Tackling Key Challenges of AI Development – Insights from an Industry-Academia Collaboration
- Harnessing the overall benefits of the latest advancements in artificial intelligence (AI) requires the extensive collaboration of academia and industry. These collaborations promote innovation and growth while enforcing the practical usefulness of newer technologies in real life. The purpose of this article is to outline the challenges faced during cross-collaboration between academia andHarnessing the overall benefits of the latest advancements in artificial intelligence (AI) requires the extensive collaboration of academia and industry. These collaborations promote innovation and growth while enforcing the practical usefulness of newer technologies in real life. The purpose of this article is to outline the challenges faced during cross-collaboration between academia and industry. These challenges are also inspected with the help of an ongoing project titled “Quality Assurance of Machine Learning Applications” (Q-AMeLiA), in which three universities cooperate with five industry partners to make the product risk of AI-based products visible. Further, we discuss the hurdles and the key challenges in machine learning (ML) technology transformation from academia to industry based on robustness, simplicity, and safety. These challenges are an outcome of the lack of common standards, metrics, and missing regulatory considerations when state-of-the-art (SOTA) technology is developed in academia. The use of biased datasets involves ethical concerns that might lead to unfair outcomes when the ML model is deployed in production. The advancement of AI in small and medium sized enterprises (SMEs) requires more in terms of common tandardization of concepts rather than algorithm breakthroughs. In this paper, in addition to the general challenges, we also discuss domain specific barriers for five different domains i.e., object detection, hardware benchmarking, continual learning, action recognition, and industrial process automation, and highlight the steps necessary for successfully managing the cross-sectoral collaborations between academia and industry.…
Document Type: | Conference Proceeding |
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Conference Type: | Konferenzartikel |
Zitierlink: | https://opus.hs-offenburg.de/6452 | Bibliografische Angaben |
Title (English): | Tackling Key Challenges of AI Development – Insights from an Industry-Academia Collaboration |
Conference: | The Upper-Rhine Artificial Intelligence Symposium (4th UR-AI Symposium), Villingen-Schwenningen, 19 October 2022 |
Author: | Alexander Melde, Manav Madan, Paul GavrikovStaff MemberORCiDGND, David Hoof, Astrid Laubenheimer, Janis KeuperStaff MemberORCiDGND, Christoph Reich |
Year of Publication: | 2022 |
First Page: | 112 |
Last Page: | 121 |
Parent Title (English): | The Upper-Rhine Artificial Intelligence Symposium (UR-AI 2022) : AI Applications in Medicine and Manufacturing |
Editor: | Christoph Reich, Ulrich Mescheder |
ISBN: | 978-3-00-073638-4 (e-ISBN) |
ISBN: | 978-3-00-073637-7 (Print) |
URL: | https://www.researchgate.net/publication/364343172 |
Language: | English | Inhaltliche Informationen |
Institutes: | Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019) |
Institutes: | Bibliografie |
Tag: | Artificial Intelligence; Challenges in Action Recognition; Collaboration of Academia and Industry; MLOps; Machine Learning; Model Search | Formale Angaben |
Relevance: | Konferenzbeitrag: h5-Index < 30 |
Open Access: | Open Access |
Diamond | |
Licence (German): | Creative Commons - CC BY - Namensnennung 4.0 International |