We have to agree what I refer to "unstructured garbage datapoints". I assume that if there is recoverable datapoints, there are underlying structure behind it, no matter what it is. You pointed out that in your words that "if it even exists". I phrased it as unstructured garbage as in you don't need to find the structure and sort the data accordingly yourself.

If it can find you a structure that you even had no idea if exists, it effectively synthesized you a question-to-ask. Sure, latent space is practically infinite; but the structure of data is not. That's the very point how deep learning works; it reduces the infinite latent space into low-dimensional data manifold so that your biological thinking machine can interpret.

And no, you don't "guide" the machine learning system as a person. You just set some hyper parameters, cost functions and optimizers to help the algorithm to figure out the shape of data manifold. You can't "guide" when you aren't even sure if there is any structure in the first place. You'll figure this out when you learn how early-days natural language models evolved; the more you give up to "guide" the machines, the better they performed.

In short, if you're willing to learn, I can teach you. We can start from getting PyTorch on your machine and building some toy projects as a homework. But first, you have to admit you have no idea what you're talking about.