AI scientists receive £20m research funding

Written by PublicTechnology staff on 30 November 2020 in News

Fifteen people have been named Turing fellows

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A total of 15 scientists across the UK will receive a share of £20m government funding for cutting edge research in artificial intelligence. 

Dr Antonio Hurtado from the University of Strathclyde and Dr Jeff Dalton from the University of Glasgow are among those to have been named Turing AI Acceleration fellows.

Hurtado has been awarded £1.16m for his work on data, while Dalton will receive £1.59m for his research on digital assistants.

Hurtado is looking 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 that used in supermarket checkouts, to perform complex tasks at ultrafast speed – from weather forecasting to processing images for medical diagnostics.

Dalton is working to revolutionise voice-based personal assistants, moving beyond the simple tasks and limited conversations performed by current assistants, such as Alexa and Siri.

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His team studies how assistants can learn to collaborate with people to accomplish longer, more complex tasks such as researching the causes of climate change or cooking a perfect Christmas dinner.

Dalton’s team will be developing novel deep learning-based methods capable of supporting long-running, more natural conversations.

This aims to enable more explainable machine reasoning, simplified assistant development and interactive agents capable of learning to ask questions and offer feedback.

Dalton said: “Being awarded the Turing AI Acceleration Fellowship is an incredible honour. We are very excited by the opportunity to accelerate progress on the next generation of virtual assistants that will transform our economy and society.  This award is key in building a world-leading research group in Scotland with state-of-the-art deep-learning hardware for conversational AI that will enable us to perform large-scale experiments on real-world datasets to maximise impact. 

“Our goal is to democratise the emerging ‘voice web’ by enabling non-experts to rapidly develop assistants using open-source technology. Our research will support the creation of a new generation of open assistants applicable to diverse sectors.”

The aim of the Turing AI Acceleration Fellowships is to give some of the UK’s most promising AI researchers the resources to accelerate their research and scale up their innovations for the real world.

The fellowships are part of the government’s ambition to establish the UK as a world leader in AI.

Hurtado said: “AI systems are key tools to make sense of huge volumes of data but consume very high levels of energy and increasingly contribute to global greenhouse gas emissions. Operating in a similar way to the biological neurons that process information in the brain, the new photonic devices will be able to process data at high speeds while reducing energy consumption, helping the UK to meet its net zero carbon ambitions by 2050.

“The new technology’s potential capability to perform complex computational tasks at ultrafast speed could see it used across a range of sectors – from meteorology forecasting to processing images at very fast rates for medical diagnostics.”

Science minister, Amanda Solloway said: “The UK is the birthplace of artificial intelligence and we have a duty to equip the next generation of Alan Turings with the tools that will keep the UK at the forefront of this remarkable technological innovation. The inspirational fellows we are backing today will use AI to tackle some of our greatest challenges head on, transforming how people live, work and communicate, cementing the UK’s status as a world leader in AI and data.”

The other researchers and projects to receive a share of the funding are: 

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


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