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 D. Sherwin, S. Karthik Mukkavilli, Konrad Paul Körding, Carla P. Gomes, Andrew Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer Chayes and Yoshua Bengio
Article (2023)
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Open Access to the full text of this document Published Version Terms of Use: Creative Commons Attribution Download (6MB) |
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.
Subjects: |
2500 Electrical and electronic engineering > 2500 Electrical and electronic engineering 2800 Artificial intelligence > 2800 Artificial intelligence (Computer vision, see 2603) |
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Department: | Department of Computer Engineering and Software Engineering |
PolyPublie URL: | https://publications.polymtl.ca/51114/ |
Journal Title: | ACM Computing Surveys (vol. 55, no. 2) |
Publisher: | Association for computing machinery |
DOI: | 10.1145/3485128 |
Official URL: | https://doi.org/10.1145/3485128 |
Date Deposited: | 18 Apr 2023 14:58 |
Last Modified: | 09 Apr 2025 20:40 |
Cite in APA 7: | Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., Luccioni, A. S., Maharaj, T., Sherwin, E. D., Mukkavilli, S. K., Körding, K. P., Gomes, C. P., Ng, A. Y., Hassabis, D., Platt, J. C., ... Bengio, Y. (2023). Tackling climate change with machine learning. ACM Computing Surveys, 55(2), 42 (96 pages). https://doi.org/10.1145/3485128 |
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