Having a passing knowledge of how ML works, I always wondered how these algorithms can use such dense data structures -- it makes a lot more sense now that I know they can be compressed and still searchable.
High-dimensional spaces are common in deep learning nowadays, and there's lots of room in high-dimensional spaces so any potential quantization errors are probably insulated from incorrect recall by virtue of the fact that things are so far apart in the original space.