Harnessing AI with Limited Data

Professor Qiang Shen

Abstract
AI stands to transform nearly every aspect of contemporary life. Much of its success is driven by deep learning techniques that rely on vast quantities of data. Yet, a pivotal question emerges when faced with limited data for a new problem, especially if such data is ambiguously characterised. Can AI maintain its efficacy under these constraints? This talk delves into contributions addressing this query, highlighting how fuzzy rule interpolation (FRI) enables approximate reasoning in situations marked by sparse or incomplete knowledge. This is particularly relevant when traditional rule-based inference mechanisms falter because observations do not align with existing rules.

The talk will centre on a prominent subset of FRI techniques, Transformation-based FRI (T-FRI). Kicking off with an exploration of the foundational T-FRI approach, it will segue into a concise overview of its expanded repertoire, each addressing certain shortcomings inherent to the original method. Subsequently, real-world applications of these methodologies will be showcased, exemplifying their potency in tackling formidable challenges in domains like network security and medical diagnosis. These cases will underscore AI's capability to function effectively even with incomplete knowledge and ambiguous data. The talk will conclude with a brief look at promising future directions in this vital area of research.

Professor Qiang Shen received a PhD in Computing and Electrical Engineering (1990) and an Honorary DSc in Computational Intelligence (2013). He holds the Established Chair of Computer Science and serves as Pro Vice-Chancellor: Faculty of Business and Physical Sciences at Aberystwyth University. He is a Fellow of the Royal Academy of Engineering and a Fellow and Council Member of the Learned Society of Wales. He had the honour of being selected as a 2012 London Olympic Torchbearer in celebration of Alan Turing’s centenary. Qiang is currently Chair of the UK Research Excellence Framework (REF) 2029 Subpanel for Computer Science and Informatics and a member of the People, Culture and Environment Pilot Panel, having previously served on the panels for REF 2014 and REF 2021. He is the 25th recipient of the IEEE Fuzzy Systems Pioneer Award. He has authored three research monographs and over 500 peer-reviewed papers, many of which have received outstanding journal or best conference paper awards, including several directly related to this talk.

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