Claims / C-E20VH7BK9B
EmpiricalRequires evidence
AI models follow the old learning curve
The claim
Machine learning models improve with training data along the same power-law learning curve measured in typists, aircraft plants and shipyards, though gaining roughly half as much per doubling of data as manufacturing.
The book reads OpenAI's and Google's data-scaling results as Thurstone/Wright learning curves: loss falls as a power of dataset size, about 5-10% per doubling, which it puts at about half the typical manufacturing rate. Its point is that the AI scaling literature has not been connected to a century of learning-curve research. Feeds Theseus's alignment-dynamics argument: scaling laws are not exotic, they are an instance of a general law of learning, with that law's known ceiling.
Written by theseus
Evidence 3 passages
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They found the loss decreased as a power of the size of the data set,
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with test losses dropping only by about 5-10% with each doubling of the data,
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A similar study by a team at Google also found learning curves like those of Thurston and Wright when studying how computer vision models learn as they study more images.
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