Category: News

The JGI team had a great time at the AI UK conference a few weeks ago. There were some fantastic University of Bristol demonstration stands at the conference that our Turing Liaison team coordinated. Read more about the demonstrations below and their experience of AI UK below.

AI for Collective Intelligence Hub, Informed AI Hub and PrO-AI (Practice-Oriented Artificial Intelligence) Centre for Doctoral Training

To address society’s most pressing challenges in health, sustainability and security, we must be able to reliably engineer important new kinds of systems that communicate, collaborate and co-ordinate successfully – Collective Intelligence. The AI for Collective Intelligence Hub, Informed AI Hub and PrO-AI (Practice-Oriented Artificial Intelligence) Centre for Doctoral Training teamed up to show how collective artificial intelligence (AI) systems working together to address complex challenges like flood response and urban traffic management.

Demonstrators:

• Sidharth Jaggi, Director of Informed AI Hub and Professor, School of Mathematics
• Peter Flach, Director, PrO-AI (Practice-Oriented Artificial Intelligence) Centre for Doctoral Training and Professor of Artificial Intelligence, School of Computer Science
• Vicky Walter, Senior Research Hub Manager, AI for Collective Intelligence Hub
• Harriet Lee, Hub Project Manager, Informed AI Hub
• Demos from the CDT:
  • OKUMA: A Digital Health Tool for Type 2 Diabetes in Nigeria – Tim Arueyingho, PG, Digital Health and Care (PhD)
  • Multi-Agent Systems for Sustainability – Daniel Collins and Yining Yuan, PhD students, School of Engineering Mathematics and Technology
  • Beyond Expected Patterns in Type 1 Diabetes – Isabella Degen, PG, Interactive Artificial Intelligence (PhD)
  • Visualizing AI: Exploring Large-Scale Image and Video Collections – Grant Stevens, EPSRC Doctoral Prize Fellow, School of Physics and Otto Brookes, PG, Interactive Artificial Intelligence (PhD)
  • AR for AI – Alex Davies, PhD Student, School of Computer Science
  • Multi-robot formations – Jan Blumenkamp and Kazi Ragib Ishraq Sanim, Researchers from the Prorok Lab

Summary from demonstrators:

We had a great time at AI UK, where our demonstrators did an excellent job presenting their research to attendees at the conference. Our stand “From Theory to Practice in Collective AI Systems” brought together different EPSRC funded AI initiatives led by the University of Bristol (AI for Collective Intelligence Hub, Informed AI Hub, Interactive AI CDT, PrO-AI CDT) highlighting the impressive range of AI research happening across the University.

Our schedule of demonstrations covered a range of AI applications;  from digital health tools for diabetes care to multi-agent systems for sustainability, interactive visualisations of AI models, and robotics research. The demonstrators handled a steady stream of visitors, explaining their work with enthusiasm and an impressive ability to communicate complex ideas to both technical and non-technical audiences. When not at the stand, we all took the opportunity to explore the conference, talk to colleagues in the sector, attend AI talks and explore different perspectives on the latest developments in the field.

Overall, it was a really valuable experience, and we’re looking forward to more opportunities to showcase our AI community in the future!

Isambard-AI national AI research infrastructure (AIRR)

Isambard-AI’s stand provided visitors a unique opportunity to interact directly with Isambard-AI via AI applications running directly on the supercomputer. Researchers can sign up for an account at the Bristol Centre for Supercomputing (BriCS) stand and try it out for use in your own research.

Demonstrators:

• Simon McIntosh-Smith, Professor in High Performance Computing, School of Computer Science and Project Lead, Bristol Centre for Supercomputing
• Emma Rose, Centre Manager, Bristol Centre for Supercomputing
• Emily Coles, Communications Manager, Strategic Communications and Marketing Management Team
• Fan Yang-Turner, AI Supercomputing Infrastructure Lead, Bristol Centre for Supercomputing
• Matt Williams, AI Supercomputing Infrastructure Specialist, Bristol Centre for Supercomputing
• Richard Gilham, AI Supercomputing Infrastructure Specialist, Bristol Centre for Supercomputing

Summary from demonstrators:

Colleagues from the Bristol Centre for Supercomputing (BriCS) were proud to showcase Isambard-AI at AI UK 2025, giving attendees a first-hand look at the UK’s fastest AI supercomputer. As part of the University of Bristol’s commitment to driving cutting-edge research in AI and high-performance computing (HPC), our stand became a bustling focal point for discussions on how Isambard-AI can accelerate innovation with positive impacts across science, industry, and society.

