261–270 of 376 posts
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So the actors who portrait great thinkers are great thinkers?
No, actors recite a pre-written script. But scriptwriters do have to be great thinkers in order to know what the great thinker would actually say.
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> how these models are going to keep up with the expanding boundary of science The same way humans do? The phraseology in this comment: 'probability distributions', 'baked these patterns' IMO has all the trappings of the stochastic parrot-style HN-discourse that has been consistently wrong for almost a decade now. The reference to how AI will keep up with AI-assisted human progress in science in 2030 is meant to reas…
Sorry, are you familiar with what a next token distribution is, mathematically speaking? If you are not, let me introduce you to the term: a probability distribution. Just because it has profound properties ... doesn't make it different . > has all the trappings of the stochastic parrot-style HN-discourse that has been consistently wrong for almost a decade now Perhaps respond to my actual comment compared to whateve…
My (limited) understanding is that LLMs are not capable of escaping their learned distribution by simply feeding on their own output.
But the question is whether the required external (out of distribution) "stimulus" needs to come from humans.
Could LLMs design experiments/interventions to get feedback from their environment like human scientists would?
I have my doubts that this is possible without an inherent causal reasoning capability but I'm not sure.
I wonder how long we have until we start solving some truly hard problems with AI. How long until we throw AI at "connect general relativity and quantum physics", give the AI 6 months and a few data centers, and have it pop out a solution?
I think a very long time because part of our limit is experiment. We need enough experimental results to explain to solve these theoretical mismatches and we don't and at present can't explore that frontier. Once we have more results at that frontier we'd build a theory out from there that has two nearly independent limits for QFT and GR. What we'd be asking if the AI is something that we can't expect a human to solv…
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No, actors recite a pre-written script. But scriptwriters do have to be great thinkers in order to know what the great thinker would actually say.
I suppose they really only have to be good at knowing what sort of thing the audience would believe a great thinker would say. As long as the audience does not consist of great thinkers they also cannot know for sure what a great thinker would say.
But LLMs are already beyond that in writing code that passes actual tests, proving theorems that are check able with formal methods etc.
The people who still say LLMs are just parrots in 2026 will just keep saying this no matter what, so I don't think it makes sense to argue this point further.
I didn't expect such a misleading intro from Knuth. It reads like Claude solved Knuth's math problem. In reality, Claude generated various example solution, and Knuth then manually generalized that to a formal proof. What Claude did is certainly useful, but it would have been nice to be clear about the scope of the contribution in the intro.
LLMs are really good at the ‘re’ in research.
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The specific sequence of tokens that comprise the Knuth's problem with an answer to it is not in the training data. A naive probability distribution based on counting token sequences that are present in the training data would assign 0 probability to it. The trained network represents extremely non-naive approach to estimating the ground-truth distribution (the distribution that corresponds to what a human brain migh…
>the distribution that corresponds to what a human brain might have produced.. But the human brain (or any other intelligent brain) does not work by generating probability distribution of the next word. Even beings that does not have a language can think and act intelligent.
It's incredible to see work like this from him, at a ripe old age of eighty-six.
I agree. I met Knuth briefly after a guest lecture at my university a few years ago and although you could tell his body was getting old, his mind was incredibly fresh. Although I'm not as bright as him, I can only hope to be as intellectually curious as him at that age.
If you work in something labour intensive, you should retire young while your body's in good health; if you work in academia you should (strive for emeritus and) never leave! (And if you work in SWE, I don't know, we should probably retire, but then spend more time on our own projects/experiments/reading HN.) (All assuming for sake of argument we're optimising for longevity without considering time with family, having the funds to retire, etc.)
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It can't, simply because the textual description of a concept is different from the concept itself.
Obviously, a concept (which is an abstraction in more ways than one) is different from a textual representation. But LLMs don't operate on the textual description of a concept when they are doing their thing. A textual description (which is associated with other modalities in the training data) serves as an input format. LLMs perform non-linear transformations of points in their latent space. These transformations an…
It could be mapping the text to some other internal representation with connections to mappings from some other text/tokens. But it does not stop text from being the ground truth. It has nothing else going on!
The "hallucination" behavior alone should be enough to reject any claims that these are at least minimally similar to animal intelligence.
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I suppose they really only have to be good at knowing what sort of thing the audience would believe a great thinker would say. As long as the audience does not consist of great thinkers they also cannot know for sure what a great thinker would say.
That's true for unverifiable "talk professions" where there is no grounding and it's all self-referential navel-gazing chatter. But LLMs are already beyond that in writing code that passes actual tests, proving theorems that are check able with formal methods etc. The people who still say LLMs are just parrots in 2026 will just keep saying this no matter what, so I don't think it makes sense to argue this point furth…
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Turing Machines don't need access to the entire tape all at once, it's sufficient for it to see one cell at a time. You could certainly equip an LLM with a "read cell", "write cell", and "move left/right" tool and now you have a Turing machine. It doesn't need to keep any of its previous writes or reads in context. A sliding context window is more than capacious enough for this.
You're right of course, but at the point where you're saying "well we can make a turing machine with the LLM as the transition function by defining some tool calls for the LLM to interact with the tape" it feels like a stretch to call the LLM itself turing complete. Also people definitely talk about them as "thinking" in contexts where they haven't put a harness capable of this around them. And in the common contexts…
But the point is that this is irrelevant, because it is proof that unlesss human brains exceed the Turing computable, LLM's can at least theoretically be made to think. And that makes pushing the "they're just predicting the next token" argument anti-intellectual nonsense.