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Machine Learning in Artificial Intelligence: Towards a Common Understanding

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dc.contributor.author Kühl, Niklas
dc.contributor.author Goutier, Marc
dc.contributor.author Hirt, Robin
dc.contributor.author Satzger, Gerhard
dc.date.accessioned 2019-01-03T00:35:51Z
dc.date.available 2019-01-03T00:35:51Z
dc.date.issued 2019-01-08
dc.identifier.isbn 978-0-9981331-2-6
dc.identifier.uri http://hdl.handle.net/10125/59960
dc.description.abstract The application of “machine learning” and “artificial intelligence” has become popular within the last decade. Both terms are frequently used in science and media, sometimes interchangeably, sometimes with different meanings. In this work, we aim to clarify the relationship between these terms and, in particular, to specify the contribution of machine learning to artificial intelligence. We review relevant literature and present a conceptual framework which clarifies the role of machine learning to build (artificial) intelligent agents. Hence, we seek to provide more terminological clarity and a starting point for (inter¬disciplinary) discussions and future research.
dc.format.extent 10 pages
dc.language.iso eng
dc.relation.ispartof Proceedings of the 52nd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject AI, Machine Learning, IoT, and Analytics: Exploring the Implications for Knowledge Management and Innovation
dc.subject Knowledge Innovation and Entrepreneurial Systems
dc.subject Artificial Intelligence, Machine Learning, Statistical Learning, Intelligent Agents
dc.title Machine Learning in Artificial Intelligence: Towards a Common Understanding
dc.type Conference Paper
dc.type.dcmi Text
dc.identifier.doi 10.24251/HICSS.2019.630
Appears in Collections: AI, Machine Learning, IoT, and Analytics: Exploring the Implications for Knowledge Management and Innovation


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