Matt Bornstein: recommendation

Transcript context

…Yeah, it, it, it really is. I I, and maybe we can go to a little bit to, to Matt again. One thing I wanted to get your thoughts on that, you know, I don't know how long you have with, with us, but is the impact on the startup ecosystem, right? Like how, how big of an enabler is this? Or does this, I guess just commoditize everything to a point where, you know, everyone's just rappers. I think it's a really, really massive deal. You know, we've met with. Conservatively hundreds of AI startups now maybe, maybe thousands. We'd have to go back and look and, and, and I sort of alluded to this before, but the really big dilemma is do I train my own model or do I just use something off the shelf? And we're really, we're increasingly seeing that the answer for almost everybody is kind of a hybrid approach. We're seeing increasing number of startups, basically triage. Their AI workloads where if things require, you know, really high levels of accuracy and you know, human like text generation, GBT four is the only answer. But many queries or workloads actually don't require that, right? So you can kind of scale down and say, you know, for a really simple query, I can use, you know, an open source model off the shelf for something in the middle. I can fine tune for various tasks and then you can get pretty sophisticated about what you route, where all of that is only possible if we have commercially usable, really high quality language models and especially ones that have been efficiently trained such that latency is, is, is low and cost is relatively low. So I think what we're gonna see happen is there's gonna be a, a big push for startups to use. Lama two models and, and other open source models that have similar levels of performance. Fine tune it in ways that actually work for specific tasks, right? Not for specific data, like I think that was sort of a head fake, but for, for specific tasks and, and really be able to build more defensible businesses that way. You know, this, there's nothing wrong with using OpenAI. That's fantastic, but it's probably not good to make that a hundred percent of your business. And, and a lot of founders are doing that now. So, so that's why I think this is, this is such a huge deal and, you know, the, the progress just today has been amazing. Like, there's gonna be, by the end of today a number of hosts where you can just easily use The Lama two models, like right outta the box, you know, replicates one that we work with, but there there are others as well. You know, you can already run it on your local computer with two bit precision, which is kind of crazy if you stop and think about that for a second, that with two bits you can actually run a super advanced language model on your own computer. So I, I think I, I just think this is a huge, huge deal for startups and I think if you're a startup founder working in ai, you know, you, you really should be taking a look at, at open source models now and seeing how they, how they can be used to, to kind of deepen your moat and, and, you know, build a really great AI product. Right. So me, I would like to help fill in the blanks. So apart from Replicate, it looks like hugging Face has also launched an inference endpoint for that. And as far as I know, it's one of the only few ways to try the 70 B model off the shelf. I think Base 10 has also maybe put something up. And then for the, for the two bit quantized model, you can look at the G GML ecosystem. Yeah. And, and then I also wanted to recognize one of the other respondents in our chat, we have a little, little comment window here. ARD Doshi was responding, I think, to Simon. And, and I, I did actually have a pushback, right? Like, we don't have to know. The full data sets of of Lama as long as we are able to eval for everything that we want to know about. I think we actually have to live with AI becoming more and more of a black box. Even though the, the mo the the weights are open I mean for me it…

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