Rushing, Bruce and Gomez-Lavin, Javier (2024) Is the Scaling Hypothesis Falsifiable? In: UNSPECIFIED.

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Abstract

The scaling hypothesis in artificial intelligence claims that a model's cognitive ability scales with increased compute. This hypothesis has two interpretations: a weak version where model error rates decrease as a power law function of compute, and a strong version where as error rates decrease new cognitive abilities unexpectedly emerge. We argue that the first is falsifiable but the second is not because it fails to make exact predictions about which abilities emerge and when. This points to the difficulty of measuring cognitive abilities in algorithms since we lack good ecologically valid measurements of those abilities.

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Item Type:	Conference or Workshop Item (UNSPECIFIED)
Creators:	Creators Email ORCID Rushing, Bruce bmrushin@purdue.edu 0000-0002-0864-9272 Gomez-Lavin, Javier jgomezlavin@gmail.com 0000-0002-0476-8290
Keywords:	machine learning, artificial intelligence, cognitive ability
Subjects:	Specific Sciences > Artificial Intelligence Specific Sciences > Artificial Intelligence > Machine Learning
Depositing User:	Dr Bruce Rushing
Date Deposited:	26 Jun 2024 17:19
Last Modified:	26 Jun 2024 17:19
Item ID:	23622
Subjects:	Specific Sciences > Artificial Intelligence Specific Sciences > Artificial Intelligence > Machine Learning
Date:	1 March 2024
URI:	https://philsci-archive.pitt.edu/id/eprint/23622

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