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DERI Seminar with Artur d’Avila Garcez, Professor of Computer Science and Director of the Data Science Institute at City, University of London

When: Thursday, November 10, 2022, 11:00 AM - 12:00 PM
Where: Zoom

Speaker: Pro Artur d’Avila Garcez, City, University of London

Title: Neurosymbolic AI

Zoom link: https://qmul-ac-uk.zoom.us/j/81148100921

Abstract: Despite achieving much success, the deep learning approach to AI has been criticised for being "black box": the decisions made by such large and complex learning systems are difficult to explain or analyse. If the system makes a mistake in a critical situation then the consequences can be serious. The use of black box systems has obvious implications to transparency but also fairness and ultimately trust in current AI. System developers might also like to learn from system errors so that errors can be fixed. The area of explainable AI (XAI) has sought to open the black box by providing explanations for large AI systems mostly through the use of visualization techniques and user studies that seek to associate the decisions made by the system with known features of the deep learning model. In this talk, I will argue that XAI needs knowledge extraction and an objective measure of fidelity as a pre-requisite for visualization and user studies. As part of a neurosymbolic approach, knowledge extraction creates a bridge between sub-symbolic deep learning and logic-based symbolic AI with a precise semantics. I will exemplify how knowledge extraction can be used in the analysis of chest x-ray images as part of a collaborative project with Fujitsu Research to find and fix mistakes in image classification. I will conclude by arguing that knowledge extraction is an important tool, but is only one of many elements that are needed to address fairness and accountability in AI.


Bio: Artur d’Avila Garcez is Professor of Computer Science and Director of the Data Science Institute at City, University of London. He is a Fellow of the British Computer Society (FBCS) and president of the steering committee of the Neural-Symbolic Learning and Reasoning (NeSy) Association. He has co-authored two books: Neural-Symbolic Cognitive Reasoning, Springer, 2009, and Neural-Symbolic Learning Systems, Springer, 2002, and co-founded the NeSy workshop series, the longest standing gathering for the presentation of cutting-edge research in neuro-symbolic AI. Garcez is a world leader in the field of neuro-symbolic AI, having created the first neuro-symbolic system for both learning and reasoning. His research has led to publications in the journals Behavioral & Brain Sciences, Theoretical Computer Science, Neural Computation, Machine Learning, Journal of Logic and Computation, IEEE Transactions on Neural Networks, Journal of Applied Logic, Artificial Intelligence, and Studia Logica, and the flagship AI and Neural Computation conferences AAAI, NeurIPS, IJCAI, IJCNN, AAMAS and ECAI. Garcez holds editorial and senior program committee positions with all the major scientific journals and conferences in Artificial Intelligence, Neural Computation and Machine Learning.

 

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