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

Title data

Hofmann, Peter ; Rückel, Timon ; Urbach, Nils:
Innovating with Artificial Intelligence : Capturing the Constructive Functional Capabilities of Deep Generative Learning.
2021
Event: 54th Hawaii International Conference on System Sciences (HICSS) , 05.-08.01.2021 , Maui, Hawaii (Virtual).
(Conference item: Conference , Speech )

Official URL: Volltext

Project information

Project title:
Project's official titleProject's id
Projektgruppe WI Künstliche IntelligenzNo information

Abstract in another language

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.

Further data

Item Type: Conference item (Speech)
Refereed: Yes
Keywords: artificial intelligence; machine learning; deep generative learning; capabilities; innovation
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Former Professors > Professor Information Systems Management and Strategic IT Management - Univ.-Prof. Dr. Nils Urbach
Research Institutions
Research Institutions > Affiliated Institutes
Research Institutions > Affiliated Institutes > Fraunhofer Project Group Business and Information Systems Engineering
Research Institutions > Affiliated Institutes > FIM Research Center Finance & Information Management
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Date Deposited: 14 Dec 2020 09:42
Last Modified: 14 Dec 2020 09:42
URI: https://eref.uni-bayreuth.de/id/eprint/61064