Research groups are embedding AI into research process and results. Examples include (a) creating new AI models as a research result, using existing or new models and datasets (b) embedding existing AI models or systems into a new result (c) scraping or otherwise obtaining data (licensing, online publication, etc.) for (re) training AI models in research labs (d) creating solutions (platforms, APIs, libraries) that leverage third party AI services such as OpenAI into research results (e) using AI to create code, images, text, data and other results (in part, hopefully, with human supervision afterwards).
In the KT process, this usage can give rise to significant legal issues that need to be understood and managed by the KTO: regulatory compliance (data and AI), ownership and IP protection of results, licensing in conditions, exploitation models.
This masterclass reviews the different scenarios and presents state of the art and best practices in terms of protection and management of results, regulatory compliance and outbound licensing when AI is involved.
I am happy to be able to share my own experiences with other colleagues via various courses, and I enjoy every moment I can spend in this network. Proud to be part of the ASTP community!
Paul van Dun
KU Leuven, Belgium
Perfect balance of learning and networking.
Smiljka Vikic-Topic
University of Zagreb, Croatia
ASTP has excellent courses to immerse yourself into the world of tech transfer.
Qing Cai
Vrije Universiteit Brussel, Belgium
As always, superb topics and inspirational speakers. Great networking. Well worth the time out of the office!!! Great work HQ, thank you.
Peter Deakin
Chalmers University of Technology, Sweden
ASTP's conference helped us to network with decision-makers in the technology transfer industry and provides a great platform to discuss future innovation in this sector.
Christopher Römer
Inventtory, Germany
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