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Innovating with artificial intelligence: Capturing the constructive functional capabilities of deep generative learning

: Hofmann, P.; Rückel, T.; Urbach, N.

Volltext ()

54th Hawaii International Conference on System Sciences 2021. Proceedings : Grand Hyatt Kauai, Hawaii, USA
Honolulu/Hawaii: Univ. of Hawaii at Manoa, 2021
ISBN: 978-0-9981331-4-0
Hawaii International Conference on System Sciences (HICSS) <54, 2021, Online>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer FIT ()

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.