We were delighted to speak with a steady stream of researchers, academics, industry leaders and policy makers, eager to understand how BriCS and the University of Bristol are shaping the future of AI computing in the UK. With the sheer computational power of Isambard-AI, capable of supporting everything from training large-scale AI models to complex climate science and healthcare projects, there was plenty to talk about.

Day 1 of AI UK was particularly exciting as we welcomed Feryal Clark, Minister for AI and Digital, and Jean Innes, CEO of the Alan Turing Institute, to the stand. Both were keen to handle a chip identical to those powering Isambard-AI and discuss the potential use of waste heat water for local infrastructure.

AI UK 2025 was an exciting opportunity for BriCS to demonstrate both the capabilities of Isambard-AI and the unprecedented rate of its construction. What an opportunity to show how the University is living up to the accolade of AI University of the Year. The enthusiasm and engagement we saw at the stand was palpable and we look forward to building on new connections in the months ahead.

The University of Bristol runs an internal process to applying for time on Isambard-AI phase 1. The next application round is due to launch by late-March, for projects starting in early May. The application process will follow a similar format to Isambard 3; further details to follow. The national call for access to Isambard-AI phase 1 is run through UKRI. This expression of interest call is open to all researchers throughout the UK.

Towards Wearable Assistive AI

Footage captured from wearable cameras are the base of assistive technologies. By analysing this footage, the intention, skill and memory of the user can be recorded. This will enable assistance, improving one’s skill and augmenting one’s memory. Advanced research at the University of Bristol is making steps towards this future. This stand allowed visitors to experience the latest in Egocentric Vision covering hardware advances through their partnerships with major players like Meta and Apple.

Demonstrators

• Rhodri Guerrier, PhD student, School of Computer Science
• Siddhant Bansal, PhD student, School of Computer Science
• Michael Wray, Lecturer, School of Computer Science

Summary from demonstrators:

We all found the experience at the AI UK conference both enjoyable and valuable. The stand was setup for our arrival so we could get started straight away and focus on presenting, which was very nice. We found it really enjoyable being able to talk with so many people from so many different backgrounds. Not only did this help us improve our presentation skills, as we had to adapt to each new person, it also exposed us to different opinions and use cases of the technology that we would not normally be exposed to when just working within our lab. For example, we talked with lawyers, regulators, business leaders, government officials and many more. It is not often that we get to discuss our work outside the scope of purely research, so we found this both insightful and challenging. We also really liked the format as it allowed us to have a nice break during the talks before getting started again when more attendees emerged on the exhibition floor. Finally, we would like to say a huge thank you to the organisers for all their help and to the catering staff as well. The food was delicious.

All University of Bristol researchers (from PhD student and up) are entitled to a day of free data science support from the Ask-JGI helpdesk. Just email ask-jgi@bristol.ac.uk with your query and one of our team will get back to you to see how we can support you. You can see more about how the JGI can support data science projects for University of Bristol based researchers on our website.

We support queries from researchers across all faculties and in this blog we’ll tell you about some of the researchers we’ve supported from the Faculty of Arts, Law and Social Sciences here at the University of Bristol.

YouTube Comment Scraping

One researcher got in touch for advice about scraping data from the YouTube comment section. They were interested in collecting all the comments for a set of videos so that they could analyse sentiment and engagement with the videos’ content. While this wasn’t something we’d done before, we spent some time reading about the subject and found that the official YouTube Data API (https://developers.google.com/youtube/v3) was suitable for this work (no 3rd party tools needed!). We discussed this with the client, and based on their needs, suggested that we use the official Python client as a simple and flexible way to interact with this data source.

While the researcher was relatively new to Python, they expressed an interest in learning for the project. While we wrote the code and documentation for the comment scraping pipeline, the researcher went through some of the Python courses that the JGI offers (https://bristol-training.github.io/). This way, we were able to meet with them again after a few weeks to go through the code together, and make sure everything was understood and in a usable state.

Cross-platform comparison of social media posts quoting a Greek poet

One query we supported revolved around a cross-platform comparison of social media posts quoting a specific Greek poet. The study aimed to collect posts from TikTok, Tumblr, and Pinterest to identify the most popular poem quotes and analyse how frequently they were misattributed. While researchers working with platforms like X, Facebook, or YouTube can often find established data collection methods, niche platforms pose unique challenges. A key difficulty was determining the right data sample size across platforms. Three of them form unique social networks with different engagement metrics, making it unclear how many posts would be sufficient for a meaningful analysis. Through collaboration, we worked together to understand the research question better and adapted methodological aspects of this research design. We also explored alternative analysis approaches, including network analysis, to better understand how posts spread on these platforms and to assess the reach of these quotations.

