AI researchers and mathematicians are launching a coordinated programme of workshops, hackathons, and training events to push machine learning tools into fields that have barely touched them—economics, biomedical science, engineering, and the humanities. The core problem is a gap. AI methods have advanced rapidly, but many domain specialists—and potential users in business, industry, and government—have little experience with them. Meanwhile, mathematicians and statisticians, who built the foundations of data science, are not yet fully engaged in improving AI systems themselves. The programme aims to bridge both sides: exporting AI tools to non-AI communities, and importing mathematical expertise—from algebra, geometry, and topology—to refine AI pipelines. If successful, the programme could accelerate scientific discovery across multiple fields. Medical researchers might use AI to spot patterns in patient data that humans miss. Social scientists could analyse complex behavioural datasets. Engineers could optimise designs or reduce computational bottlenecks. The programme also plans to produce “How to use AI” guides, establish a virtual working group, and offer pump-priming project funding to seed collaborations. The organisers—the International Centre for Mathematical Sciences, the Isaac Newton Institute, and the UK Knowledge Exchange Hub—envisage a step change in how AI and mathematics work together, benefiting both fundamental research and UK industry.
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The last decade has seen an explosion of AI with spectacular advances in recent years that have brought AI and its use to the everyday life of many. Its methodologies are advancing fast and AI tools are becoming more and more sophisticated. Yet, there are many problems that AI could help solve but where the communities of AI and domain specialists have not yet met to explore and scope the possibilities and a wide range of potential users who have had little experience of machine learning and other AI tools. The primary goal of our proposed programme of activities under the banner “AI and the Mathematical Sciences” will foster the interaction, collaboration and exchange of knowledge between the AI community and non-AI communities with the goal to encourage wider use of new AI capabilities both within the mathematical sciences and beyond. Mathematics and statistics form strong pillars of data science and AI. From that point of view, AI is an extension of the mathematical sciences. On the other hand, AI has the power to support with mathematical science research. As tools become ever more powerful, they can help generate conjectures, optimise code, reduce computational complexity, and even suggest proofs. The activities in our programme will thus “export” AI out from the mathematical sciences to other domains such as medical sciences, social sciences, humanities, and to business, industry and government (BIG). Or equally, activities may be directed at communities of mathematicians to tackle their pressing problems, and AI is thus “imported”. With all these interactions, scientific discoveries in the domain areas will be accelerated with the use of new AI tools. They will also foster strong inter-(and intra-)disciplinary partnerships by bringing together AI and non-AI communities. The mathematical sciences also play an important role in the development of the AI tools themselves with more subject areas being drawn in to improve and control AI systems. As well as statisticians and numerical analysts, more theoretical areas such as algebra, geometry and topology are increasingly being employed to finetune AI pipelines. To foster these interactions and to draw in more mathematical scientists to apply their knowledge and expertise to AI challenges is a further goal of “AI and the Mathematical Sciences”. Co-organised by the International Centre (ICMS) in Edinburgh, the Isaac Newton Institute (INI) in Cambridge, and the UK Knowledge Exchange Hub for the Mathematical Sciences (KE Hub) operating virtually, our programme of activities will draw on established UK-wide networks of mathematical scientists supported by professional research and innovation enablers. In this first phase, a portfolio of activities ranging from workshops, schools, hackathons, summits, and challenge meetings are planned. These activities will increase adaptation of AI in a number of non-AI science communities including economics, finance, engineering, social sciences and biomedical sciences. In addition, we will deliver training, establish a new virtual Working Group and some “How to use AI” guides, and develop and provide access to pump-priming project funding that should enhance the take up of AI by academics in the mathematical sciences in their KE activities. Ultimately, we envisage a step change in the integration of AI and the mathematical sciences for the benefit of scientific research and UK PLC.
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