What does "ground truth" mean in the context of AI especially in the context of machine learning?
I am a little confused because I have read that the ground truth is the same as a label in supervised learning. And I think that's not quite right. I thought that ground truth refers to a model (or maybe the nature) of a problem. I always considered it as something philosophical (and that's what also the vocabulary 'ground truth' implies), because in ML we often don't build a describing model of the problem (like in classical mechanics) but rather some sort of a simulator that behaves like it is a describing model. That's what we/I call sometimes black box.
What is the correct understanding?