Tim Scarfe: commitment

Transcript context

…right now is this this idea of of trying to to build hardware that best fits like the models we have right now and just like really make a super efficient inference. You could say like the in the past at least the issue has been you stop yourself from innovating on the software side, but if you lock down your hardware too much, right, like the NVIDIA chips has been pretty good for innovation, like they're pretty flexible. Of course, they also have guided the way we do AI in a lot of ways like towards matrix multiplications and so on. Still people have been able to innovate. But on the other hand, I think also hardware is getting easier and easier to make with these kind of AI for EDA tools. So it's sort of shortening the like maybe now people are thinking about like making their cooler kernels, right? It's like it's not so unreasonable to think that soon we'll just be doing our AI for making like instead of CUDA kernels, we'll just like make some custom circuits for every single thing we want. So if you come up with a if you want your new algorithm to run really fast, you just design like a specialized circuit for that. And the AI helps you like optimize it and check that it's correct. Of course you still have the fab, but people are we're getting better at like batching things for the fab. I mean I also think having the really flexible hardware is gonna be very interesting, like the stuff where it's learning on chip and ideally like maybe the more adaptability that it has and the less we need to bake in. It's I mean, don't know. It's just more cool, I think. Yeah. But it isn't that a wonderful example of this recursive self improvement? Because as you say, like, we're we're we're building the AI and then the AI is helping us build better kernels, better software, better hardware, which then in turn makes the AI better and then, you know, you get this this kind of loop. IONS is a recursive self improvement in a way. Right? Exactly. Exactly. But but but we should bring this to life. And so there's this concept called auto form formalization. And and and you guys on your website, you say basically that you have done something similar to AlphaProof in respect of building chips. And roughly speaking, AlphaProof is when, you know, they it was they won silver at the IMO in 2024. And what they did back then was they used a language model to generate, a bunch of lean code. And then obviously was a little bit messy, so some of the formalization was by hand and they did multiple renditions and so on. And then they would do verification with lean. And you're kind of doing something like that for chips. Yeah. Yeah. So it's interesting because there is the order formalization which we define as taking human specifications and writing up and turning them into formal specifications in Lean for example. Then, but then of course you also need the proof step where you provide the yes, prove that your code, whatever you have actually satisfies that specification. And I think some companies like Acxiom, for example, they're very focused on this part, right? Like it's and also in some sense AlphaProof, that's also the main thing it did was it started with a formalization and then the hard part was training the model to provide a proof or disprove. I think it's actually really nice trick in AlphaProof was that when they did the formalization of the proof, it didn't really matter if they got it right or wrong because if they just asked the model to provide a proof or a disprove. So if they got it wrong and it was no longer true, then it would just prove that it was not true. Like it would that was like as you could still use it. Of course, like when they actually did the IMO challenges, they wanted the form out of formalization to be correct. So then they did it by hand. But they didn't need it for the training, which I think helped scale it up. I think, yes, so can do a similar thing with hardware by the way. It's pretty easy to just take some chip design and then you know, you can basically just come up with some properties that may and may not be true. And then you can train the model to try and prove or disprove that this thing holds. So then, but that's all about creating the proof. Then the orthoformization, in some sense it's harder because, it's all like because it's harder to create the training data for it, right? Like I think that's kind of also a story about AI in the last 2 years since reinforcement learning. Like anything you can create good RL environment for, you can probably learn. But anything else is like out of reach right now. So some of these chips have thousands of pages of specifications. And if you want to turn that, yeah, you're turning that into a formal model. It's not, it doesn't really like if you got just a couple of words wrong somewhere or a couple of numbers, then it doesn't work. Like then what you prove is not important or is not relevant. And I think, I mean it's always been an issue for, in the chip industry like that. And they've kind of tried to solve it by having orthogonal teams. So they have 1 team that's designing the chip and 1 team that's designing the tests and another team that's designing kind of tests of the tests, like where they kind of like coverage, they call it functional coverage, where they measure what the tests test and then check everything off. So hopefully like if all of the 3 teams have read something the same way and understood it the same way, they have the right idea of it. I mean you can try and do something similar with AI. You can argue whether it's really orthogonal if it's the same model that are doing each of the 3 jobs.…

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