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billion dollar okay i think it's going to be a three comma settlement i honestly do or judgment
billion dollar okay i think it's going to be a three comma settlement i honestly do or judgment
i think it was best stated by zuck who said you know in the meta earnings i'd rather invest more and then not be late later on and i think the feeling that they're getting is the technology and it's not one of those things where people say it's a bubble like the technology is there
if you look at something like perplexity who we're going to talk about today you know they have 50 million active users already that's scale now and they can run that and i think that's pretty impressive from their standpoint and i think fundamentally aligns to what we've seen with cost reductions happening in the um inference space so it's uh basically happening already thanks to the inference cost thanks to open source models
these aren't guard rails because you can't create these nuanced rules in your guard rails that's like oh if someone says something about new york times do this and if they said about the new york post do that you're you would have guard rails that would have rules that would be just like like there'd be too many rules in it right and so this this goes back to like what i said it's either in its training data or additional fine tunes they've done on top of the model or definitely in the reinforce
here comes the one i think is the big one in the case of gemina guardrails and so guardrails is what stops you from basically let's use like a very extreme example here telling you how to make a ball because in the training data because these things are trained on the open internet we've talked about this common crawl right and maybe even in the reinforcement learning it never got told to not answer those questions so what you do is you put guardrails around it to say either before the question
kudos to twitter to not letting those things uh or you know x not letting those things circulate
twitter is the center x is the center of the universe for where people are publishing the know-how as well
actually meta has released something i'll just pull it up just to to talk about it but called llama guard and uh um and so my guard yeah and so it is their open source language model llama yeah and um this is uh just gonna pull it up here and we'll do a demo next week because i don't have one queued up for this but it's really powerful and excellent and what it is um just released in december and basically again you can get the paper and you can go download the model um but what it does is it ba
it's really incredible. They can do this for any type of, you know, workflow or brainstorming or product review or anything you need.
Microsoft has tied their revenue and future products to open AI. Yes. That is what they've pitched wall street and it's what they've pitched consumers and developers and consumers, consumers, enterprise, and developers are their three-legged stool. Yes. They went all in with developers for the open AI, uh, platform consumers for being, and then enterprise for co-pilots on office. Yeah.
just level setting both anthropic and open AI, you know, uh, I'd say kind of leading models in the space, definitely closed.
and then meta and then Databricks. Uh, and there's another one we'll bring up mistral as well today. Uh, the, these, these folks are fully open, uh, commercial use case is different. The commercial use, uh, differs based on their licenses, but in general, they're sharing everything and we're seeing really fast innovation. So that's the level set.
Google started this whole thing, obviously the, you know, the reason others can get there is the papers are open source and people have written about them, but we haven't seen an open model from Google in, in a while. Although, you know, they do support open models in their, uh, vertex platform and then meta and then Databricks.
And I think it, you know, does justify that, you know, Microsoft is still supporting, you know, open source, which, which is important here.
Well, look, I'm going to lean into a company that you are famously known for angel investing in Uber. Oh, yeah, I was in that one, I think. Yeah. And one could argue back then, someone would say the location API on an iPhone through iOS. Oh, you're just building a wrapper around that. Yes. Right. But that was a very important API. That API has led us to have Uber, Instacart, DoorDash, you know, a whole bunch of, you know, services with location kind of being at the core of it. But then they had
So I think, uh, what, and, um, you know, Nick is sharing the same thing here. I think it's trained on samples from YouTube, which is a very fascinating thing because they know what's copyrighted in YouTube. Obviously they do a ton of work around that. And then they have a bunch of stuff that is just done organically. So my understanding is that they've taken clips from YouTube to got it here.
And so, you know, it's, it's okay. So this is, you know, it's, it's kind of interesting, feels like a bit, um, it, you know, early music generation.
i get confused with when we see about regulation because hugging face been around a very long time and these are all like machine learning models i mean where are you going to cut this off there's so many here that are serving different purposes
amazon partnered with them so that's who aws's partner has been up front in terms of giving users the selection they need for their you know particular use cases
hugging face really was leader in that and it continues to be where they don't have a single model they have lots of different models that have been uploaded by different users
in the case of, you know, USDC and circle, they publish sort of their holdings and they make it very clear.
I kind of think like this could be a real huge opening for NFTs outside the 30%.