Cross-Discipline Overview

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Work of Many

The paper “Tackling Climate Change with Machine Learning” is a comprehensive, cross-discipline overview of the research in climate change and other related fields. This paper sets its scope beyond simply predicting the future and defines a comprehensive role scientists and society should play to address the current and future challenges using machine learning. It divides the current researches and efforts into two categories: mitigation and adaptation (Rolnick, et al. 2019). In mitigation, the efforts are focused on lessening the effects of the climate change, such as changing the energy industry to consume less fossil fuel or building more sustainable cities (Rolnick, et al. 2019). The adaptation, on the other hand, accepts that certain outcomes are inevitable and focuses on technologies that would allow humans to survive, including weather prediction, social infrastructure, and education (Rolnick, et al. 2019). The paper also provides some guidance to prioritizing different tasks: some tasks are estimated to be highly beneficial with huge impact while some others may be potentially useful but with high risk (Rolnick, et al. 2019).

References

  • Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., ... & Luccioni, A. (2019). Tackling Climate Change with Machine Learning. arXiv preprint arXiv:1906.05433.

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