> But what's the point? Autoencoders don't really have much practical use now, but that's not the point of them. The point is we really want to figure out how

> But what's the point? Autoencoders don't really have much practical use now, but that's not the point of them. The point is we really want to figure out how to do unsupervised learning well and autoencoders are one of the few ways of doing it. We want to do unsupervised learning well because most learning that humans do is unsupervised. The idea behind autoencoding is that by forcing the network to try to learn efficient ways to compress the data, it could learn important features of the data. The fact that the pictures are so blurry means that this doesn't work very well, but that's why it's a research problem. Autoencoders don't work well, but some unsupervised techniques that extend on them do, and we get impressive results like https://arxiv.org/pdf/1511.06434.pdf https://arxiv.org/pdf/1511.06434.pdf (see page 5) where the network learns to generate natural-looking bedroom images.