Anwendungsfälle und Methoden der künstlichen Intelligenz in der anwendungsorientierten Forschung im Kontext von Industrie 4.0
B. Maschler, D. White, and M. Weyrich. in ten Hompel M., Vogel-Heuser B., Bauernhansl T. (Eds.) Handbuch Industrie 4.0. Springer Reference Technik. Springer Vieweg, Berlin, Heidelberg, published online: 24.11.2020, p. 1-15, (2020)
DOI: 10.1007/978-3-662-45537-1_123-1
Abstract
It is expected that data-driven methods of artificial intelligence in the context of Industry 4.0 will shape the future of industrial manufacturing. Although the topic is very present in research, the extent of the actual use of these methods remains unclear. This article therefore analyzes scientific articles published between 2013 and 2018 to obtain statistical data on the use of artificial intelligence methods in industry. Special attention is paid to the types of training and evaluation data, the distribution in different industrial branches, the considered use cases and the geographical origin of these articles. The resulting findings are distilled into practical advice for decision-makers.
in ten Hompel M., Vogel-Heuser B., Bauernhansl T. (Eds.) Handbuch Industrie 4.0. Springer Reference Technik. Springer Vieweg, Berlin, Heidelberg, published online: 24.11.2020, p. 1-15
%0 Report
%1 maschler2020anwendungsflle
%A Maschler, Benjamin
%A White, Dustin
%A Weyrich, Michael
%D 2020
%K 2020ias 2020kiundmachinelearning ias
%R 10.1007/978-3-662-45537-1_123-1
%T Anwendungsfälle und Methoden der künstlichen Intelligenz in der anwendungsorientierten Forschung im Kontext von Industrie 4.0
%U https://doi.org/10.1007/978-3-662-45537-1_123-1
%X It is expected that data-driven methods of artificial intelligence in the context of Industry 4.0 will shape the future of industrial manufacturing. Although the topic is very present in research, the extent of the actual use of these methods remains unclear. This article therefore analyzes scientific articles published between 2013 and 2018 to obtain statistical data on the use of artificial intelligence methods in industry. Special attention is paid to the types of training and evaluation data, the distribution in different industrial branches, the considered use cases and the geographical origin of these articles. The resulting findings are distilled into practical advice for decision-makers.
@techreport{maschler2020anwendungsflle,
abstract = {It is expected that data-driven methods of artificial intelligence in the context of Industry 4.0 will shape the future of industrial manufacturing. Although the topic is very present in research, the extent of the actual use of these methods remains unclear. This article therefore analyzes scientific articles published between 2013 and 2018 to obtain statistical data on the use of artificial intelligence methods in industry. Special attention is paid to the types of training and evaluation data, the distribution in different industrial branches, the considered use cases and the geographical origin of these articles. The resulting findings are distilled into practical advice for decision-makers.},
added-at = {2020-02-27T16:37:27.000+0100},
author = {Maschler, Benjamin and White, Dustin and Weyrich, Michael},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2dbd27f0c7516dd193bf99808e0d1129d/taylansngerli},
doi = {10.1007/978-3-662-45537-1_123-1},
institution = {in ten Hompel M., Vogel-Heuser B., Bauernhansl T. (Eds.) Handbuch Industrie 4.0. Springer Reference Technik. Springer Vieweg, Berlin, Heidelberg, published online: 24.11.2020, p. 1-15 },
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intrahash = {dbd27f0c7516dd193bf99808e0d1129d},
keywords = {2020ias 2020kiundmachinelearning ias},
timestamp = {2021-04-06T09:37:49.000+0200},
title = {Anwendungsfälle und Methoden der künstlichen Intelligenz in der anwendungsorientierten Forschung im Kontext von Industrie 4.0},
url = {https://doi.org/10.1007/978-3-662-45537-1_123-1},
year = 2020
}
