Nathan Labenz: evaluation

Transcript context

…No. It's actually faster than solid state. There is spinning disk. Impossible to do what we do. Flash, still not fast enough, but at least it's like a another magnitude slower than RAM. Gotcha. Interesting. So suddenly, you have, what, 100 terabytes, a petabyte of NVMe's. Now you can actually do what you want to do. Before, it was even the basically, like, the way of doing it would have to be done differently. That would actually, like, increase cost by an order of magnitude, increase everything by an order of magnitude. Right? And it's not doable. So again, that's the the thing like I think that's how Brave Search is was built with Brave. Incremental, always taking advantage of what the world had to offer in a way. Right? So that history of the hardware enablement is really interesting. Can we do the same, an equivalent history for the progression of language models or semantically enabling models? Because going back to like early days, you had literally just keywords and then n grams. Then now obviously we've got pretty amazing language models, I would imagine that like the best language models are maybe an overkill for some of the use cases, if only because of cost and latency. So what's that history like and where are you guys today in terms of the models that you use for semantic purposes? Yeah. Remember, like, the example I told you before about Lady Gaga, age versus whole world is Lady Gaga? That's a semantic metric. Right? We started to use embeddings, semantic embeddings based on on a model that was called the star space. I don't know if it's familiar sounds familiar.…

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