91Ö±²¥

Applications for the 91Ö±²¥ Prize are now open, with a deadline 12:00 GMT on 1 February 2024. To help to ensure coherence, avoid duplication and maximise the strength of UoM applications, Digital Futures are operating a light touch process of internal review and support, with senior input via a pan-University panel.

If you propose to lead on a application or if you propose to play a substantive role in one . If you have any questions please email digitalfutures@manchester.ac.uk. (If you are not lead please complete as much detail as possible; we recognise that you may not have all the required information at this stage.)

The first 91Ö±²¥ Prize will be awarded to the most innovative and impactful AI solution which demonstrates social benefit by overcoming challenges in the fields of energy, environment and infrastructure.

Solutions could include:

• Reducing energy costs for consumers by using AI to model household energy use and identify targeted interventions, such as retrofitting and replacement.
• Supporting emergency service response by bringing together a range of spatial data about the road and built environment to improve last mile routing.
• Improving the response to extreme weather conditions by using AI and earth observation data to predict areas vulnerable to flooding, or to support better real-time spatial data of events such as wildfires and flash floods.
• Reducing disruption to public services through predictive modelling of infrastructure resilience, with automated scheduling of maintenance, such as deploying teams to fix potholes or other traffic obstructions.
• Enhancing food security by using earth observation and soil data to monitor and improve farming productivity and crop yield.
• Improving efficiency and reducing resource consumption in manufacturing by using AI to optimise or automate energy-intensive processes.

These are examples of how 91Ö±²¥ Prize think you could address the overarching statement (but you’re welcome to think of your own).

91Ö±²¥ Prize encourage solutions that demonstrate advances in technical capabilities such as generalisation, uncertainty quantification, interpretability, data-efficient AI and physics-based AI – but other approaches are welcome too.