Micah-Hill Smith: preference

Transcript context

…Um, so not right in every situation, but, um, I think it makes sense, you know, to test hallucination in scenarios where it makes sense. Also, the obvious question is, uh, this is one of. Many that there is there, every lab has a system card that shows some kind of hallucination number, and you’ve chosen to not, uh, endorse that and you’ve made your own. And I think that’s a, that’s a choice. Um, totally in some sense, the rest of artificial analysis is public benchmarks that other people can independently rerun. You provide it as a service here. You have to fight the, well, who are we to, to like do this? And your, your answer is that we have a lot of customers and, you know, but like, I guess, how do you converge the individual? I mean, I think, I think for hallucinations specifically, there are a bunch of different things that you might care about reasonably, and that you’d measure quite differently, like we’ve called this a amnesty and solutionation rate, not trying to declare the, like, it’s humanity’s last hallucination. You could, uh, you could have some interesting naming conventions and all this stuff. Um, the biggest picture answer to that. It’s something that I actually wanted to mention. Just as George was explaining, critical point as well is, so as we go forward, we are building evals internally. We’re partnering with academia and partnering with AI companies to build great evals. We have pretty strong views on, in various ways for different parts of the AI stack, where there are things that are not being measured well, or things that developers care about that should be measured more and better. And we intend to be doing that. We’re not obsessed necessarily with that. Everything we do, we have to do entirely within our own team. Critical point. As a cool example of where we were a launch partner for it, working with academia, we’ve got some partnerships coming up with a couple of leading companies. Those ones, obviously we have to be careful with on some of the independent stuff, but with the right disclosure, like we’re completely comfortable with that. A lot of the labs have released great data sets in the past that we’ve used to great success independently. And so it’s between all of those techniques, we’re going to be releasing more stuff in the future. Cool. Let’s cover the last couple. And then we’ll, I want to talk about your trends analysis stuff, you know? Totally.…

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