Innovating with Artificial Intelligence: Capturing the Constructive Functional Capabilities of Deep Generative Learning

dc.contributor.author Hofmann, Peter
dc.contributor.author Rückel, Timon
dc.contributor.author Urbach, Nils
dc.date.accessioned 2020-12-24T20:09:03Z
dc.date.available 2020-12-24T20:09:03Z
dc.date.issued 2021-01-05
dc.description.abstract As an emerging species of artificial intelligence, deep generative learning models can generate an unprecedented variety of new outputs. Examples include the creation of music, text-to-image translation, or the imputation of missing data. Similar to other AI models that already evoke significant changes in society and economy, there is a need for structuring the constructive functional capabilities of DGL. To derive and discuss them, we conducted an extensive and structured literature review. Our results reveal a substantial scope of six constructive functional capabilities demonstrating that DGL is not exclusively used to generate unseen outputs. Our paper further guides companies in capturing and evaluating DGL’s potential for innovation. Besides, our paper fosters an understanding of DGL and provides a conceptual basis for further research.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2021.669
dc.identifier.isbn 978-0-9981331-4-0
dc.identifier.uri http://hdl.handle.net/10125/71289
dc.language.iso English
dc.relation.ispartof Proceedings of the 54th 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, Organizing, and Management
dc.subject artificial intelligence
dc.subject capabilities
dc.subject deep generative learning
dc.subject innovation
dc.subject machine learning
dc.title Innovating with Artificial Intelligence: Capturing the Constructive Functional Capabilities of Deep Generative Learning
prism.startingpage 5505
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