The carbon impact of artificial intelligence

By Payal Dhar. Nature Machine Intelligence, 2020

410 citations recorded by the source corpus.

Subfields: Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/805d950d6df9bdabd6b87d06de213909192341db

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. Mohammed Bennamoun

Citations in the corpus

• The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations (2021, background): “Several studies have investigated this issue in the context of energy consumption and carbon emissions (Lacoste et al. 2019; Schwartz et al. 2019; Strubell et al. 2019; Henderson et al. 2020; Dhar 2020).”
• A systematic review of Green AI (2023, background): “Dhar [40] flags the importance of being able to quantify carbon impact and the lack of tools and data available.”
• Training Spiking Neural Networks Using Lessons From Deep Learning (2021, background): “OpenAI’s language model, GPT-3, contains 175 billion learnable parameters, estimated to consume roughly 190 000 kWh to train [25], [26], [27].”