Fifteen researchers have been awarded Turing AI Acceleration fellowships, to develop artificial intelligence technologies.
The technologies will be developed to combat cancer, support data modelling, cybersecurity and use AI tools with crowdsourced information to track the spread of infectious diseases.
Named after AI pioneer Alan Turing, the fellowships are supported by a £20 million government investment, being delivered by UK Research and Innovation (UKRI).
The Turing AI Acceleration fellows and projects in health and care include:
Professor Christopher Yau, The University of Manchester: clinAIcan – Developing Clinical Applications of Artificial Intelligence for Cancer
Professor Yau aims to develop AI-driven predictive models that will allow us to describe how cancers evolve at the molecular level.
He aims to exploit the fact that cancers, whilst never exactly identical, often share similar development trajectories which we can learn about by collating information from across deep high-resolution molecular profiles of many cancers.
By embedding our extensive biological knowledge of cancer within AI models, he will develop systems that will produce predictions that are more realistic, interpretable and better explain the progression of cancers.
Professor Damien Coyle, University of Ulster: AI for Intelligent Neurotechnology and Human-Machine Symbiosis
Professor Coyle aims to develop AI technology that will 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.
Enabling movement-independent communication through this brain-computer interface could help those who are unable to communicate following a serious injury or illness.
Professor Coyle is leading a national trial in partnership with 17 hospitals to evaluate AI-enabled neurotechnology for consciousness assessment in prolonged disorders of consciousness following severe brain injury.
Dr Jeff Dalton, University of Glasgow: Neural Conversational Information Seeking Assistant
Dr Dalton aims to improve the capability and performance of voice-based personal assistants, similar to Alexa and Siri.
The research will use deep-learning methods for machine reading of text and learning from user interaction to enable agents that are developed more quickly and easily without specialised experts.
Professor Aldo Faisal, Imperial College: Reinforcement Learning for Healthcare
Professor Faisal aims to develop a fundamentally different approach to decision-support systems, though the use of reinforcement learning.
This can learn and distil the best plan of action to treat a patient, by harnessing existing hospital data and the expert knowledge of clinicians.
The AI system will learn to recommend optimised medical interventions such as prescribing drugs and changing dosages as needed by a patient, and also learn to make recommendations and their causes in a manner that is meaningful to decision-makers.
Dr Antonio Hurtado, University of Strathclyde: PHOTONics for ultrafast Artificial Intelligence (PHOTON-AI)
Dr Hurtado aims to develop AI systems inspired by the powerful capabilities of networks of neurons in the brain and utilising photonic devices that create, manipulate or detect light.
These light-enabled AI systems will be able to operate at very high speeds while retaining low energy consumption. Their potential could be to process images at very fast rates for medical diagnostics.
Dr Raul Santos-Rodriguez, University of Bristol: Interactive Annotations in AI
Dr Santos-Rodriguez’s fellowship will focus on human-centric machine learning. He will explore new extended forms of supervision and interaction between AI systems and humans to build trust and accountability while making the learning process more efficient.
In particular, focusing on developing methods for humans to provide informative and actionable feedback in order to shape the behaviour of AI systems, allowing humans in return to fully understand and measure the effect of their contribution.
The fellowship will cater for data and AI practitioners, domain experts and end-users across multiple fields, including health, IT, engineering and social media.
Dr Sebastian Stein, University of Southampton: Citizen-Centric AI Systems
Dr Sebastian Stein aims to develop trusted AI systems which put citizens at their heart and involve them in decision-making, rather than viewing them as passive providers of data.
These citizen-centric AI systems could be used in a wide range of applications, from using crowdsourced information to track the spread of infectious diseases and issue personalised guidance, to helping people manage their energy and transportation needs in a more sustainable manner.
Dr Adrian Weller, University of Cambridge – Trustworthy Machine Learning
Machine Learning presents tremendous opportunities for society but also introduces risks, such as embedding unfair biases or creating new vulnerabilities.
Dr Weller will explore fairness, interpretability and robustness, to ensure these systems are trustworthy.
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