Not in the AI space at all, but this was an interesting glimpse. I'd like to learn more about the dimensionality of the embedding and how that is minimised - i

Not in the AI space at all, but this was an interesting glimpse. I'd like to learn more about the dimensionality of the embedding and how that is minimised - it seems intuitive (not the same thing as "actually correct"!) that keeping the vector length smaller would be more computationally efficient, with a trade-off in (theoretically) worse performance. I'd guess that performance drop assumes a totally "even" distribution of meaning throughout the embedding space, so I wonder if the enhancements revolve around targeting the embedding more towards real data distributions, or something more subtle.