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Tackling climate change with machine learning

Authors :
Rolnick, David
Donti, Priya L.
Kaack, Lynn H.
Kochanski, Kelly
Lacoste, Alexandre
Sankaran, Kris
Ross, Andrew Slavin
Milojevic-Dupont, Nikola
Jaques, Natasha
Waldman-Brown, Anna
Luccioni, Alexandra Sasha
Maharaj, Tegan
Sherwin, Evan D.
Mukkavilli, S. Karthik
Körding, Konrad Paul
Gomes, Carla P.
Ng, Andrew Y.
Hassabis, Demis
Platt, John C.
Creutzig, Felix
Chayes, Jennifer
Bengio, Yoshua
Rolnick, David
Donti, Priya L.
Kaack, Lynn H.
Kochanski, Kelly
Lacoste, Alexandre
Sankaran, Kris
Ross, Andrew Slavin
Milojevic-Dupont, Nikola
Jaques, Natasha
Waldman-Brown, Anna
Luccioni, Alexandra Sasha
Maharaj, Tegan
Sherwin, Evan D.
Mukkavilli, S. Karthik
Körding, Konrad Paul
Gomes, Carla P.
Ng, Andrew Y.
Hassabis, Demis
Platt, John C.
Creutzig, Felix
Chayes, Jennifer
Bengio, Yoshua
Source :
PolyPublie
Publication Year :
2023

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.

Details

Database :
OAIster
Journal :
PolyPublie
Notes :
PolyPublie
Publication Type :
Electronic Resource
Accession number :
edsoai.on1429912562
Document Type :
Electronic Resource