Castles and Coprolites

I attended a fascinating lunchtime seminar this week on the potentials for archaeology and AI, which brought together perspectives from across different departments at Durham University . One of the talks was by Prof Jelena Ninic , a civil engineer whose work focuses on structural assessment and maintenance of infrastructure, particularly ageing transport networks such as railways and bridges. Her presentation discussed how heritage is not something separate from modern engineering but fundamentally embedded within it. Much of the infrastructure we rely on every day is in fact, historic. Across the UK and elsewhere, roads, bridges, railways, tunnels, often have origins in the nineteenth century, if not earlier. These are complex, evolving systems that have been repaired, adapted, and extended over decades or centuries. As such, they present a series of challenges that are as much archaeological as they are engineering in nature.  A key challenge is maintaining historic infrastructu...

Last month I spent a day at the Daresbury laboratory for a RICHeS data day, to think about how we will manage data for our facilities and make it accessible. This is a daunting task, but a challenge I am excited to tackle, working together with the Heritage Science Data Service team. One of the highlights of the day was touring the Visual Computing Labs. Seeing full laser scans and digital models of entire cities (in this case Liverpool) was genuinely awe inspiring. These aren’t just impressive visualisations, but complex data‑rich representations that can be interrogated. In archaeology, where we constantly move between scales, from microscopic residues to landscapes and infrastructures, the potentials are endless. What might we learn and better understand if we can apply these technologies to ancient cities? Another highlight was seeing virtual museums integrated with a treadmill system. The user sees a virtual environment and feels as if they are moving through it. It felt like an...

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...