The Achilles' Heel of AI Computer Vision

Would you ride in an autonomous vehicle if you knew that it was subject to visual problems? How about undergo cancer treatment based on a computer interpretation of radiological images such as an x-ray, ultrasound, CT, PET, or MRI scan knowing that computer vision could easily be fooled? Computer vision has a problem–it only takes slight changes in data input to fool machine learning algorithms into "seeing" things wrong. Recent advances in computer vision are largely due to the improved pattern-recognition capabilities through deep learning, a type of machine-based learning. Machine learning is a subset of artificial intelligence where a computer is able to learn concepts from processing input data either through supervised learning where the training data is labeled, or not as in unsupervised learning or a combination without explicit programming.