Erlangen AI Hub: Mathematical Foundations of Intelligence
The Erlangen AI Hub exists to revolutionise the application of modern mathematics to understand
AI, unifying and expanding the field to unlock new, more intelligent systems. The EPSRC-funded research programme,
partnered by industry, brings together leading minds from across the UK’s mathematical, algorithmic and computational
communities. It employs foundational tools to break new ground in AI, and redefine its future use to benefit science, industry,
the economy and society.
The Erlangen model
In 1872, Felix Klein published his Erlangen Programme, which brought a revolutionary and unifying perspective to geometry via symmetries formalised by algebra. With a growing understanding of the geometric nature of data and intelligence, the Erlangen Programme for AI draws inspiration from Klein’s work. It will galvanise new AI technologies based on solid foundations of pure mathematics, harnessing the power of classical theories and encouraging new ones, in mathematics and beyond, to empower the next generation of AI.
Building a community
Led by Oxford, the hub network consists of six academic institutions spanning the UK, bringing together leading researchers in mathematics, algorithms, and computing. The research programme aims to remove barriers between fields and unify a diverse cohort, exploiting tools from currently underexplored mathematical fields to understand and advance AI. The hub also aims to attract theoreticians to new problems and applications in AI in both scientific and industrial settings.
Advancing AI research
The world-leading research at the Erlangen AI Hub will apply powerful ideas from geometry, topology, and other branches of modern mathematics to provide rigorous solutions to four key areas that underlie modern AI systems:
- Understanding Data: Introduce mathematical methodologies for discovering and expressing hidden structures in data, which then can be exploited by new machine learning models.
- Understanding Machine Learning Models: Characterise machine learning models mathematically to understand their success and failure, and ensure their robustness, reliability, and fairness.
- Understanding Learning: Use mathematics to understand learning and optimisation algorithms and enable them to benefit from structures in machine learning models and data.
- Understanding Decision-Making: Enable the building of self-adaptive, largely autonomous AI systems that understand their limitations, increasing reliability and minimising human intervention.
From science to society
The Erlangen AI Hub is one of nine AI research hubs across the UK funded by EPSRC. The hub sits at the centre of a collaborative network of stakeholders, fusing academic and industry knowledge with real world action, bringing world-leading research to applied settings. It aims to achieve rapid and enduring impact in science, industry, government and beyond.
Academic leadership team
Hub Directors Professor Michael Bronstein at the University of Oxford, Professor Anthea Monod at Imperial, and Professor Jeffrey Giansiracusa at Durham University will be supported by node leads Professor Jacek Brodzki at University of Southampton, Primoz Skraba at Queen Mary University London, and Professor Ran Levi at University of Aberdeen.