Unprovability comes to machine learning

Lev Reyzin ()

Nature, 2019, vol. 565, issue 7738, 166-167

Abstract: Scenarios have been discovered in which it is impossible to prove whether or not a machine-learning algorithm could solve a particular problem. This finding might have implications for both established and future learning algorithms.

Keywords: Computer science; Mathematics and computing (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/d41586-019-00012-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:565:y:2019:i:7738:d:10.1038_d41586-019-00012-4

Ordering information: This journal article can be ordered from
https://www.nature.com/

DOI: 10.1038/d41586-019-00012-4

Access Statistics for this article

Nature is currently edited by Magdalena Skipper

More articles in Nature from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().