• Computer Science, Mathematics
  • Published in ArXiv 2019

Tackling Climate Change with Machine Learning

@article{Rolnick2019TacklingCC,
  title={Tackling Climate Change with Machine Learning},
  author={David Rolnick and Priya L. Donti and Lynn H. Kaack and Kelly Kochanski and Alexandre Lacoste and Kris Sankaran and Andrew Slavin Ross and Nikola Milojevic-Dupont and Natasha Jaques and Anna Waldman-Brown and Alexandra Luccioni and Tegan Maharaj and Evan D. Sherwin and S. Karthik Mukkavilli and Konrad P. K{\"o}rding and Carla Gomes and Andrew Y. Ng and Demis Hassabis and John C. Platt and Felix Creutzig and Jennifer Chayes and Yoshua Bengio},
  journal={ArXiv},
  year={2019},
  volume={abs/1906.05433}
}
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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