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., Maharaj, T., Sherwin, E. D., Mukkavilli, K., Kording, K. P., Gomes, C., Ng, A. Y., Hassabis, D., Platt, J. C., Creutzig, F., Chayes, J., Bengio, Y.,

Tackling Climate Change with Machine Learning

in arXiv, 01.08.2019

Peer Review , Land Use, Infrastructure and Transport

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.

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