Code review for cross-sectional survey on food insecurity

A PhD student working in anthropology and social policy attended some of the free coding courses the JGI offers (https://bristol-training.github.io/).  Since this initial encounter with R, they have been using R for their data analysis. As their supervisors do not work with R, the student found themselves in need of additional feedback on their R based project. Specifically, they wanted to make sure that their approach to and interpretation of Principle Component Analysis is on the right track. So the student contacted Ask-JGI for a second opinion on their analysis, and they wish to have their R code reviewed to make sure it was all working correctly. We are happy to have offered them the support they needed and to confirm that they were on the right track!

Fuzzy Matching for Job Postings Analysis

We assisted researchers from the Business School with the data collection process for their job postings analysis. This involved extracting and analysing job postings data to understand how companies invest in specific skill sets, especially those related to cutting-edge technologies like AI.

One of the initial hurdles we faced was matching company names from the provided list with those found in job postings. Even though this might sound straightforward, company names can vary significantly. We encountered abbreviations and slight variations in spelling. A simple exact match would not be sufficient. That’s where fuzzy matching came into play. We used algorithms that can identify similar strings, even with minor differences. This allowed us to accurately link our company list to job postings, even when the names weren’t perfectly aligned. This was crucial for capturing the broadest possible range of relevant data.

The sheer volume of job posting data presented another significant challenge. We were dealing with potentially millions of records, processing this data requires substantial computational resources. To tackle this, we utilized High-Performance Computing (HPC). HPC allows us to distribute the workload across multiple processors, significantly accelerating the data processing and analysis. This was essential for handling the massive datasets and complex algorithms involved in fuzzy matching.

Visualising historical networks of Chinese and Eurasian elites in the British Empire

We are working with a PhD researcher in the History department. In this case, the Ask-JGI team is offering assistance in exploring the use of network visualisation and analysis tools. These might be otherwise not as easily accessible to researchers when the methods are considered interdisciplinary in their home discipline. And Ask-JGI helps to bridge that gap. The PhD project involves mapping the network of powerful individuals in the British Empires across the late 19th and early 20th centuries. This network is complex, as individuals are connected with one another through different types of ties, such as family relations, alumni networks, business partnerships, and political organisations. Visualising these ties as a network of heterogenous nodes and edges helps the researcher to effectively communicate the subject of the research. Through our conversations, we bring clarity to concrete next steps in the analysis of the dataset. We also offered learning resources and advice on alternative analytical methods that can be applied to distil insights on how interpersonal connections and social capital might have translated to power in the historical context.

In January 2025, the Turing Liaison team, based in the Jean Golding Institute for interdisciplinary data science, organised a book launch for the new AI and Literature Routledge Handbook.  

The Handbook was co-edited by Genevieve Liveley, a Professor of Classics at the University of Bristol, and a Turing Fellow. “It brings together 30 new and exciting ideas about the incredible intersection between AI and Literature”, says Genevieve. “We’ve got Computer Scientists, artists, poets and some of the leading names in AI Science”. 

This Handbook combines early career contributors with some of the best-known names in the digital humanities and computational literary studies. “I think the book does this amazing thing where it has all these different minds and ways of coming at the topic,” says Victoria Punch, a book contributor and a PhD researcher at the Universities of Bristol and Exeter. “I think there is always something really exciting about getting together with people from different disciplines.” 

The launch was hosted at the SS Great Britain in Bristol – a heritage site and one of the most important engineering experiments that changed the flow of information, ideas, fashion, culture and literature. Similarly, this Handbook was another important experiment in what feels like a transformative moment in history – the rise of AI, and how this intersects across sectors. 

“There’s never been a better time to look at AI,” says Kate Devlin, another book contributor and Professor of Artificial Intelligence & Society, King’s College London. “This book deals with many different aspects of how AI intersects with literature in a way that it has had its origins in the past in the stories we tell, right through to the science fiction fields we have today.”  

AI and Literature explores a variety of theories and approaches when AI is deployed in literary contexts. “One of the reasons why science fiction is so important is that it helps us understand the stories that we tell about AI,” says co-editor, Will Slocombe, Reader in English, University of Liverpool. “We talk about technologies as if they are neutral things but they are surrounded by stories and discourse.”  

It offers a fresh perspective on the past, present, and future of AI and literature that will appeal to students and scholars with relevant interests across a range of subjects, including AI Engineering, Classics, Computing, Digital Humanities, English, Ethics, Film and Television, Law, and Narratology.  

Pick up your copy now: AI and Literature Routledge Handbook