Green AI 🌲 (Practical AI #124)

Empirical analysis from Roy Schwartz (Hebrew University of Jerusalem) and Jesse Dodge (AI2) suggests the AI research community has paid relatively little attention to computational efficiency. A focus on accuracy rather than efficiency increases the carbon footprint of AI research and increases research inequality. In this episode, Jesse and Roy advocate for increased research activity in Green AI (AI research that is more environmentally friendly and inclusive). They highlight success stories and help us understand the practicalities of making our workflows more efficient.

Empirical analysis from Roy Schwartz (Hebrew University of Jerusalem) and Jesse Dodge (AI2) suggests the AI research community has paid relatively little attention to computational efficiency. A focus on accuracy rather than efficiency increases the carbon footprint of AI research and increases research inequality. In this episode, Jesse and Roy advocate for increased research activity in Green AI (AI research that is more environmentally friendly and inclusive). They highlight success stories and help us understand the practicalities of making our workflows more efficient.

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Featuring:

Roy Schwartz – Twitter, WebsiteJesse Dodge – TwitterChris Benson – Twitter, GitHub, LinkedIn, WebsiteDaniel Whitenack – Twitter, GitHub, Website

Show Notes:

Green AI article in the communications of the ACM
Training a single AI model can emit as much carbon as five cars in their lifetimes
Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping
Parameter-Efficient Transfer Learning for NLP
Reproducibility at EMNLP 2020

Something missing or broken? PRs welcome!