The Turing AI Acceleration Fellowships will give fifteen of the UK’s top AI innovators the resources to drive forward their ground-breaking research, the government claims.   

IT and communications are prominent areas of AI research within the programme, including:

Work on energy efficient data processing – which would support key sectors such as energy, healthcare and finance at a time when demand for data is growing exponentially.  

The fellowships form part of a major government investment in AI skills and research, including sixteen Centres for Doctoral Training in AI and conversion courses to train the next generation of AI experts.

Named after British AI pioneer Alan Turing, the £20 million fellowship scheme will be delivered by Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI), in partnership with the Alan Turing Institute and Office for Artificial intelligence. 

Some of the Turing AI Acceleration Fellows receiving investment today include:

Dr Antonio Hurtado, University of Strathclyde: Dr Hurtado aims to meet the growing demand across the UK economy to process large volumes of data fast and efficiently, while minimising the energy required to do so. His AI technology will use laser light, similar to those used in supermarket checkouts, to perform complex tasks at ultrafast speed - from weather forecasting to processing images for medical diagnostics. Being able to perform these tasks at lightning speed, with minimal energy consumption, could help to transform industries such as energy, healthcare and finance, improving efficiency, while helping the UK to meet its net zero ambitions by 2050.

·Professor Aldo Faisal, Imperial College London: Professor Faisal aims to relieve the pressures and workload on doctors and clinicians by developing an ‘AI clinical colleague’, which will be able to recommend medical interventions such as prescribing drugs or changing doses in a way that is understandable to decision makers, such as doctors, helping to them to make the best final decision on a course of action for a patient. This technology will use ‘reinforcement learning’, a form of machine learning that trains AI to make decisions, and could be used in other regulated sectors such as aerospace or energy, where there is a need for decision-making support.

Professor Damien Coyle, University of Ulster: Professor Coyle aims to develop AI technology that will be play a crucial role in new forms of wearable neurotechnologies – devices which measure signals from the brain and enable their wearer to interact with technology without movement. By enabling communication between the brain and computers that do not rely on movement, this technology could help those who are unable to communicate following a serious injury or illness. 

Dr Jeff Dalton, University of Glasgow: Dr Dalton aims to improve the capabilities and performance of virtual personal assistants. Currently, virtual assistants on the market are only capable of limited conversations, and their development is expensive. Dr Dalton will build advanced information assistants that can work with users more effectively, including asking questions back and forth, explaining their reasoning more clearly, and helping to solve complex information tasks, for example explaining the causes of climate change.  

The new Fellows will join a cohort of five Turing AI Fellows that have previously been awarded and the Turing AI World-Leading Researcher Fellowships due to be awarded in 2021. These fellowships are part of a major government investment in AI skills and research which also includes 16 UKRI Centres for Doctoral Training in AI.

 The Turing AI Acceleration Fellows are as follows

• Professor Damien Coyle, University of Ulster – AI for Intelligent Neurotechnology and Human-Machine Symbiosis

• Dr Jeff Dalton, University of Glasgow – Neural Conversational Information Seeking Assistant

• Dr Theo Damoulas, University of Warwick – Machine Learning Foundations of Digital Twins

• Professor Aldo Faisal, Imperial College – Reinforcement Learning for Healthcare

• Professor Yulan He, University of Warwick – Event-Centric Framework for Natural Language Understanding

• Dr Jose Miguel Hernandez Lobato, University of Cambridge – Machine Learning for Molecular Design

• Dr Antonio Hurtado, University of Strathclyde – PHOTONics for Ultrafast Artificial Intelligence

• Dr Per Lehre, University of Birmingham – Rigorous Time-Complexity Analysis of Co-evolutionary Algorithms

• Professor Giovanni Montana, University of Warwick – Advancing Multi-Agent Deep Reinforcement Learning for Sequential Decision Making in Real-World Applications

• Dr Christopher Nemeth, Lancaster University: Probabilistic Algorithms for Scalable and Computable Approaches to Learning (PASCAL)

• Dr Raul Santos-Rodriguez, University of Bristol - Interactive Annotations in AI

• Dr Sebastian Stein, University of Southampton – Citizen-Centric AI Systems

• Dr Ivan Tyukin, University of Leicester – Adaptive, Robust and Resilient AI Systems for the FuturE

• Dr Adrian Weller, University of Cambridge - Trustworthy Machine Learning

• Professor Christopher Yau, The University of Manchester – clinAIcan – Developing Clinical Applications of Artificial Intelligence for Cancer