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Towards engineering artificial intelligence-based applications

  • AI technologies such as deep learning provide promising advances in many areas. Using these technologies, enterprises and organizations implement new business models and capabilities. In the beginning, AI-technologies have been deployed in an experimental environment. AI-based applications have been created in an ad-hoc manner and without methodological guidance or engineering approach. Due to the increasing importance of AI-technologies, however, a more structured approach is necessary that enable the methodological engineering of AI-based applications. Therefore, we develop in this paper first steps towards methodological engineering of AI-based applications. First, we identify some important differences between the technological foundations of AI- technologies, in particular deep learning, and traditional information technologies. Then we create a framework that enables to engineer AI-applications using four steps: identification of an AI-application type, sub-type identification, lifecycle phase, and definition of details. The introduced framework considers that AI-applications use an inductive approach to infer knowledge from huge collections and streams of data. It not only enables the rapid development of AI-application but also the efficient sharing of knowledge on AI-applications.

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Metadaten
Author of HS ReutlingenZimmermann, Alfred; Möhring, Michael
DOI:https://doi.org/10.1109/EDOCW49879.2020.00020
ISBN:978-1-7281-6471-7
ISSN:2325-6605
Erschienen in:2020 IEEE 24th International Enterprise Distributed Object Computing Workshop (EDOCW), proceedings, Eindhoven, Netherlands, 5 October 2020
Publisher:IEEE
Place of publication:Piscataway, NJ
Document Type:Conference Proceeding
Language:English
Year of Publication:2020
Tag:application development; artificial intelligence; design science; framework
Page Number:9
First Page:54
Last Page:62
DDC classes:004 Informatik
Open Access?:Nein
Licence (German):License Logo  Lizenzbedingungen IEEE