Changelog Master Feed 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.
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Featuring:
- Roy Schwartz – Website, X
- Jesse Dodge – X
- Chris Benson – Website, GitHub, LinkedIn, X
- Daniel Whitenack – Website, GitHub, X
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!