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Epoch AI: Compute Trends in AI Training
webCredibility Rating
4/5
High(4)High quality. Established institution or organization with editorial oversight and accountability.
Rating inherited from publication venue: Epoch AI
This Epoch AI dataset is frequently cited in AI governance discussions, including debates about compute-based regulatory thresholds (e.g., the EU AI Act's GPAI rules and US executive orders referencing 10^26 FLOPs as a frontier model threshold).
Metadata
Importance: 72/100dataset
Summary
Epoch AI's database tracking historical and current trends in computational resources used to train AI models. It provides empirical data on how training compute has scaled over time, enabling analysis of AI progress and forecasting future capabilities.
Key Points
- •Tracks training compute (in FLOPs) for major AI models over time, showing exponential growth trends
- •Data covers decades of AI development, illustrating the dramatic increase in compute since deep learning's rise
- •Useful for understanding hardware requirements, cost trends, and the relationship between compute and AI capabilities
- •Supports policy and governance discussions around compute thresholds for frontier AI models
- •Empirical foundation for arguments about compute as a key lever for AI oversight and regulation
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| EU AI Act | Policy | 55.0 |
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