Recent years have seen a hype of machine learning-based AI technology. As in computer science in general, especially techniques like convolutional neural networks and the availability of more advanced hardware have led to the development of numerous new applications across all industries. At the same time, the market requirements and customer needs have enforced solutions that not only perform well in experimental settings but can robustly be applied in the real world.
This workshop aims at demonstrating current AI applications in industry and healthcare-related scenarios of digital curation. In many industries where safety and health are affected, presenting digital content cannot be done in a fully automated way but has to be curated by knowledge workers. For supporting this curation a whole generation of new technologies and tools has to be developed, which is a key focus of the Qurator platform.
Based on prototypes or prototype ideas defined within the Qurator project and the current project status, several talks are given to underline requirements in real-world processes as well as discuss challenges. It is intended to discuss with participants their own experience or perception of AI topics in this context, supported by interactive discussion formats. The program of this workshop offers new insights to people that are interested in improving their process quality and/or are working in knowledge management environments.
Real-world industry applications of AI also have to consider aspects like regulatory requirements, transparency/explainability, interoperability or standardized assessment of the quality of AI solutions. The presentations in this track will spotlight different types of industries impacted by these dimensions by sharing experiences and new research approaches that will then provide a starting point for an exchange between participants.
It must also be mentioned that applying machine learning techniques on large data sets is only one tool of many. In domains like health or others involving many intra-organizational perspectives, the amount of available data is negligible compared to the dimensionality of the domain, so that these techniques have to be combined with explicit knowledge. The challenge lies therefore in combining explicit domain knowledge with the insights that can be gained from the data. As we regard this a very important feature of knowledge work in the near future, there will be a special focus on the combination of explicit knowledge engineering and machine learning.
Welcome and Introduction
Industrial process management and knowledge capture in health scenarios - examples of knowledge work supported by curating technologies
Dr.-Ing. Frauke Weichhardt, Semtation
Henry Hoffmann, Ada Health
Requirements for presenting process information in the age of Industrial Digitalisation (title not confirmed yet)
Dr. Phanthian Zuesongdham, Hamburg Port Authority
Process management requirements in the publishing industry (title not confirmed yet)
Dr. Sue-Ann Bäsler, Cornelsen Verlag
World Café on Qurator industry scenarios including Hands on sessions,Conclusion and interactive voting
Presenter: Ute John (Gesellschaft für Wissensmanagement GfWM e.V.)
14:00 - 16:30
Importance of terminologies and ontologies in knowledge work
Explainable AI: Steps ahead in AI application in a B to C context
Compliance and AI
Importance of standardization of AI and AI benchmarking
N.N., Ada Health
16:30 - 17:00
Fishbowl Discussion about Workshop topics
Presenter: Ute John (GfWM e.V.)