Google & Johns Hopkins University Can Adversarial Examples Improve Image Recognition?

A fundamental concept in Chinese philosophy and culture is Yin and Yang -- the belief that harmony is achieved when opposites coexist and share elements of the other. This can be interpreted to suggest that purpose and goodness can be found even in stuff like floodwaters, mosquitoes, and -- in the world of artificial intelligence -- adversarial examples. Adversarial examples are perturbations added to an image that are invisible to the human eye but can trick a computer vision system into misclassifying objects -- potentially causing for example an autonomous vehicle to drive through a stop sign. Adversarial examples are a bane to the researchers who build the neural networks that deliver much of today's advanced AI. Now, a team from Google and Johns Hopkins University says it has found a silver lining to adversarial examples.