In the case of the UPS truck, both the computer system and the human were attempting to drive the car through a tight spot with very little wiggle room to the left or right. In most cases, the driver takes over by yanking the steering wheel in the opposite direction the software is trying to turn. That movement wasn't possible under these circumstances, however, leaving it unclear whether the car or the human was in control. The struggle for control was amplified by the lack of a sharp turn, preventing the driver from cranking the wheel to regain his steering input from the software.

Recent press has challenged the hype surrounding deep learning, trumpeting several findings which expose shortcomings of current algorithms. However, many of deep learning's reported flaws are universal, affecting nearly all machine learning algorithms.

A few well-publicized recent papers have tempered the hype surrounding deep learning. The papers identify both that images can be subtly altered to induce misclassification and that seemingly random garbage images can easily be generated which receive high confidence classifications. A wave of press has sensationalized the message. Several blog posts, a YouTube video, and others have amplified and occasionally distorted the results, professing the gullibility of deep networks. Given the hoopla, it's appropriate to examine these findings.