Why AI Struggles to Crack the Code of Nondestructive Testing
There’s a common trope in science fiction: an all-seeing machine, unblinking and exact, scanning the world for flaws we can’t detect. In the real world of nondestructive testing (NDT)—the science of using sensors to find cracks, corrosion, and hidden damage in everything from aircraft wings to oil pipelines—that dream still feels frustratingly out of reach.
We have artificial intelligence that can beat world champions at Go, generate Hollywood-quality dialogue, and diagnose rare diseases from pixelated scans. And yet, when we point these same tools at ultrasonic signals or thermographic images from NDT inspections, the results are… inconsistent at best.