%0 Generic %1 rolnick2019tackling %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 %A Maharaj, Tegan %A Sherwin, Evan D. %A Mukkavilli, S. Karthik %A Kording, Konrad P. %A Gomes, Carla %A Ng, Andrew Y. %A Hassabis, Demis %A Platt, John C. %A Creutzig, Felix %A Chayes, Jennifer %A Bengio, Yoshua %D 2019 %K ai4science change climate survey %T Tackling Climate Change with Machine Learning %U http://arxiv.org/abs/1906.05433 %X 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.

@misc{rolnick2019tackling, abstract = {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.}, added-at = {2022-03-16T22:04:39.000+0100}, 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 and Maharaj, Tegan and Sherwin, Evan D. and Mukkavilli, S. Karthik and Kording, Konrad P. and Gomes, Carla 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/249fc551c45aeac26b07e9909fe7319d8/annakrause}, description = {1906.05433.pdf}, interhash = {d1e92a99e5b7f9b48014d2577c993e09}, intrahash = {49fc551c45aeac26b07e9909fe7319d8}, keywords = {ai4science change climate survey}, note = {cite arxiv:1906.05433Comment: For additional resources, please visit the website that accompanies this paper: https://www.climatechange.ai/}, timestamp = {2022-10-12T14:53:48.000+0200}, title = {Tackling Climate Change with Machine Learning}, url = {http://arxiv.org/abs/1906.05433}, year = 2019 }