Speaker: Vaishak Belle

Title: Towards Tractable, Interpretable and Responsible AI

Date/Time: June 20, 2019  /  13.40-14.30

Place: FENS L067

Abstract: Artificial Intelligence (AI) provides many opportunities to improve private and public life, and it has enjoyed significant investment in the UK, EU and elsewhere. Indeed, discovering patterns and structures in large troves of data in an automated manner is a core component of data science. Machine learning currently drives applications in computational biology, natural language processing and robotics. However, such a highly positive impact is coupled to a significant challenge: when can we convincingly deploy these methods in our workplace? For example, can we understand the decisions made by these automated systems? Can we provide prior knowledge and suggestions to the learning modules? And finally, should the automated system be held responsible for the decisions it takes? In this talk, we report on some recent progress in the field on these themes, especially covering some approaches touching on tractable learning. .

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. More information can be found at: //vaishakbelle.com/lab

Contact: Esra Erdem