[_jayhack_]

Vector embedding is not an invention of the last decade. Featurization in ML goes back to the 60s - even deep learning-based featurization is decades old at a minimum. Like everything else in ML this became much more useful with data and compute scale

[senderista]

Yup, when I was at MSFT 20 years ago they were already productizing vector embedding of documents and queries (LSI).

[jongjong]

Interesting. Makes one think.

[senderista]

To be clear, LSA[1] is simply applied linear algebra, not ML. I'm sure learned embeddings outperform the simple SVD[2] used in LSA.

[1] https://en.wikipedia.org/wiki/Latent_semantic_analysis

[2] https://en.wikipedia.org/wiki/Singular_value_decomposition