KI-LiveS

Summary

Based on existing applications and problems in industry, KI-LiveS examined how current research results from the fields of machine learning and artificial intelligence (AI) could be used in industry in a meaningful way.

In order to carry that knowledge into the the industry, data sets from an industrial context were used to develop AI solutions in cooperation with the companies.

For this purpose, an AI lab was established within the project, ensuring a streamlined interdisciplinary workflow between the project and company partners.

Duration

Launch 01.10.2019
Termination 31.03.2022

Objective(s)

The AI Lab examines how German companies can be provided with competencies in cutting-edge research findings in the field of artificial intelligence.

Related links

Project partners

UDE

TU Dortmund University

University Hospital Essen

  • Center for Epidemiology

Publications

  • Cichiwskyj, Christopher; Schmeißer, Stephan; Qian, Chao; Einhaus, Lukas; Ringhofer, Christopher; Schiele, Gregor
    Elastic AI: System support for adaptive machine learning in pervasive computing systems
    In: CCF Transactions on Pervasive Computing and Interaction, Jg. 3, 2021, Nr. 3, S. 300 – 328
    DOI
  • Burger, Alwyn Johannes; Cichiwskyj, Christopher; Schmeißer, Stephan; Schiele, Gregor
    The Elastic Internet of Things - A Platform for Self-Integrating and Self-Adaptive IoT-Systems with Support for Embedded Adaptive Hardware
    In: Future Generation Computer Systems, Jg. 113, 2020, S. 607 – 619
    DOI
  • Cichiwskyj, Christopher; Qian, Chao; Schiele, Gregor
    Time to Learn: Temporal Accelerators as an Embedded Deep Neural Network Platform
    In: IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning: Second International Workshop, IoT Streams 2020, and First International Workshop, ITEM 2020, Co-located with ECML/PKDD 2020 ; Ghent, Belgium, September 14-18, 2020 ; Revised Selected Papers / 2nd International Workshop on IoT Streams for Data-Driven Predictive Maintenance ; IoT Streams 2020 ; September 14-18, 2020, Ghent, Belgium / Gama, João; Pashami, Sepideh; Bifet, Albert; Sayed-Mouchaweh, Moamar; Fröning, Holger; Pernkopf, Franz; Schiele, Gregor; Blott, Michaela (Hrsg.). Cham: Springer, 2020, S. 256 – 267
    DOI