Led by Professor John Shawe-Taylor, the Chair’s team is developing a series of research projects delivering AI that can improve health and education, reduce inequality, and spur economic growth – all while tackling climate change and working to preserve our oceans and forests.
The aim of the team is to work with AI researchers, policymakers and start-ups in order to highlight the power and benefits of AI in solving the UN Grand Challenges. As such, the Chair helped to establish the first Category Two institute directly focussed on Artificial Intelligence under the auspices of UNESCO at the Jožef Stefan Institute in Slovenia. The International Research Centre on Artificial Intelligence (IRCAI) aims to provide a coordination point, funding route and exploitation accelerator for approaches to the United Nations Sustainable Development Goals (SDGs) that make use of AI.
By running workshops, events and networks for experts and students alike, the Chair is raising the profile of the important role AI has in our day to day lives, and equipping people from around the world to use this technology to positively shape their futures. One such workshop brought together machine learning and artificial intelligence practitioners from research communities across Africa and the world to design activities that will strengthen the work of African AI4D researchers, policy-makers and practitioners.
ABOUT THE CHAIR’S RESEARCH
John’s main research area is Statistical Learning Theory, but his interests range from Neural Networks, to Machine Learning, to Graph Theory. Machine learning is the practice of applying artificial intelligence to computer systems in order to minimise the need for programming – the machines are able to learn and improve from experiences for themselves. Developments in machine learning can raise educational standards through improved educational apps, digital engagement, and personalised learning. John has made positive contributions to a vast number of industries with his work, most recently with his paper identifying a more accurate prediction-method for ambulance call-outs.
John is a pioneer within the field of Machine Learning and his work has helped to drive a fundamental rebirth in the field with the introduction new analysis and mapping methods. Amongst the technological areas that have benefited highly are computer vision, document classification and brain scan analysis. More recently, he has also worked on interactive learning and reinforcement learning.