291–300 of 558 posts
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But perhaps we should regard it as a major achievement.
I mean in the same way getting Wolfram Alpha to solve a really hard/ugly differential equation I suppose
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I assume you're using the "regular" Pro version of Gemini 3.1 for the above, rather than the Deep Think mode, which is more comparable to GPT-5.5 Pro. To my knowledge, regular 3.1 Pro is a tier below and often makes mistakes. Moreover, there's no reason to believe the progress of LLMs, which couldn't reliably solve high-school math problems just 3–4 years ago, will stop anytime soon. You might want to track the progr…
There are many indications that model progress is slowing down, so that is not entirely accurate.
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Compilers just made it all possible, but they are not new and shiny. LLMs did not produce the philosophical questions, but they do raise them. It's worth noting that computers have been changing the way we think about consciousness long before LLMs, largely thanks to compilers.
Yea there’s no logical stopping point when you use that logic. Why not say electricity or the element silicon?
Not to mention that the less easily-explainable a technical achievement is, the less investment it will attract simply because fewer people will grasp the ramifications. You can describe AI in two words ("machine human") while it would take a few more to describe compilers in an instantly understandable way.
If we look where these models were 5-7 years ago...the existential threat of the Ph.D. was not even on the radar back then. The people finishing up their doctorate now are the first that can truly leverage these tools.
Now, if these to-be researcher students feel defeated (enough to quit), or completely lean on AI models the work for them, we're going to have a problem. Same with the funding of those Ph.D. positions. If we move away from "funding to produce researchers" to "funding to achieve results", will money that was usually spent to fund Ph.D. students start to flow towards compute?
If we look at it a bit cynically: Some researcher will be able to pump out a lot more papers by spending money on compute, than a couple of years of training students.
Interesting times. But also so much uncertainty. I feel terrible for the students that will have to decide now what they want to do, with all this knowledge.
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> there's no reason to believe the progress of LLMs [...] will stop anytime soon Wrong. Every advancement has followed a s curve. Where we are on that curve is anyones guess. Or maybe "this time its different".
This could be right for the current architecture of LLMs, but you can come up with specialized large language models that can more efficiently use tokens for a specific subset of problems by encoding the information differently ( https://www.nature.com/articles/d41586-024-03214-7 ). So if instead of text we come up with a different representation for mathematical or physical problems, that could both improve the qual…
But then, wouldn't we first have to translate all of our current math and physics knowledge into that new representation in order to be able to train a model on it? Looks like a tremendous amount of work to me.
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While this sounds generous (and in some ways it is), it does not address the general point that GP is making. That is, the systematic disadvantage which large parts of humanity have w.r.t. to access to the tools. You could say they can't drive a Lambhorgini either, but that also doesn't solve the problem.
Its a problem of the individual institutions and countries. The budget required for AI tools currently is negligible compared to other university expenses. We don't need to call everything a systemic disadvantage when the disadvantaged (at the institution level) have agency here.
Is it? Do you have any idea what the salary of a mid-tier university researcher in an Eastern European country is? Or in Africa or south-east Asia? With sota LLM pricing you easily get into the same order of magnitude, so essentially labour cost would double for researchers at such universies. Not "negligible" at all.
> "Even though I can motivate it in retrospect, ChatGPT’s idea to use h^2-dissociated sets to control relations of order at most h feels quite ingenious. As far as I can tell, this idea is completely original." The question that keep bothering me is can an LLM generate an idea that is truly novel? How would/could that actually happen? But then that leads to the question - what are we actually doing when we think? Per…
Yes, they can. Some people like to parrot "next token prediction", "LLMs can only interpolate", and other nonsense, but it is obviously not true for many reasons, in particular since we introduced RL. Humans do not have the monopoly on generating novel ideas, modern AI models using post training, RL etc can come to them in the same way we do, exploration. See also verifier's law [0]: "The ease of training AI to solve…
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Its a problem of the individual institutions and countries. The budget required for AI tools currently is negligible compared to other university expenses. We don't need to call everything a systemic disadvantage when the disadvantaged (at the institution level) have agency here.
Can you tell me what is the budget necessary to supply AI tools capable of substantial research assistance to all academic staff at a university? You seem to have a good estimate in your head; I definitely do not. From personal experience, ChatGPT 5.5 (the Plus tier) is excellent for programming tasks and also for various teaching related tasks but I have not observed the research benefits that Tim Gowers has when I…