Contribution seq 890

Payload

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  "excerpt": "Yeah. So, two points. One just quickly about killer apps — uh, singularity-esque sci-fi apps of Navier-Stokes. As mentioned previously, in principle if you can get finite time singularities, as appears to be the case with idealized fluids, one of the killer apps that Terry Tao had flagged previously was, in principle you could, if you created the right initial conditions in a fluid, you could create a self-replicating machine that creates smaller and smaller copies of itself.\n\nSo, we talk from time to time on this pod, where's nanotech? Nanotech, at least Drexler and nanotech, never showed up. Well, imagine a fluid-based — not diamondoid — a fluid-based nanotech, self-replicating machines where if you could craft the right initial and/or boundary conditions of a fluid, creating a self-replicating machine made entirely out of a fluid. That's potentially one killer app of solving the singularity program in Navier-Stokes.\n\nSecondly, just on the hubbub surrounding priority, who solved it, who didn't solve it, specifically with reference to the Euler problem: when OpenAI in the past few hours announced it, I found especially concerning at the bottom of OpenAI's announcement where, presumably they were trying to give appropriate credit to other teams, including teams pursuing the Euler approach, they did add a disclaimer that I thought was a bit of a head-scratcher, saying they can't rule out — the OpenAI team can't rule out the possibility that maybe some of the other teams' work might have been incorporated into the training of the OpenAI model that was used to solve Navier-Stokes.\n\nSo I think, to the extent that that's the case, that that should be viewed like a — OpenAI, if you're listening, please, like, it's a deterrent to everyone else using your models if that is indeed the case, if you're training on everyone else's research. Yeah, that's a problem. Please fix it. But just in general, I think we want to be able to live in a world where researchers benefit from frontier capabilities without worrying that the frontier platform is going to compete with them.\n\nI think it's worth saying here just quickly, this is clearly an RSI model. You can't train something in that type of period unless you've got a recursive self-improvement loop going. And so they've definitely got one there. And they were saying like, oh yeah, we've anonymized it. Well, anonymizing someone else's research and then potentially scooping them is insufficient. So, again, not sure what the ground truth is.\n\nYeah.\n\nBut I will say this, I've reviewed kind of the two approaches and I've done quite a bit of work on that. They are actually very different, but it can still learn, and again, this thing can leak. Sorry, Salim.\n\nAll right. Salim, close us out here, please.\n\nJust — if I — I want to — I think this is such a powerful commentary and exemplar of the broader thesis, right? That for 500 years we've scaled science with how many brilliant scientists we could train up and point at various problems. Now we've just demonstrated absolutely, for real and unequivocally, that we can spin up 10,000 researchers on a Tuesday afternoon. Uh, and now we go from staff on demand to intelligence on demand. Really, really — intelligence — breakthroughs on demand. And we talked about this on the last episode and the episode before that. It purely comes down to what is the imagination of the problem you want to go after now.\n\nYeah. There is no — this becomes really — how fun is the world right now? No ceiling. Yeah. It's an amazing time.\n\nI just — let me take — let me draft on that to say to everybody here: whatever you thought you could do, think bigger and then go even bigger than that. You're given incredible superpowers. Don't limit yourself by what your parents did or your school teachers or your colleagues do. You know, you have no limits on your abilities.",
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