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Found 4 papers, 1 papers with code
AI capabilities can be significantly improved without expensive retraining
State-of-the-art AI systems can be significantly improved without expensive retraining via "post-training enhancements"-techniques applied after initial training like fine-tuning the system to use a web browser.
Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning
We analyze the growth of dataset sizes used in machine learning for natural language processing and computer vision, and extrapolate these using two methods; using the historical growth rate and estimating the compute-optimal dataset size for future predicted compute budgets.
Machine Learning Model Sizes and the Parameter Gap
From 1950 to 2018, model size in language models increased steadily by seven orders of magnitude.
Compute Trends Across Three Eras of Machine Learning
Since the advent of Deep Learning in the early 2010s, the scaling of training compute has accelerated, doubling approximately every 6 months.
