Dan Balsam: belief

Transcript context

…Just to give people a little bit of additional sense of the breadth of things that are already out there, and we can put links to the show notes, including, like, links to the actual Silico projects. Cameron Berg, second mention. Cameron, if your ears are burning, hello. He did one where he was looking into models' ability to report on concepts that had been injected into their latent space, finding interestingly that they seem to not be able to tell whether or not something has been injected. But then when they're asked what has been injected, they can give accurate answers, which is pretty weird. One is on editing weights to fix a collapse in an RL run where there was, like, the same token was coming up as, like, the first token all the time and somehow going in and isolating what was causing that, removing that, and then getting diversity back without having to redo the whole RL run. The good folks at base ten are pursuing efficiency gains by trying to compact KV caches, and so they've got some results on on that. I don't think they've shared their whole project, but they've talked about it on Twitter. Prime Intellect is is automating post training, which is one of the things that I'm also, like, really interested in in general. And there and there's a Tinker API integration too that is is obviously lends itself to that sort of thing. Various bio results that are probably out of scope for today's discussion. But there's, like, an awful lot of different things already. What would you say is there anything that it, like, doesn't do, or can you really just think of it as kind of anything you might wanna do that's ML research, fire it up and start a conversation about it? I think it's a pretty general purpose tool for for ML research. Of course, it has weaknesses, and we're we're working on improving it all the time. I think one of the, like, very odd things about the AI era from an engineering perspective is, like, the the classic engineering advice is, like, build for specificity and then generalize. But when you're dealing with, like, kind of general intelligences, kind of the right strategy is build for generality and then specialize. And so I think we've just built, like, very capable agents at the end of the day. And sometimes we're surprised by the way that people use them. But our intended function of these agents is is ML research, variety kinds, especially interpretability research. But we think this is all interrelated. Like, we're all trying to understand the same problem, which is what are these how do these creatures work, and…

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