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Intelligent systems are characterized by learning processes that allow the acquisition of new skills and deal with a large amount of heterogeneous, uncertain and probabilistic data. Due to the complexity of the real-world situations represented by them, the automatic abstraction of information from data, the formation of appropriate representative models, and the semantic processing of existing information is essential to build intelligent systems for digital transformation, such as mobility To enable industry, medicine and education.

Projekte

Haisem Lab

Labor for methodical development of high-performance AI-applications for modern hardware-Architectures

 

 

 

 

 

iASiS

Big Data for precision medicine

 

 

 

IIP-Ecosphere: Next Level Ecosphere for Intelligent Industrial Production

IIP-Ecosphere: Next Level Ecosphere for Intelligent Industrial Production

 

 

 

 

 

 

InclusiveOCW

Inclusive, collaborative creation and usage of open courseware in the professional promotion of people with visual impairments

 

 

 

Industrial Data Acquisition Solution (INDAAQ)

INDAAQ deals with industrial data and its implementation in the manufacturing process

 

 

 

Internationales Zukunftslabor Künstliche Intelligenz

International Future Lab for Artificial Intelligence

 

 

 

Maschinelles Lernen Fähigkeiten für IKT-Fachleute      

 MACHINA project aims to enhance Vocational Education and Training for ICT professionals in the field of Machine learning.

 

 

Managed Forgetting

Today's flood of information makes it more and more difficult to concentrate on the really relevant and important things.

 

 

 

MIRROR

From perception to expectation to realization: comprehensive intermedia analysis on misinformation when migrating into the EU and potentially resulting threats.

 

 

 

MM4SPA

Intelligent synchronization and semantic enrichment of position and video data for the analysis of sports gamesa