(2021-07-09) Brander Search Reveals Useful Dimensions In Latent Idea Space

Gordon Brander: Search reveals useful dimensions in latent idea space. When you train a deep learning system, it builds up a a latent space. A latent space is a hyperdimensional space where things that are similar along some dimension are near to each other along that dimension. Latent space is like a map you can use to correlate things with other things, along many dimensions.

What if we imagined ideas as a kind of hyper-dimensional latent space?

Imagine your notes as a stack of papers. You have a long piece of red string, with a needle at one end.

You’ve arranged your ideas along one dimension of latent idea space. Imagine doing this for all possible dimensions at once. (associative)

Search lets us explore latent idea space.

Any sufficiently advanced search is indistinguishable from a hyperlink.