https://arxiv.org/abs/1906.05433
Tackling Climate Change with Machine Learning David Rolnick <https://arxiv.org/search/cs?searchtype=author&query=Rolnick%2C+D>, Priya L. Donti <https://arxiv.org/search/cs?searchtype=author&query=Donti%2C+P+L> , Lynn H. Kaack <https://arxiv.org/search/cs?searchtype=author&query=Kaack%2C+L+H>, Kelly Kochanski <https://arxiv.org/search/cs?searchtype=author&query=Kochanski%2C+K>, Alexandre Lacoste <https://arxiv.org/search/cs?searchtype=author&query=Lacoste%2C+A>, Kris Sankaran <https://arxiv.org/search/cs?searchtype=author&query=Sankaran%2C+K> , Andrew Slavin Ross <https://arxiv.org/search/cs?searchtype=author&query=Ross%2C+A+S>, Nikola Milojevic-Dupont <https://arxiv.org/search/cs?searchtype=author&query=Milojevic-Dupont%2C+N> , Natasha Jaques <https://arxiv.org/search/cs?searchtype=author&query=Jaques%2C+N>, Anna Waldman-Brown <https://arxiv.org/search/cs?searchtype=author&query=Waldman-Brown%2C+A>, Alexandra Luccioni <https://arxiv.org/search/cs?searchtype=author&query=Luccioni%2C+A> , Tegan Maharaj <https://arxiv.org/search/cs?searchtype=author&query=Maharaj%2C+T>, Evan D. Sherwin <https://arxiv.org/search/cs?searchtype=author&query=Sherwin%2C+E+D> , S. Karthik Mukkavilli <https://arxiv.org/search/cs?searchtype=author&query=Mukkavilli%2C+S+K>, Konrad P. Kording <https://arxiv.org/search/cs?searchtype=author&query=Kording%2C+K+P>, Carla Gomes <https://arxiv.org/search/cs?searchtype=author&query=Gomes%2C+C>, Andrew Y. Ng <https://arxiv.org/search/cs?searchtype=author&query=Ng%2C+A+Y>, Demis Hassabis <https://arxiv.org/search/cs?searchtype=author&query=Hassabis%2C+D> , John C. Platt <https://arxiv.org/search/cs?searchtype=author&query=Platt%2C+J+C>, Felix Creutzig <https://arxiv.org/search/cs?searchtype=author&query=Creutzig%2C+F> , Jennifer Chayes <https://arxiv.org/search/cs?searchtype=author&query=Chayes%2C+J>, Yoshua Bengio <https://arxiv.org/search/cs?searchtype=author&query=Bengio%2C+Y> (Submitted on 10 Jun 2019) 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. Subjects: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:1906.05433 <https://arxiv.org/abs/1906.05433> [cs.CY] -- You received this message because you are subscribed to the Google Groups "geoengineering" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at https://groups.google.com/group/geoengineering. To view this discussion on the web visit https://groups.google.com/d/msgid/geoengineering/CAJ3C-05Rfp3LnNz2W2Yj4YEue9Ca8o_1PSvKMSmjpBtC1%2BEXUQ%40mail.gmail.com. For more options, visit https://groups.google.com/d/optout.
