Nathan Labenz: uncertainty

Transcript context

…Yeah, we, we evolved working in training and you know, we have a funky inference endpoint as well. I think it's, it's good to do research in these areas. And some of our research led to a blog about 0 centre norm which now the Quinn model uses and some of our research was been used by the Trinity RC models on our solution to the curse of depth problem. But, you know, when people want to train models, it often is because they want to train to incorporate the latest data. And so, you know, being a search person, I was like, there's this way to get the latest data that is, is going to stay up to date, you know, live up to date and, and not be as expensive as running GPUs continuously to create a new model. Because even if they were creating models all the time, by the time you train them and then finish them and release them, they're already out of date. So I think concentrating more on search was direct learnings with customers on on this release cycle. Yeah, that's really interesting. Do you think that ultimately we see both? I mean I've had this idea for a long time and it doesn't seem to be really happening. In fact data bricks you know acquired Mosaic and then kind of killed this offering in the market as far as I know. But I've I've had this idea that if you're GE or three M that you could imagine having a model that was trained on all of your historical in house proprietary data, which is vast, right? And you would love it if your model knew kind of on an intuitive world model basis, as much about your company and what it does and all its history as they obviously do about the broader world. Do you think that you can get there with pure search or is there still something to be said for kind of continued pre training or mid training, whatever you want to call it that would try to bake in a sort of corporate world model that presumably would complement a search, but I I don't know if it's necessary. It sounds like you maybe think it isn't. It's interesting, I think a lot of companies feel that they have a vast amount of data, but when you compare it to the size of of the web, which is what these frontier models are trained on, they're trained on the web plus, let's say all the books in the world, etcetera. Then the extra corporate data is, is small. So how do you incorporate it and weigh it correctly? If you do just the corporate data, you won't know anything about calculus, let's say, you know, so that would be a problem for some, for some companies. So, so then people imagine adding, you know, the the web plus their data that gets very expensive. You can see, you know, the deep seq models, you know, say it was 5 million to train, but actually they very much admit that maybe it was another 5 million to finish. And these are by extreme experts, which enterprises, you know, don't, don't have. And so I think that, yeah, our thought was that, you know, search is a good bridge between all of the corporate information and a model because models are good enough to know how to incorporate new information that's relevant to to the actual query being asked and can be able to fetch more information to create, you know, that answer or that research report. But if you think about finishing a model with corporate data, there's another phenomenon called catastrophic forgetting, that as you add information at the end, after a model's trained and released, then if you add too much new information, then it kind of forgets some of the things that it really needed to remember. So I think, you know, there's a number of smart people working on that problem. And don't worry, you won't be able to miss it if people, please, people, do solve that problem. You'll read about it everywhere.…

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