How to win the Manchester Prize

The Manchester Prize is a challenge prize.

Challenge prizes are a unique approach to funding innovation, offering a series of incentives, with a final prize given to whoever can first or most effectively meet a defined goal. They are public, open competitions which lower barriers to entry to attract the broadest possible community of innovators.

They differ from other types of prizes such as the Nobel or Pulitzer, in that they incentivise innovators to work towards a specific challenge, rather than being a reward for past achievements.

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Please note: The entry period for this round of the Manchester Prize closed on 1st February 2024. Information on this webpage is provided for historical reference for existing entrants.

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The challenge statement: what you need to do to win the the Manchester Prize

The first Manchester 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 we think you could address the overarching statement (but we’re hoping to be surprised – you’re welcome to think of your own).

We 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.