Title
Systems that learn and reason
Abstract
After the amazing breakthroughs of machine learning (deep learning or otherwise) in the past decade, the shortcomings of machine learning are also becoming increasingly clear: unexplainable results, data hunger and limited generalisability are all becoming bottle necks. In this talk we will look at how the combination with symbolic AI (in the form of very large knowledge graphs) can give us a way forward, towards machine learning systems that can explain their results, that need less data, and that generalise better outside their training set.
Bio
Frank van Harmelen has a PhD in Artificial Intelligence from Edinburgh University, and has been professor of AI at the Vrije Universiteit Amsterdam since 2001, where he leads the Learning and Reasoning group. He was one of the designers of the knowledge representation language OWL, now in world-wide use, he co-edited the standard reference work in his field (The Handbook of Knowledge Representation), and received the Semantic Web 10-year impact award for his work on the Sesame engine for knowledge graphs. He is a Fellow of the European Association for Artificial Intelligence, member of the the Dutch Royal Academy of Sciences (KNAW) and a foreign member of the Belgian Academy of Sciences, and is adjunct professor at Wuhan University of Science and Technology in China.
Speaker is presenting online.
Practical
The Tuesdays seminars series are devoted to various topics relevant for the Integreat´s research focus. Presenters from the Integreat community and beyond have 40 minutes to present, followed by a group discussion.
Seminars are open for attendance for everybody.
For those unable to attend in person: https://uio.zoom.us/j/62535744013
Point of contacts:
- Organiser and moderator: Egor Kostylev, University of Oslo
- Integreat Administrative Leader Maria Dikova, University of Oslo