This is the best summary I could come up with:

During the pilot program, the system has already detected 77 wildfires before dispatch centers received 911 calls—about a 40 percent success rate, according to the NYT.

Traditionally, Cal Fire detects emerging wildfires by relying on the same network of over 1,000 mountaintop cameras monitored by humans, who look out for signs of smoke.

It can only detect fires that are visible to its network of cameras, and human intervention is still required to confirm the AI model’s alerts.

Engineers from DigitalPath, the California-based company responsible for creating the software, have been manually vetting each fire the AI identifies.

The process has been challenging, with many false positives from fog, haze, dust kicked up from tractors, and steam from geothermal plants, according to Ethan Higgins, a chief architect of the software.

The AI system processes “billions of megapixels” of images every minute from cameras that cover approximately 90 percent of California’s fire-prone areas.

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