The Manchester Centre for AI Fundamentals and Manchester's ELLIS Unit are co-hosting a series of seminars featuring expert researchers working in the fundamentals of AI.
Abstract
Although artificial intelligence (AI) has improved remarkably over the last years, its inability to deal with uncertainty severely limits its future applications. In its current form, AI cannot confidently make predictions robust enough to stand the test of data generated by processes different (even by tiny details, as shown by ‘adversarial’ results) from those seen at training time. While recognising this issue under different names (e.g. ‘overfitting’ or 'domain adaptation'), traditional machine learning seems unable to address it in non-incremental ways. As a result, even state-of-the-art AI systems suffer from brittle behaviour, and find it difficult to operate in new situations.
The epistemic AI project re-imagines AI from the foundations, through a proper treatment of the "epistemic" uncertainty stemming from our forcibly partial knowledge of the world. Its overall objective is to create a new learning paradigm designed to provide worst-case guarantees on its predictions, thanks to a proper modelling of real-world uncertainties. The project aims to formulate a novel mathematical framework for optimisation under epistemic uncertainty, radically departing from current approaches that only focus on aleatory uncertainty. This new optimisation framework will in turn allow the creation of new ‘epistemic’ learning settings, spanning all the major areas of machine learning: unsupervised learning, supervised learning and reinforcement learning, as well as generative AI.
Last but not least, the project aims to foster an ecosystem of academic, research, industry and societal partners throughout Europe able to drive and sustain the EU’s leadership ambition in the search for a next-generation AI.
Short bio
Fabio Cuzzolin was born in Jesolo, Italy. He received the laurea degree magna cum laude from the University of Padova, Italy, in 1997 and a Ph.D. degree from the same institution in 2001, with a thesis entitled “Visions of a generalized probability theory”. He was a researcher with Politecnico di Milano in Milan, Italy, and a postdoc with the UCLA Vision Lab at the University of California at Los Angeles, led by Prof Stefano Soatto. He later joined the Perception team at INRIA Rhone-Alpes, Grenoble, as a Marie Curie fellow.
He joined Prof Phil Torr's vision group at the Department of Computing of Oxford Brookes University in September 2008, and has been a Professor of Artificial Intelligence since January 2016. In 2012 he funded the Visual Artificial Intelligence Laboratory (VAIL), currently funded by the European Union, URKI, Innovate UK, the British Council and the Leverhulme Trust for around £3M. Since 2024 he is the inaugural Director of the Institute for AI, Data Analysis and Systems (AIDAS) at Oxford Brookes University, and the university's academic liaison with the Turing University Network.
Fabio is a world leader in the field of imprecise probabilities and random set theory, to which he contributed an original geometric approach. His Lab's research spans artificial intelligence, machine learning, computer vision, surgical robotics, autonomous driving, AI for healthcare as well as uncertainty theory. The team is pioneering frontier topics such as machine theory of mind, epistemic artificial intelligence, evolving and universal AI, neural operators under uncertainty, neurosymbolic reasoning and continual semi-supervised learning.
He is the Coordinator of the H2020 FET Open project "Epistemic AI" (E-pi), and was Scientific Officer for the H2020 project 779813-SARAS (Smart Autonomous Robot Assistant Surgeon). Fabio is the author of circa 150 publications, published or under review, including 5 books and around 40 journal papers and book chapters, with a total impact factor of around 280. His work won a Best Paper Award at PRICAI’08, a Best Poster at ISIPTA’11 and the 2012 MLVR Summer School, a nomination for Best Paper and an Outstanding Reviewer Award at BMVC’12, a Best Paper award at the IJCAI 2022 AI4AD workshop and recently the Best Student Paper prize at IJCLR 2022.
