and he shows that the gradient descent actually doesn't go that far away from the point where it interpolated random labels and it generalizes pretty bad much worse than it would if you just initialize it randomly so that really tells us something notable about how this optimization landscape looks like and we really need to understand how comes that the gradient-based algorithms initialize randomly are able to avoid the bad minimum and so the goal here you know it's it's pretty much clear these days that this