Karina Nguyen: recommendation

Transcript context

…I want to follow both those threads. So the first is on this idea of synthetic data. Is a simple way to understand it, that the models are generating the data that future models are trained on and you ask it to generate all these ways of doing stuff, all these tasks as you described, and then the newer models trained on this data that the previous model generated? Some tasks are synthetically curated. So this is an active research area is how can you synthetically construct new tasks with models to learn. Sometimes when you develop products, you get a lot of data from the product and user feedback and you can use that data too in this cross-training world. Sometimes you still want to use human data because actually some of the tasks can be really, really hard to teach. Experts only know certain knowledge about some chemicals or biological knowledge, so you actually need to tap into the experts' knowledge a lot. So yeah, I think to me synthetic data training is more for product... It's a rapid model iteration for similar product outcomes. And we can dive more into it, but the way we made Canvas and tasks and new product features for ChatGPT was mostly done by synthetic training. Let's actually get into that. That's really interesting. I want to talk about evals, but let's follow that thread. So talk about how this helped you create Canvas.…

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