Castles and Coprolites

Chemistry has always been at the heart of my academic interests. As a teenager, I was obsessed was rocks. I remember staring at mineral specimens and trying to figure out how the chemistry I was learning at school related to the complex chemistry of rocks, and how crystals formed. Funnily enough though, my favourite part of chemistry at school was organic chemistry. I loved the logic of it; it felt like a giant puzzle that you could decode if you learned all the pieces, the functional groups and how they interacted. There was something deeply satisfying about seeing patterns emerge and understanding how molecules behaved. I wavered for a long time between choosing Chemistry or Geology to study at university, but in the end, I had a panic about the maths and opted for Geography instead. It all worked out in the end, though, because that decision opened the door to archaeology and the interdisciplinary world I now inhabit. And as much as I still love rocks, it turns out people can be qui...

Or maybe it is, but it certainly isn’t what I thought it was, a year or so ago. There’s been a lot of attention lately on tools like ChatGPT and Copilot, systems that generate text, answer questions, and increasingly have found their way into academic life. These are examples of what are termed large language models (LLMs), and the term AI in everyday use is usually referring to these LLMs. But as I have come to realise, they represent just one branch of ‘artificial intelligence'. AI has long played a quieter role to help researchers make sense of the vast and complex datasets produced by advanced imaging techniques, environmental modelling, and material analysis.  Since joining Durham in April this year , and working to develop applications of XR-CT (X-ray computed tomography) for heritage science , I’ve become increasingly aware of just how crucial AI is for making sense of this kind of data. XR-CT lets us look inside objects such as pottery, bones, sediments, without breaking t...