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 strongly recommend the course, even if you are not going to set-up an office but you are part of the team that wants to change or improve how technology transfer is done at your institution.
Ricardo Castro
Centre for research in agricultural Genomics, Spain
Great atmosphere to learn and to share experiences.
Maria Ruiz Novales
Interface-Enterprises - University of Liege, Belgium
Great networking, new knowledge and inspiration!
Jonas Rimbäck
University of Gothenburg, Sweden
I really like the course and the networking. I would like to come again.
Nancy Goel
Qatar Foundation, Qatar
Good opportunity to discuss common issues with fellow professionals and to get updates about the community.
Giuseppe Visimberga
Leiden University, Netherlands
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