[cdumler]

I would argue reasonable to believe it will be notably better until it is catastrophically wrong. The issue with neural nets is that they can be really well trained within the bounds of training set; however, when faced with novel, there is no underlying abstraction inference on which to revert. Look instances where autonomous cars are disable or have weird responses when people modify signs[1]. The driving will be better, but the when the information sits squarely outside of the norm (art on the road, abstract images that defy normal depth cues, etc), the car will do very unexpected things.

As humans, we have this issue already. We call them "optical illusions." Optical illusions are really brain neural net failures where there are competing narratives between evaluations of the information provided by our senses we conclude invalid results. We don't have the ability to process absolute light values, so we infer colorspace by relative color within our 3D mental model, which can result in inferring a difference in color where there is none.

1. https://spectrum.ieee.org/slight-street-sign-modifications-c... 2. https://en.wikipedia.org/wiki/Checker_shadow_illusion