[FOM] Learnability
Joe Shipman
joeshipman at aol.com
Wed Jan 16 23:22:44 EST 2019
This can’t be right. All known mathematically definable problems of practical significance can be formulated in ways that are set-theoretically absolute and do not depend on CH.
The paper is well-written and rigorous, but the kind of “function” it finds the “learnability of” to be “undecidable” involves “inputs” which contain an infinite amount of information (namely, a finite subset of the real number interval [0,1]) . The learning machine is then supposed to be able to produce an “output” which contains an infinite amount of information (another finite subset of [0,1]).
If the input were streamed, and the output were streamed, and “learning” was defined in terms of some limit property of the input and output functions, then we would have a realistic model, but it seems their proof would then not apply. In their model, all the input is needed to produce any of the output.
— JS
Sent from my iPhone
> On Jan 13, 2019, at 7:26 PM, Victor Marek <marek at cs.uky.edu> wrote:
>
> During a routine perusal of the site RealClearScience.com, I read the piece
> about the article "Learnability can be undecidable", by S. Ben-David, P. Hrubes
> et.al. The piece is published in the journal Nature Machine Intelligence.
> In that paper the authors appear to show that certain aspect of machine learning
> (pretty practical task) is equivalent (in ZFC) to Continuum Hypothesis which
> is (as we know since P.J. Cohen) undecidable in ZFC.
>
> I never did Machine Learning, and this appears to be absolutely incredible.
> The piece in RealClearScience is a product of a science writer, not necessarily
> knowing what s/he is talking about.
>
> Obviously, the matter is relevant to F.O.M. Could someone in the community make
> this matter clearer for pedestrians such as I?
>
> Thanks,
>
> Victor Marek
> Victor W. Marek Department of Computer Science
> marek at cs.uky.edu University of Kentucky
> marek at cs.engr.uky.edu Lexington, KY 40506-0633
> 859-257-3496 (office) 859-257-3961 (Dept)
> http://www.cs.uky.edu/~marek 859-257-1505 (FAX)
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