Talking AI — Computer vision: brilliant on what it knows, fragile on everything else

Computer vision isn't a simple surveillance camera: it's a model trained to recognize a defect — a scratch, a misassembled part, a missing label — from thousands of example images, then to decide on its own between "pass" and "reject." Its strength and its weakness are the same thing: it is formidable on what it has already seen and loses its footing the moment it meets something new. An April 2026 study measured it: on a never-seen product, accuracy falls to 65% until the model is retrained. The right question to ask a vendor isn't "what is your detection rate?" but "what does your system do when it meets a part, a product, or a lighting condition it has never seen?" The takeaway: before signing, demand a test on real cases absent from the training data, under real operating conditions, and dismiss any figure obtained in the lab.

Source: arXiv, "Accelerating New Product Introduction for Visual Quality Inspection via Few-Shot Diffusion-Based Defect Synthesis," April 2026 (zero-shot accuracy of 65.0% on a never-seen product, rising to 85.1% after example generation).