Most AI systems today solve single, well-defined tasks—like translating a document or reading a medical scan—but they struggle with messy, real-time problems that involve many interacting people, data streams, and decisions. This hub will build AI that can handle those collective challenges: coordinating pandemic responses across a population, predicting flash crashes from thousands of automated traders, or guiding city planning from live mobility data. The problem is that current AI lacks the tools to extract reliable patterns from overlapping, biased data streams, to design local interventions that nudge entire systems in a positive direction, and to do all this while preserving privacy. The hub will develop those tools across five sectors—cities, pandemics, healthcare, environment, and finance—working directly with government, industry, and public-sector partners. If it succeeds, AI could help cities adapt in real time to changing traffic or weather, help health services spot diabetes crises before they happen, and make financial markets more stable. The hub will also train a generation of researchers and release open-source tools, so the advances spread beyond any single project.
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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.
Adam Dennett (Co-Investigator)Aisling O'Kane (Co-Investigator)Alison Heppenstall (Co-Investigator)Hywel Williams (Co-Investigator)Jing Chen (Co-Investigator)Joan Condell (Co-Investigator)John Peter Cartlidge (Co-Investigator)Laura Smith (Co-Investigator)Leon Danon (Co-Investigator)Michaela Black (Co-Investigator)Nirav Ajmeri (Co-Investigator)Phillip Morgan (Co-Investigator)Seth Bullock (Principal Investigator)Theresa Smith (Co-Investigator)
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