Microsoft's Aurora AI Now Predicts Air Quality with High Speed and Accuracy
Microsoft has revealed a pioneering solution from Aurora AI, the cutting edge AI system that can predict air quality extremely quickly and with unprecedented accuracy. This is a large step forward in environmental monitoring technology and could lead to better health outcomes and more fact based decisions for communities around the globe.
Highlights
- Aurora AI predicts air quality changes faster than existing systems.
- It uses real-time data from satellites, ground sensors, and weather forecasts.
- The system boasts over 90% accuracy in short-term air quality forecasts.
- Microsoft aims to support cities and health agencies with Aurora’s insights.
- It is already being piloted in highly polluted urban regions globally.
The success of Aurora AI comes from how it is able to ingest a variety of different data sources such as satellite imagery, local sensors and meteorological data. It allows the system to process real‑time vast environmental datasets in real time and interpret them. It has rapid forecasting ability thereby enabling the governments and health agencies to issue early warnings so that people can take protective measures from polluted air on time.
The machine learning backbone of Aurora AI is fine tuned on fluid environmental patterns. Conventional systems are different because it continually learns and improves upon predictions, it is a dynamic tool and not a static model. This is just a small slice of Microsoft’s investment in sustainable technologies, all of which leverage its public commitment to climate resilience and public health.
Aurora AI is not limited to applications in city air monitoring. In agriculture, for example, Microsoft envisions using its HoloLens to find plot coordinates faster, plot irrigation schedules and make weather predictions and dispatch and organize help for emergencies. Aurora could predict pollution spikes or drops and help industries cut environmental impact by appropriately adjusting operations. The future of air quality prediction looks smarter, faster and more actionable than ever as more cities come on board.