Sterkenburg, Tom F. (2016) Putnam's Diagonal Argument and the Impossibility of a Universal Learning Machine. [Preprint]
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Abstract
The diagonalization argument of Putnam (1963) denies the possibility of a universal learning machine. Yet the proposal of Solomonoff (1964) and Levin (1970) promises precisely such a thing. In this paper I discuss how their proposed measure function manages to evade Putnam's diagonalization in one respect, only to fatally fall prey to it in another.
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Item Type: | Preprint | ||||||
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Keywords: | diagonal argument, computability, inductive logic, Bayesian confirmation, universal prediction, algorithmic information theory, problem of induction | ||||||
Subjects: | General Issues > Confirmation/Induction | ||||||
Depositing User: | Mr Tom Sterkenburg | ||||||
Date Deposited: | 16 May 2016 15:52 | ||||||
Last Modified: | 16 May 2016 15:52 | ||||||
Item ID: | 12096 | ||||||
Subjects: | General Issues > Confirmation/Induction | ||||||
Date: | 2016 | ||||||
URI: | https://philsci-archive.pitt.edu/id/eprint/12096 |
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- Putnam's Diagonal Argument and the Impossibility of a Universal Learning Machine. (deposited 16 May 2016 15:52) [Currently Displayed]
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