Overview
What this challenge is about.
You receive 30 days of logs covering 240 near-miss events (close approach to a human, low-battery emergency, network loss). For each event, classify whether the safety stop triggered correctly, was missed, or was a false positive. Map findings to a clear safety taxonomy. Identify the top three failure modes by frequency-times-severity. Write a 4-page safety memo with a remediation plan and a recommendation on whether the Hong Kong pilot should proceed.
The Brief
What you'll do, and what you'll demonstrate.
Audit the cafe robot's safety stops on real near-miss logs and produce a go/no-go safety memo for a Hong Kong landlord.
Earning criteria — what you'll demonstrate
- Conduct a structured safety audit from operational logs
- Build and apply a failure-mode taxonomy
- Rank safety risks by frequency and severity, not gut feel
- Communicate safety findings to non-engineering risk committees
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
AI Safety Researcher
Conducting a structured safety audit from operational logs is the entry-point work for AI safety researchers in deployed-robotics teams.
This challenge sharpens
- safety-analysis
- failure-mode-analysis
- risk-assessment
AI Product Manager
Owning the safety story for a market expansion is increasingly part of the AI PM's job at deployed-robotics companies.
This challenge sharpens
- risk-assessment
- incident-review
- safety-analysis
Applied AI Scientist
Log-based failure-mode analysis with a defensible taxonomy is the applied-AI scientist's tool for prioritizing engineering work.
This challenge sharpens
- log-analysis
- failure-mode-analysis
- incident-review