Can't be trusted => can't be fully trusted != is useless Also nothing is perfect. And even severely imperfect things can be reasonable good solution/improve a

Can't be trusted => can't be fully trusted != is useless Also nothing is perfect. And even severely imperfect things can be reasonable good solution/improve a situation. So don't base a curt ruling on it. Be very careful about using it when it can put lives at risk (e.g. self driving). EDIT:And make the people selling the system responsible for it's failure!! You can't expect a person you tell to not touch the wheel for hours to then be able to react asap if something goes wrong, that's not how humans work. Hence putting the responsibility on the human which happens to sit behind the wheel is completely unreasonable.END EDIT Anyway my main problem with DL is that its hard to evaluate how trust-able it is if applied to a very diverse real world situations (i.e. self driving). You can evaluate it on data sets but data sets are extremely unlikely to get anywhere close to representing a diverse reality, and while each edge case not represented is very rare due to the diversity of reality there is a endless number of them so sooner or later they will be hit. And as they are edge case not represented by your data or evaluation sets you can't relay reason well what will happen if they are it. Furthermore due to the nature of data collection it's not rare for independent data sets to unexpectedly share subtle biases. A good example is a Tesla car getting confused because in front of it was a truck which had traffic lights loaded on the back. A extremely rare/unusual situation, which happened anyway. So you have to layer different systems, preferable also some fallback emergency systems not dependent on DL.