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Climate change and machine learning


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Climate change and machine learning

With the increasing deployment of machine learning (ML) technologies across society, it is important to understand in which ways ML may accelerate or impede climate progress, and how various stakeholders can guide those developments. On the one hand, ML can facilitate climate change mitigation and adaptation strategies within a variety of sectors, such as energy, manufacturing, agriculture, forestry, and disaster management. On the other hand, ML can also contribute to rising greenhouse gas emissions through applications that benefit high-emitting sectors or drive increases in consumer demand, as well as via energy use associated with ML itself. In this talk, we will explore ML's multi-faceted relationship with climate change.


David Rolnick. Assistant Professor of Computer Science at McGill University.

 

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