Comment by ekidd

Vector embeddings are slightly interesting because they come pre-trained with large amounts of data.

But similar ways to reduce huge numbers of dimensions to a much smaller set of "interesting" dimensions have been known for a long time.

Examples include principal component analysis/single value decomposition, which was the first big breakthrough in face recognition (in the early 90s), and also used in latent semantic indexing, the Netflix prize, and a large pile of other things. And the underlying technique was invented in 1901.

Dimensionality reduction is cool, and vector embedding is definitely an interesting way to do it (at significant computational cost).