"The Cognitive Revolution"
01:03:26 · Nathan Labenz
[01:03:26] S0: After Sagar left, Prakash came back to the compute gap. 3,800 GPUs against the hundreds of thousands at the Frontier Labs. Yeah. I think that's gonna be a really hard deficit to make up for. I mean, we talked about this within the context of Google, I think, a few times. You know, when you own a quarter of the world's compute supply, you can have a a missed cycle, and you can, like, figure out a way to get back on the horse. And seems like this week, they've done that. Right? We barely even talked about carbon. I I mean, with Mistral, it does seem like the one big thing is that they do have, like, state backing. You know, they... I I I really do think if they miss a model cycle in a purely capitalist context, they're maybe done. You know? You... Like, if if you are just relying on return seeking investors to fund your growth and you miss at this point, that may be fatal. There's not a lot of sovereign AI plays out there that are at all credible, and literally, there might just be one. So in that context, I do think that gives them not... It doesn't translate directly to compute, but I do think it translates to at least, like, reasonable expectation that they will be around to fight another day because there's just so many interests that want to see that happen that I can't imagine, you know, even if everything were to totally flop, would the whole continent of Europe just be like, well, I guess we have zero now. It's all China and The US from here. Like, I I just can't see that, being an acceptable answer. Llama three, he he mentioned. I did look up. Llama three was... Get the exact dates here. July 2024, and it was trained on 3.8 times 10 to the 25 flops. So llama three more than two years ago, nearly four times the input compute that Mistral Large Four has today. That is a... There's a... I think a lot can be, unpacked from that one statistic. Part seven, what the open model shouldn't know. After Freeman left, Prakash and I took up whether you can keep a capability out of a model at all. Prakash's case started with his account of the 50¢ army being paid to scrub the Internet of things like Tiananmen Square.
01:05:58 · Prakash Narayanan
[01:05:58] S6: And so in order to get these guys to understand what they're supposed to censor, they had to teach them what Tiananmen Square was. So the CCP ended up having these party classes to teach the the 50 centers what was Tiananmen Square, and what you should be looking for when you want a sensor. And I think that is gonna be largely true of almost all things LLM, where these guys are trying to teach it what not to do, and they're going to specifically take out these behaviors and the LLM is going be left wondering, all right, so these are all the good behaviors and I can have antonym to every good behavior, that antonym should go into this solution space and this latent space that I should be seeing that antonym in doesn't exist.
01:06:46 · Nathan Labenz
[01:06:46] S0: Prakash's conclusion was that you can't carve a capability out of the latent space. My reply? I'm I'm kinda more bullish on some of these, like, not necessarily unlearning, but, you know, I I just recorded a podcast earlier today where I was talking a bit about how feasible it is. I mean, there's there's multiple levels of control, of course, but, like, there are efforts underway to basically lock up certain classes of biodata that are being newly generated now because the worry is that they'll create dangerous capabilities and models if they're they're used in training, and not necessarily to shut that out, you know, forever from everyone, but at least to have it on kind of a know your customer basis or whatever. It does seem that those... What what few experiments have been done on, like... And, you know, I'm thinking here of ESM and also some work, the EVO series of models that came out of ARC Institute and Stanford and whatever big collaborations, of course. It seemed like they showed that if you do withhold certain classes of data, you can get generally capable models that that still kind of don't know about these areas of, censorship. So I'm I'm not so sure that that can't work. Took that question to Sagar when he came on. First though, Mistral built Large four with cyber defense as a focus, and Prakash asked how that changed the training.
01:08:22 · Sagar Vaze
[01:08:22] S4: So so the cyber capabilities and agent decoding capabilities in general are something which are typically given to the model in the in the post training stage. So most of the stuff we've discussed today so far has been about pre training. So Right. Where a large percentage of the flops go at at the start. Right. And then in post training is where you say, okay, well, what are people going to really use this model for? And that's when, you know, you start giving it these these new capabilities of finance, law, defense, etcetera. Earlier in the year, there were there were some events where some very capable models had restricted access to both certain geographies as well as to certain enterprises. And at the same time, we were seeing that these very capable models had these incredible cyber capabilities, which often were being jailbroken by malicious actors in order to to infiltrate or expose vulnerabilities in in, you know, legitimate defenders. And an imbalance which we saw here is that these defenders were then trying to, you know, use these same models to patch their own vulnerabilities, but were finding that their requests were refused. Because in practice, for very good reasons, these closed labs want to make sure that the models are not misused. They've got classifiers under the hood to identify malicious cyber requests from a standard request. But unfortunately, in practice, if you're a defender and you're trying to shore up your infrastructure, your defensive request looks quite a lot like a malicious request because you have to try and find vulnerabilities in your own code base to patch them. And so we thought it was imperative for there to be an open weight cyber model of similar capabilities to these closed models such that, you know, enterprises could scalably defend themselves.
01:09:59 · Nathan Labenz
[01:09:59] S0: Prakash noted that people had been taking open weight models and obliterating them, stripping out their refusals to run cyberattacks. He asked whether Mistral Large Four ships with guardrails or comes pre obliterated.
01:10:15 · Sagar Vaze
[01:10:15] S4: Yes. So something... This is something we discussed in our blog post. So, you know, we believe in open ways because we think that it's a net benefit to society. We think that it's a net benefit to the ecosystem. And so we're always dynamic about exactly what... How to balance that net benefit. In practice, what's happened here is that, you know, we do understand that these models can be misused. So we do have a version of Mistral Large four, which has been released, which has been now assessed, which refuses the most dangerous cyber requests. And then we're working with trusted third parties... Sorry, trusted partners to give them access to the unobliterated model unobliterated model so that they can defend themselves. And, you know, these are people we vet and make sure that make sure that they're using it in ways we find responsible. But coming back to the the framing of the question, your framing of question was that, okay, there's this open source model, say, I mean, it could be Mistral's. It could be anyone else's. And there are some guardrails, and in principle, these guardrails can be overcome by, you know, malicious actors so that these capabilities can be unlocked. I think that's the sort of sentiment. But what I would highlight is that, you know, that anytime you have access to the model, this is in principle possible, right? Even if it's behind a closed source API, because you can jailbreak a model regardless of whether you have access to the weights or not. And so here I'd consider the counterfactual, right? Imagine if we were in a world in which there was only closed source models. Right? And these are... This jailbreaking still existed and there was still a way in which you could use them to to perpetrate malicious attacks. Where would we be? We really wouldn't... We would have no recourse because these same these same same these defensive requests are being blocked by these same companies. And if there was no open waste model, we would really have no scalable way to defend our infrastructure as these models become more and more capable, which we're seeing in each iteration. So we we understand the risks. We have a responsible perspective every time that we we open source a model. It's something that we consider very carefully.