Informatik
Refine
Year of publication
- 2020 (4) (remove)
Document Type
- Conference proceeding (4) (remove)
Language
- English (4) (remove)
Has full text
- yes (4)
Is part of the Bibliography
- yes (4)
Institute
- Informatik (4)
Publisher
- Association for Information Systems (1)
- IEEE (1)
- RWTH Aachen (1)
- Springer (1)
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.