Corpus ID: 189762063

Tackling Climate Change with Machine Learning

@article{Rolnick2019TacklingCC,
  title={Tackling Climate Change with Machine Learning},
  author={D. Rolnick and P. Donti and Lynn H. Kaack and Kelly Kochanski and Alexandre Lacoste and K. Sankaran and A. Ross and Nikola Milojevic-Dupont and N. Jaques and Anna Waldman-Brown and Alexandra Luccioni and Tegan Maharaj and E. Sherwin and S. K. Mukkavilli and Konrad P. K{\"o}rding and Carla Gomes and A. Ng and Demis Hassabis and John C. Platt and F. Creutzig and J. Chayes and Yoshua Bengio},
  journal={ArXiv},
  year={2019},
  volume={abs/1906.05433}
}
  • D. Rolnick, P. Donti, +19 authors Yoshua Bengio
  • Published 2019
  • Computer Science, Mathematics
  • ArXiv
  • 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… CONTINUE READING
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