London Machine Learning Meetup
The field of statistical relational learning aims at unifying logic and probability to reason and learn from relational data. Logic provides a means to codify high-level dependencies between individuals, enabling descriptive clarity in the knowledge representation system, and probability theory provides the means to quantify our uncertainty about this knowledge. In this talk, we report on some recent progress in the field while touching on the themes of interpretability and responsibility in AI. If time permits, we will also discuss very recent work on automating responsible decision making, by explicitly capturing the blame that should be accorded to a system in regards to a decision taken by it.
Bio: Vaishak Belle is a Chancellor’s Fellow at the School of Informatics, University of Edinburgh, an Alan Turing Institute Faculty Fellow, and a member of the RSE (Royal Society of Edinburgh) Young Academy of Scotland. Vaishak’s research is in artificial intelligence, and is motivated by the need to augment learning and perception with high-level structured, commonsensical knowledge, to enable AI systems to learn faster and more accurate models of the world. He is interested in computational frameworks that are able to explain their decisions, modular, re-usable, and robust to variations in problem description. He has co-authored over 40 scientific articles on AI, and along with his co-authors, he has won the Microsoft best paper award at UAI, and the Machine learning journal award at ECML-PKDD. In 2014, he received a silver medal by the Kurt Goedel Society.
*Sponsors*
Man AHL: At Man AHL, we mix machine learning, computer science and engineering with terabytes of data to invest billions of dollars every day.
Evolution AI: Build a state-of-the-art NLP pipeline in seconds.
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