Kyle Kranen: observation

Transcript context

…yeah, he did Rexi as well. Yeah, Rexi. Yeah. I mean that, that was the taboo data at the time, right? You have tables of like, audience qualities and item qualities, and you’re trying to figure out like which member of the audience matches which item or, or more practically which item matches which member of the audience. And at the time, really it was like we were trying to enable. Uh, recommender, which had historically been like a little bit of a CP based workflow into something that like, ran really well in GPUs. And it’s since been done. Like there are a bunch of libraries for Axis that run on GPUs. Uh, the common models like Deeplearning recommendation model, which came outta meta and the wide and deep model, which was used or was released by Google were very accelerated by GPUs using, you know, the fast HBM on the chips, especially to do, you know, vector lookups. But it was very interesting at the time and super, super relevant because like we were starting to get like. This explosion of feeds and things that required rec recommenders to just actively be on all the time. And sort of transitioned that a little bit towards graph neural networks when I discovered them because I was like, okay, you can actually use graphical neural networks to represent like, relationships between people, items, concepts, and that, that interested me. So I jumped into that at Nvidia and, and got really involved for like two-ish years. Yeah. Uh, and something I learned from Brian Zaro Yeah. Is that you can just kind of choose your own path in Nvidia.…

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