Brad Carson: evaluation

Transcript context

…I know that 1 thing that you advocate for is is increased funding for for academia. Right? Like, because hey, do could they even afford to train a large scale model? Like, no. And actually, we've discussed this before in our server, because we have quite a few academicians in there. And it turns out, it's it's actually an interesting problem because on the 1 hand, you say like, oh, absolutely, like we want academics to have more money so that they can afford to do test on, you know, large scale models. But on the other hand, necessity is the the mother of innovation. Right? And so sometimes by not having the funds, it forces them to find more efficient, clever ways, you know, to to do the testing. So there's this interesting trade off between supplying them the money to do the things they need to do, but not not discouraging them from from innovating with less is more. Right? I suppose the the problem the Chinese have, right, in their own way. You know, it's like, you know, they don't get the chips, and so they innovate on algorithms and and architecture in some way. Right? So, yes, mother is the necessity of invention. And when you have, as the companies do now, right, these chips are coming out. Right? They they're emphasizing compute and scaling rather than some of the other things that might actually, you know, help them if they didn't have those kind of resources. Yeah. I do think the universities need more. I think the challenge is this. You you can get access to cloud computing now, so you don't actually need your own GPUs if you're at the University of Tulsa. You know, you can go to, you know, 1 of the providers of it out there. But if you're a top level ML, PhD graduating from MIT or Berkeley, Stanford, Caltech, you know, the 5 or 6 places that are Carnegie Mellon, they're really turning these out, odds are, almost certainly, you're not gonna go into academia at all. You're gonna go work for a lab. And so, you know, the number of people who are being siphoned off into the private sector, not into universities, said is legion. It's very hard to hire people, at the top institutions, you know, who are very good at these kind of fields. The money is not only far superior at the companies. They actually have better access to data, you know, and so they have a lot of things going for them. So I do think it's a problem. The universities, the public sector more generally, you know, right, is not doesn't have access to, like, all the resources, all the career opportunities. You know, the papers are now not even being published by the labs a lot of times, so the research is being kind of kept internal as well. You know, this is a general purpose technology as everyone defines it. It's probably the first 1 in history that's being developed behind closed doors, right, with very little public oversight and with the best minds going behind the doors. So, yeah, I think there should be you know, I'm always excited personally by when I read Argonne is going to try to develop their own LLM for you know, the public sector. I'm like, yeah, that's like a good project. I'm for that. You know, there's I see efforts out of Zurich where they're trying to do kind of public AI and, you know, make it where government or civil society, NGOs, these kind of groups, nonprofits can have access to compute and, you know, have access to models that work for them. Yeah. I think that's all really important. Because I think, again, you know, the kind of non lab world is is not getting access to these kind of things. And the best minds, all the money, the best data all goes, you know, to 1 of these companies. And that's remarkable in some ways, but the downstream effect of that is universities are really, I think, suffering these days. You know, the old days of supercompute. Right? Where where there were the school had access to a supercomputer and researchers could could kind of would have quotas of time that they could they could run on the supercomputers. Could probably arrange some type of of sort of public sharing of of, you know, large scale model training. It's it's a bit tricky, but but still I I think you're absolutely right that more is needed and it'd be interesting to have a lot of these kind of shared and co developed,…

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