Josep M. Pujol: evaluation

Transcript context

…I didn't know it specifically. 2019. Right? So a long time ago. And and basically, this relationship of being able to go from Lady Gaga's age to how old is Lady Gaga, It was done via embeddings, 200 dimensions, 2 bytes, vector search. But back at that time, it was not called vector search. Was called like nearest neighbors approximations. And that's what actually what we built. So we actually took advantage of these recent technologies. Again, there was like no only semantic embeddings. Actually, there was also like based on queries. Remember, like, we were basically very heavily based on queries. Just to give you a number, a nice number, our query similarity system host like, has more than 9,000,000,000 unique queries. Not so, like, the quiddies, Hotmail is only 1, so 9,000,000,000. So whenever you actually get a new quiddies, we're able to return a set of like similar quiddies that we have seen in the past out of this 9,000,000,000 in 20 milliseconds. Right? And that is done both using embeddings, but also using angrioms based on queries. Right? Because we need both since you cannot be semantic 100. Right? And that's that was true back then and it's true now. Why not? What's the limitation there? Or where does the purely semantic approach fall short?…

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