Tackling Climate Change with Machine Learning-Reference-Cited by-同舟云学术

Tackling Climate Change with Machine Learning

Published:2022-02-07 Issue:2 Volume:55 Page:1-96
ISSN:0360-0300
Container-title:ACM Computing Surveys
language:en
Short-container-title:ACM Comput. Surv.

Author:

Rolnick David¹^ORCID,Donti Priya L.²,Kaack Lynn H.³,Kochanski Kelly⁴,Lacoste Alexandre⁵,Sankaran Kris⁶,Ross Andrew Slavin⁷,Milojevic-Dupont Nikola⁸,Jaques Natasha⁹,Waldman-Brown Anna¹⁰,Luccioni Alexandra Sasha¹¹,Maharaj Tegan¹²,Sherwin Evan D.¹³,Mukkavilli S. Karthik¹⁴,Kording Konrad P.¹⁵,Gomes Carla P.¹⁶,Ng Andrew Y.¹³,Hassabis Demis¹⁷,Platt John C.¹⁸,Creutzig Felix⁸,Chayes Jennifer¹⁹,Bengio Yoshua¹¹

Affiliation:

1. McGill University and Mila - Quebec AI Institute

2. Carnegie Mellon University

3. Hertie School and ETH Zürich

4. University of Colorado Boulder

5. Element AI/Service Now

6. University of Wisconsin - Madison and Université de Montréal

7. New York University and Harvard University

8. Mercator Research Institute on Global Commonsand Climate Change and Technische Universität Berlin

9. Google Brain and UC Berkeley

10. Massachusetts Institute of Technology

11. Mila - Quebec AI Institute and Université de Montréal

12. Mila - Quebec AI Institute and Polytechnique Montréal

13. Stanford University

14. University of California and Lawrence Berkeley National Lab

15. University of Pennsylvania

16. Cornell University

17. DeepMind

18. Google AI

19. University of California, Berkeley

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.

Funder

National Science Foundation

Center for Climate and Energy Decision Making through a cooperative agreement between the National Science Foundation and Carnegie Mellon University

US Department of Energy

Natural Sciences and Engineering Research Council of Canada

MIT Media Lab Consortium

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

Link

https://dl.acm.org/doi/pdf/10.1145/3485128