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I think OpenAI's limitations are quickly verifiable results. 10,000 agents running in parallel aren't exactly talking to each other. They have a goal and one eventually hits the goal which can be automatically confirmed. This is why math is currently attacked, lean is easy to confirm A = B.

They aren't gonna create room temperature superconductors because they'll need a lab that can quickly, and verifiably, test superconductors at 10,000 per minute and that's just a physical limitation no matter how many datacenters you throw at it.

by RandomLensman15 hours ago|

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Why would that necessarily be accessible by thinking only? Or do you see them running or directing large laboratories of various types?

by derektank12 hours ago|

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AI systems will probably be able to, if not direct labs themselves, cut down a lot of the search space to only the most promising candidates. That’s basically what Isomorphic Labs is doing for candidate drug research. It doesn’t seem crazy that the same could be true for material sciences.

by RandomLensman12 hours ago|

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Maybe. Are we seeing any indication of increased success rates in stage 3 trials already for "AI drug candidates"?

Could also be that some things are not possible in some ways. I wonder if there is some general theory on "shortcuts".

by jacquesm11 hours ago|

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Stage 1, yes, stage 2, it's a wash (for now at least), which does not promise much for stage 3, but it is still a bit early for that.

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Simulation to the point that the accuracy is large enough to extrapolate to real materials. You can do a lot of stuff when you have access to a million GPUs that would otherwise be much harder.

by RandomLensman14 hours ago|

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I have my doubts there, but we will see.

https://en.wikipedia.org/wiki/Computational_chemistry

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A lot of materials science is already done through simulation, same with a large chunk of chemistry and biology. That fraction will only increase over time.

https://en.wikipedia.org/wiki/Computational_biology

https://en.wikipedia.org/wiki/Computational_materials_scienc...

These fields are all going through a tremendous acceleration right now.

by RandomLensman14 hours ago|

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I am aware of advances, but I think a large chunk is a stretch in various ways. We'll just see what happens - simulating even a glass of water fully is tough.

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That depends on the granularity of your simulation. For most - not all - purposes a glass of water can be simulated coarsely or by simulating a far smaller amount with greater fidelity and then to extrapolate.

For useful materials science the numbers are still large but far lower than the numbers involved in 'a glass of water'.

by RandomLensman14 hours ago|

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I really depends on what is needed. If you know that something quite "coarse" is enough to create/uncover room temperature superconductors with high critical current - fair enough. Again, we'll see what happens.

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With the kind of compute they have at their disposal 'quite coarse' may actually be 'impressively detailed' and that is why I think they have an edge on this kind of stuff. It's like being able to borrow a trillion and then to go play research lab, it would be highly surprising if they weren't able to monetize such a head start.

by djoldman14 hours ago|

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If they did that and were able to find someone to pay them, that'd be a pretty good contribution, no?

by azan_12 hours ago|

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> I expect their motivation for devoting resources to solve these problems will dramatically drop as soon as they IPO. Maths is just a convenient vanity metric for OpenAI to demonstrate progress, but long term progress in mathematics has no commercial value to them.

And why would IPO remove incentive that's present pre-IPO? Somehow math progress will influence price at IPO but won't influence stock price post-IPO? Or will they just stop caring about stock price once they are public. None of these options makes any sense.

by czzr4 hours ago|