Software,
shipped honestly.
For teams using software, machine learning, data or hardware to solve a defined real-world problem. We grade the working prototype, the engineering decisions and the ethics — not the founder narrative.
Three deliverables.
Something a juror can run on a phone, a browser or a desk. Demos in slide-decks alone are not accepted at the showcase round. Source code is submitted in a private repository.
Five pages on architecture, dataset provenance, evaluation method, known failure modes and what the team would do next with more time. Teams that pretend to have solved harder problems than they have are marked down.
One page on who could be harmed by the tool if it shipped, how the team would mitigate that, and which decisions a real operator would still have to make. We borrow this requirement from research-ethics review boards.
Engineers, not pitch coaches.
The Digital & AI track is led by Dr Aaron Walsh out of Imperial College Enterprise Lab in London, by Newlab’s engineering bench in New York, and by Cyberport’s senior engineer-in-residence cohort in Hong Kong. Mentors are working practitioners — they read code, not slides.
Teams whose work touches on health, fairness or safety-critical systems are paired with an additional reviewer drawn from the academic council. Our reference points include the Lemelson-MIT InvenTeams programme and the James Dyson Award’s engineering-led judging.
Selected work, 2024–25.
A lightweight triage tool for after-hours under-resourced clinics. Open-sourced; piloted in two community clinics in Sha Tin.
A bus-network simulator for school districts evaluating route changes. Adopted as a planning aid by Lambeth’s schools transport office.
An OCR pipeline for low-resource Chinese palaeography. The team’s evaluation methodology was the standout part of the entry.