I chose this example because, in practice, sometimes changing a single feature does ruin the prediction, especially in computer vision. Often, the systems are s

I chose this example because, in practice, sometimes changing a single feature does ruin the prediction, especially in computer vision. Often, the systems are somewhat resilient to these kinds of errors, but often not also. The fact that a computer can label an object missing many features does not imply that it cannot also make a mistake doing so. Like the Tesla that couldn't recognize a truck right in front of it. Then there's Google's Deep Dream, which did silly things like think that all hammers had arms attached to them. Then there's also this: http://www.evolvingai.org/fooling http://www.evolvingai.org/fooling and many other examples like it. I chose a simple example that would be maximally relatable and still accurate even with respect to state of the art algorithms and datasets with billions of samples.