A seismologist is testing AI models for earthquake prediction. Model A correctly identifies 95% of earthquakes in test data containing 200 events. How many false negatives does it produce?

Fri, 17 Apr 2026 03:01:23 +0000

**How AI Still Struggles to Predict Earthquakes—Even with 95% Accuracy** Every few months, a breakthrough in earthquake prediction captures public attention, headlines spreading quickly across digital platforms. Recently, a seismologist tested an advanced AI model designed to detect seismic activity. The model demonstrated impressive performance: it identified 95% of the 200 test events as earthquakes. For those tracking the intersection of geology and technology, this number sparks curiosity—but raises a crucial question: What happens when the model misses a pattern? Understanding false negatives offers clearer insight into this evolving challenge. **Why This AI Score Matters in the US Research Landscape** As earthquake risks remain a significant concern across earthquake-prone regions in the U.S., particularly along the West Coast, advances in predictive technology carry real relevance. The test’s 95% accuracy rate reflects meaningful progress—but also reveals limitations that shape public trust and scientific credibility. With mobile users increasingly seeking reliable information on risk prevention, translating technical metrics into accessible context becomes essential. Why does a model miss 5% of actual quakes? And how does that number influence preparedness strategies nationwide?