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Found 5 papers, 2 papers with code
Chinchilla Scaling: A replication attempt
Hoffmann et al. (2022) propose three methods for estimating a compute-optimal scaling law.
Algorithmic progress in language models
We investigate the rate at which algorithms for pre-training language models have improved since the advent of deep learning.
Explosive growth from AI automation: A review of the arguments
Key questions remain about the intensity of regulatory responses to AI, physical bottlenecks in production, the economic value of superhuman abilities, and the rate at which AI automation could occur.
Power Law Trends in Speedrunning and Machine Learning
Using a random effects model, we improve on this baseline for relative mean square error made on predicting out-of-sample world record improvements as the comparison metric at a $p < 10^{-5}$ significance level.
Algorithmic progress in computer vision
Using Shapley values to attribute performance improvements, we find that algorithmic improvements have been roughly as important as the scaling of compute for progress computer vision.
