Thomas Lukasiewicz: Learning & Reasoning in Artificial Intelligence

The talk will give an overview of the research in the intersection of knowledge representation and reasoning with machine and deep learning, ranging from commonsense reasoning in deep-learning-based natural language processing as well as deep learning with explanations to deep-learning-based approaches to logical reasoning and structured data extraction from unstructured sources (such as natural language text and videos). In particular, the talk will also present a novel approach to ontology reasoning that is based on deep learning rather than logic-based formal reasoning. More specifically, a new model for statistical relational learning is introduced on top of deep recursive neural networks. Compared with one of the best logic-based ontology reasoners on large standard benchmark datasets, the implemented system shows a high reasoning quality, while being up to two orders of magnitude faster.

Bio: Thomas Lukasiewicz is a Prof of Computer Science in the Department of Computer Science at the University of Oxford and a Turing Fellow at the Alan Turing Institute in London. Prior to this, he was holding a prestigious Heisenberg Fellowship by the German Research Foundation (DFG), affiliated with the University of Oxford, TU Vienna, Austria, and Sapienza University of Rome, Italy. His research interests are in artificial intelligence (AI), machine learning, and information systems, including especially knowledge representation, uncertainty in AI, and deep learning. He received the IJCAI-01 Distinguished Paper Award, the AIJ Prominent Paper Award 2013, and the RuleML 2015 Best Paper Award. He is area editor for the journal ACM TOCL, associate editor for the journals JAIR and AIJ, and editor for the journal Semantic Web and Heliyon.

*Sponsors*
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