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

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Title:Innovating with Artificial Intelligence: Capturing the Constructive Functional Capabilities of Deep Generative Learning
Authors:Hofmann, Peter
Rückel, Timon
Urbach, Nils
Keywords:AI, Organizing, and Management
artificial intelligence
capabilities
deep generative learning
innovation
show 1 moremachine learning
show less
Date Issued:05 Jan 2021
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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/71289
ISBN:978-0-9981331-4-0
DOI:10.24251/HICSS.2021.669
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: AI, Organizing, and Management


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