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SAIL: SustAInable Life-cycle of Intelligent Socio-Technical Systems

Overview

Current systems that incorporate AI technology mainly target the introduction phase, where a core component is training and adaptation of AI models based on given example data. SAIL’s focus on the full life-cycle moves the current emphasis towards sustainable long-term development in real life. The joint project SAIL addresses both basic research in the field of AI, its implications from the perspective of the humanities and social sciences, and concrete applications in the field of Industry 4.0 and Intelligent Healthcare. SAIL is an interdisciplinary and interinstitutional collaboration of Bielefeld UniversityPaderborn UniversityBielefeld University of Applied Sciences, and OWL University of Applied Sciences and Arts, funded by the MKW NRW.

Project headed by the DSE group: (Project R2.3) Human-centered continuous optimization

Manual workplaces involving both humans and technical machinery are usually optimized at design time. This ignores improvements due to human learning dur- ing long-term work. Therefore, we propose to study the application of Bayesian optimization and life-long learning with a human-centered focus, e.g., a suitable weighting of past and current data or a transfer between humans.

SAIL addresses the next stage of AI development by looking at the entire life cycle of AI systems and their technological and societal implications. Accordingly, SAIL is interdisciplinary in nature involving researchers from the core areas of AI, engineering, computer science, and the social sciences and humanities. The research program is divided into three research pillars and two application areas. Basic research will look at the interaction of AI and human partners in evaluating and coordinating errors and goals. In addition, mature AI systems are analyzed to model and mitigate their potentially undesirable long-term effects at the functional, cognitive, and societal levels. Finally, the entire AI lifecycle is considered in terms of efficiency to enable the practical deployment of AI systems with minimal energy, time, and storage requirements and low cognitive effort on the part of the human partner. The application areas of SAIL are intelligent industrial work environments and adaptive assistance systems for healthcare.

Funding program

Ministry of Culture and Science of North Rhine-Westphalia (MKW NRW), NW21-059D

Key Facts

Project duration:
08/2022 - 07/2026
Funded by:
MKW NRW
Websites:
Homepage
Projektbeschreibung bei der FH Bielefeld
Current research projects
Projektbeschreibung bei JAII
Projektseite DICE

More Information

Principal Investigators

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Prof. Dr. Reinhold Häb-Umbach

Communications Engineering / Heinz Nixdorf Institute

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Prof. Dr. Marco Platzner

Computer Engineering

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Prof. Dr.-Ing. habil. Ansgar Trächtler

Regelungstechnik und Mechatronik / Heinz Nixdorf Institut

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Prof. Dr. Christian Plessl

High-Performance Computing

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Prof. Dr.-Ing. Roman Dumitrescu

Advanced Systems Engineering / Heinz Nixdorf Institut

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Prof. Dr.-Ing. habil. Walter Sextro

Dynamics and Mechatronics (LDM)

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Jun.-Prof. Dr. Sebastian Peitz

Data Science for Engineering

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Prof. Dr. Katharina Rohlfing

Key research area Transformation and Education

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Prof. Dr. Eric Bodden

Heinz Nixdorf Institut

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Prof. Dr. Axel-Cyrille Ngonga Ngomo

Transregional Collaborative Research Centre 318

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AOR. Dr. Ilona Horwath

Technik und Diversity

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Jun. Prof. Dr. Suzana Alpsancar

Applied ethics with a focus on technology ethics in the digital world

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Cooperating Institutions

Universität Bielefeld

Cooperating Institution

Fachhochschule Bielefeld

Cooperating Institution

Technische Hochschule Ostwestfalen-Lippe (TH OWL)

Cooperating Institution

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Publications

Neural Class Expression Synthesis
N.J. KOUAGOU, S. Heindorf, C. Demir, A.-C. Ngonga Ngomo, in: C. Pesquita, E. Jimenez-Ruiz, J. McCusker, D. Faria, M. Dragoni, A. Dimou, R. Troncy, S. Hertling (Eds.), The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023), Springer International Publishing, 2023, pp. 209–226.
LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals
C. Demir, M. Wiebesiek, R. Lu, A.-C. Ngonga Ngomo, S. Heindorf, ECML PKDD (2023).
Neuro-Symbolic Class Expression Learning
C. Demir, A.-C. Ngonga Ngomo, International Joint Conference on Artificial Intelligence (2023).
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