The UKRI portfolio aims to support a robust and interconnected AI vision. To uphold the UK’s leading position in AI research and fulfil its potential for society, the entire ecosystem must function cohesively, linking AI researchers, innovators, and practitioners. The National Edge AI Hub is one of nine Hubs that UKRI supports.
| Theme | Leading Institution | Name | Hub Focus |
|---|---|---|---|
| AI for Real data | Newcastle University | National Edge AI Hub for Real Data: Edge Intelligence for Cyber-disturbances and Data Quality | This hub explores edge AI: the combination of edge computing and deploying AI closer to the source of the input data. The hub focuses on the effect of cyber disturbances on the effectiveness and resilience of edge AI, with a particular focus on cyber threats. |
| University of Edinburgh | CHAI – EPSRC AI Hub for Causality in Healthcare AI with Real Data | Developing causal AI solutions that will predict outcomes of interventions and help choose personalised treatments, thus transforming health and healthcare. The hub will develop novel methods to identify and account for causal relationships in complex data. | |
| University of Bristol | AI for Collective Intelligence (AI4CI) | Developing new machine learning and smart agent technologies designed to deal with real time, dynamic data streams generated across hybrid systems of interacting individuals distributed over space and/or networks. The hub will address these challenges in the context of real-world cases: healthcare, pandemics, cities, finance, and the environment. | |
| University College London | AI Hub in Generative Models | The hub aims to create tools that industry, science and government can use to fine-tune generative models (GMs), and make these tools widely available. Specific areas of research include Open LLMs and applications to creative design, Molecules and drug design and Healthcare. | |
| AI for Research and Engineering | University of Liverpool | AI for Chemistry: AIchemy | The hub will carry out research in foundational AI methods, AI for experimental and computational chemistry, and autonomous, closed-loop robotics for chemical discovery. |
| University of Edinburgh | AI for Productive Research & Innovation in eLectronics (APRIL) Hub | The hub aims to develop AI tools for cutting development times for everything from new, fundamental materials for electronic devices to complicated microchip designs and system architectures, leading to faster, cheaper, greener and overall, more power-efficient electronics. | |
| Fundamentals of AI | Lancaster University | ProbAI: A Hub for the Mathematical and Computational Foundations of Probabilistic AI | Probabilistic AI: embedding probability models, probabilistic reasoning and measures of uncertainty within AI methods. |
| University of Bristol | Information Theory for Distributed AI (INFORMED-AI) | The hub aims to develop theoretical foundations and algorithmic approaches for the design of intelligent distributed systems that are effective, resilient and trustworthy. | |
| University of Oxford | Mathematical Foundations of Intelligence: An “Erlangen Programme” for AI | The hub will focus on using mathematical principles, particularly in geometry, topology, and probability, to enhance AI methods. |