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.
Technology transfer is ever changing and a key piece for the competitiveness of your institution, this course is an excellent tool to prepare yourself for that challenge.
Ricardo Castro
Centre for research in agricultural Genomics, Spain
Organisation and speakers were great, so enthusiastic about their work and very good at transferring their knowledge.
Sarka Bartova
Research Centre Rez, Czech Republic
This is a wonderful conference to expand the horizon of my knowledge of tech transfer and commercialisation.
Seung-Ho Lee
Deltatech-Korea, South Korea
Most relevant, interesting and practical training I have ever had, very interactive an multi-disciplinary. It makes an IMPACT.
Edith van Lishout
KU Leuven, Belgium
Excellent professional gathering, perfect collection of topics and very up-to-date insight.
Ludovic Urgeghe
University of Mons, Belgium
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