As the title states, why are latent spaces even able to intelligently learn representations? There's no guarantee that we learn the most important features since it's all done automatically in something like an autoencoder. In comparison, with other dimensionality reduction algorithms, e.g., PCA, have ways of logically choosing what dimensions to keep.
Is there some proof for why latent spaces are able to learn intelligent representations?