Model launches now include policy and authorization work

Transcript

From the transcript Latent Space publishes with the episode.

1. 19:10

   So, like, a while ago, you know, when Fable first came out, we had Glasswing and- Mm... there was a whole delay and change in how models started to get released. We saw this for most frontier models. Uh, there's safety testing. What's the status of things now? How do you guys see it? How do you determine... Like, one thing is there's a new sonnet and, um, haiku also coming. Yeah. I assume those will just come out.

2. 19:33

   But for something like Opus 5.5 or a new Fable, what's the, what's the current status? Yeah. So I would say this starts with what our kind of policy decisions need to be on safeguards and on safety. Um, and that's both offline safety and online safety. And so there's been a lot talked about lately around data retention and what we're doing there in order to w- you know, release these models safely, and the reason for that is because you have, you know, situations where there are many, many accounts across, like, a whole giant network of people who are choosing to spin up accounts and use the models in various ways, and there's no way that in an online way as tokens are flowing through the system, we would be able to kind of catch that there's something malicious happening with the models or distillation or whatever might be happening.

3. 20:19

   And so we've started to have to have data retention policies so that we could do automated review and human review, whatever it needs to be, to make sure that good things are happening with the models. And with each model that we're releasing, like, that's one example of a policy decisio- decision that needs to happen on, like, what will the policy be for this specific model? Um, and so Opus 5.5 that launched yesterday, for example, does not have those data retention requirements that Fable has, right?

4. 20:46

   But that's a decision that we have to make each time we're launching one of these models, and if it is going to have those policies, we're gonna have to do the work to make sure that those things are enforced. Similarly, with programs we have, like we have cyber verification programs and now newly our life science verification program which says, you know, like, you have a difference in how the online safeguards are working for the model that you're using, making sure all those programs are in place is another one.

5. 21:11

   And so just kind of a flavor of the type of work that we've done as part of each model launch that these are... Everything that I'm saying right now are not things [laughs] that we used to have to think about when we were launching these models- [laughs] Indeed... um, and now it's, it's the majority of the work is, like, what are we doing to prevent distillation, the online safeguards, the offline safeguards, the policies.

6. 21:31

   A really great example is we built a system to make sure that authorization for being allowed to use a model or being allowed to use one of these, like, programs that give you maybe, like, relaxed safeguards, that authorization threads all the way down through our system in a way that we can really trust. You know, it's not like, "Oh, you got access to Mythos," and so we throw you an API key and we flip a feature flag and call it a day.

7. 21:53

   We have a really robust system on how we're doing auth and making sure that if you're supposed to be using that model, like, only you are using that model in the way that you're meant to be using it.