ACM Comput. Surv. %K Climate adaptation artificial change intelligence learning machine mitigation %N 2 %R 10.1145/3485128 %T Tackling Climate Change with Machine Learning %U https://doi.org/10.1145/3485128 %V 55 %X 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.

@article{10.1145/3485128, abstract = {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.}, added-at = {2022-03-29T08:10:04.000+0200}, address = {New York, NY, USA}, articleno = {42}, author = {Rolnick, David and Donti, Priya L. and Kaack, Lynn H. and Kochanski, Kelly and Lacoste, Alexandre and Sankaran, Kris and Ross, Andrew Slavin and Milojevic-Dupont, Nikola and Jaques, Natasha and Waldman-Brown, Anna and Luccioni, Alexandra Sasha and Maharaj, Tegan and Sherwin, Evan D. and Mukkavilli, S. Karthik and Kording, Konrad P. and Gomes, Carla P. and Ng, Andrew Y. and Hassabis, Demis and Platt, John C. and Creutzig, Felix and Chayes, Jennifer and Bengio, Yoshua}, biburl = {https://www.bibsonomy.org/bibtex/28f977a2199d7b6a18d08577c81b0074c/msteininger}, doi = {10.1145/3485128}, interhash = {ddc0f79cbc1c72c506420bf599f7e622}, intrahash = {8f977a2199d7b6a18d08577c81b0074c}, issn = {0360-0300}, issue_date = {March 2023}, journal = {ACM Comput. Surv.}, keywords = {Climate adaptation artificial change intelligence learning machine mitigation}, month = feb, number = 2, numpages = {96}, publisher = {Association for Computing Machinery}, timestamp = {2022-03-29T08:10:04.000+0200}, title = {Tackling Climate Change with Machine Learning}, url = {https://doi.org/10.1145/3485128}, volume = 55, year = 2022 }