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Details of Grant 

EPSRC Reference: EP/Y028392/1
Title: AI for Collective Intelligence (AI4CI)
Principal Investigator: Bullock, Professor S
Other Investigators:
Dennett, Professor AR Danon, Dr L Ajmeri, Dr N
Heppenstall, Professor AJ Cartlidge, Dr J Smith, Dr T
Williams, Professor HTP Chen, Professor J Smith, Dr LGE
O'Kane, Dr AA Morgan, Professor P
Researcher Co-Investigators:
Dr C Chen
Project Partners:
Admiral Group Plc Advice Robo Arup Group Ltd
Cabinet Office Cardiff Capital Region Climate Action Against Disinformation
CRAFT Deep Blue Srl DeepSearch Labs
Dept Levelling Up, Housing & Communities Digital Poverty Alliance FinTech North
FinTech West Glasgow City Council Government of Scotland
Mayden Met Office Methods Analytics Ltd
Natural Resources Wales NayaOne Nesta
Office for National Statistics Plymouth Marine Laboratory Propellant
Roche (UK) Royal Statistical Society Royal Town Planning Institute
SIMBA Chain (UK) Southern Health NHS Foundation Trust Stratiphy
Taunton & Somerset NHS Foundation Trust The Flowminder Foundation Timecentres UK Ltd
UK Health Security Agency Uni Hospital Southampton NHS Fdn Trust University of Cambridge
Westminster City Council WhiteCap Consulting Ltd Wikimedia UK
Department: Computer Science
Organisation: University of Bristol
Scheme: Standard Research
Starts: 01 February 2024 Ends: 31 January 2029 Value (£): 10,274,279
EPSRC Research Topic Classifications:
Artificial Intelligence Environment
Human-Computer Interactions Mathematical Aspects of OR
EPSRC Industrial Sector Classifications:
Environment Healthcare
Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
08 Nov 2023 AI for Science or Real Data Interview Panel A Announced
Summary on Grant Application Form

AI and Machine Learning often address challenges that are relatively monolithic in nature: determine the safest route for an autonomous car; translate a document from English to French; analyse a medical image to detect a cancer; answer questions about a difficult topic. These kinds of challenge are very important and worthwhile targets for AI research.

However, an alternative set of challenges exist that are more *collective* in nature and that unfold in *real time*:

- help minimise the impact of a pandemic sweeping through a population of people by informing the coordination of local and national testing, social distancing and vaccination interventions;

- predict and then monitor the extent and severity of an extreme weather event using multiple real-time physical and social data streams;

- anticipate and prevent a stock market crash caused by the interactions between many automated trading agents each following its own trading algorithm;

- derive city-wide patterns of changing mobility from high-frequency time series data and use these patterns to drive city planning decisions that maximise liveability and sustainability in the future city;

- assist populations of people with type 2 diabetes to avoid acute episodes and hospitalisation by identifying patterns in their pooled disease trajectories while preserving their privacy and anonymity.

Developing AI systems for these types of problem presents unique challenges: extracting reliable and informative patterns from multiple overlapping and interacting data streams; identifying and controlling for inherent biases within the data; determining the local interventions that can allow smart agents to influence collective systems in a positive way; developing privacy preserving machine learning and advancing ethical best practices for collective AI; embedding novel machine learning and AI in portals, devices and tools that can be used transparently and successfully by different types of user.

The AI for Collective Intelligence (AI4CI) Hub will address these challenges for AI in the context of critically important real world use cases (cities, pandemics, health care, environment and finance) working with key stakeholder partners from each sector.

In addition to significantly advancing applied AI research for collective intelligence, the AI4CI Hub will also work to build *community* in this research area, linking together academic research groups across the UK with each other and with key industry, government and public sector organisations, and to build *capability* by developing and releasing open access training materials, tools, demonstrator systems and best practice guidance, and by supporting the career development of early and mid-career researchers both within academia and beyond.

The AI for Collective Intelligence Hub will be a centre of gravity for a nation-wide research effort applying new AI to collective systems.

Key Findings
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Potential use in non-academic contexts
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Date Materialised
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Organisation Website: http://www.bris.ac.uk