01:38 — Litter is too often considered “out of sight, out of mind.” San Diego native Nathaniel Felleke created a homemade, car-mounted machine learning tool to accurately detect trash and overlay the data onto a Google map.
02:37 — The resulting data effectively acts as a heat map of the geographical areas with the most litter in the city. Mounting this tech on city vehicles (cleanup crews, repair vehicles, and so on) could help prioritize their work moving forward.
03:40 — Putting the tool into practical use throughout the city via a startup or partnership could promote Nathaniel’s model into the latest practical use case for AI’s widespread capabilities.
Looking for real-world insights into hyperautomation? Subscribe to the Hyperautomation channel: