One problem with training a neural network end-to-end this way is that the system is susceptible to unpredictable glitches: The same principle that lets people trick a NN into [thinking a panda is a vulture](https://codewords.recurse.com/issues/five/why-do-neural-netw...) can happen randomly just by differing lighting/shadow conditions, sun glare, or who knows.
One can always train the network with more and more scenarios, but how do you know when to stop? How good is good enough in this regard?
One can always train the network with more and more scenarios, but how do you know when to stop? How good is good enough in this regard?