Cambridge scientists are using artificial intelligence technology to boost research in a range of fields – from better understanding human intelligence, to describing turbulent flows, to freeing computer systems from the cloud – after securing new Fellowships launched to drive breakthrough discoveries.

The Encode: AI for Science Fellowships embed top AI talent in the UK’s leading labs to tackle scientific challenges and accelerate the path to real-world solutions. Three Fellowships in the first cohort are being hosted at Cambridge.

Encode Fellow Jonathan Carter is using technology originally developed for astrophysics research to decipher how humans understand physics – for example, how the human brain performs intuitive physics calculations, like predicting where a thrown ball will land. Working with Hiranya Peiris, who holds the Cambridge Professorship of Astrophysics (1909), their approach uses interpretable variational encoders, a specialised neural network that can find compact, meaningful representations in complex data. This cross-disciplinary research could advance both our understanding of human intelligence and our ability to build AI systems that learn and generalise like humans do.

Shruti Mishra, another Encode Fellow, is developing an AI system that can discover clear, understandable equations describing how turbulent flows behave across different scales. This is a long-standing challenge in physics that affects everything from weather prediction to aerospace design. Guided by Miles Cranmer, Assistant Professor of Data Intensive Science at Cambridge, Shruti is combining machine learning with symbolic mathematics to automatically produce equations that scientists can interpret and trust, rather than ‘black-box predictions’, where the decision-making process is difficult to understand. Their work has the potential to enable more accurate climate predictions and improve industrial designs.

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