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Epoch confirms GPT5.4 Pro solved a frontier math open problem

epoch.ai

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Re: Epoch confirms GPT5.4 Pro solved a frontier math open problem

#431

Post by XCSme »

glalonde wrote: post #111
XCSme wrote: post #18

Earlier quoted context omitted.

Not sure if AI can have clever or new ideas, it still seems to be it combines existing knowledge and executes algoritms. I am not necessarily saying humans do something different either, but I have yet to see a novel solution from an AI that is not simply an extrapolation of current knowledge.

"extrapolation" literally implies outside the extents of current knowledge.

Yes, but not necessarily new knowledge.

It means extending/expanding something, but the information is based on the current data.

In computer games, extrapolation is finding the future position of an object based on the current position, velocity and time wanted. We do have some "new" position, but the sistem entropy/information is the same.

Or if we have a line, we can expand infinitely and get new points, but this information was already there in the y = m * x + b line formula.

Re: Epoch confirms GPT5.4 Pro solved a frontier math open problem

#432

Post by timschmidt »

datsci_est_2015 wrote: post #426
timschmidt wrote: post #419

Earlier quoted context omitted.

> claim that there are no counterexamples of human and AI output being indistinguishable on the internet? Is that a claim I've made? I don't see it anywhere. I think a lot of people think that because they can get the AI to generate something silly or obviously incorrect, that invalidates other output which is on-par with top-level humans. It does not. Every human holds silly misconceptions as well. Brain farts. Fat…

> Is that a claim I've made? Yes, you literally just said QED.

Are we reading the same thread?

You said AI works were easily distinguishable via em-dashes and "not this, but that"

I said I have witnessed humans using that metric accuse other humans here on hackernews. Q.E.D.

You've asserted that they are easily distinguished. Practitioners in the field fail to distinguish using the same criteria. Is that not dispositive? Seems like it to me.

I claimed much earlier in the thread "I think there's demonstrably very little difference at all between human and AI outputs" which is consistent with "I think all the humans I know and all the LLMs I've interacted with exist on a multidimensional continuum of intelligence with significant overlap."

Two ways of saying the same thing.

Both of them suggesting that sometimes you may be able to tell it's the output of an AI or Human, sometimes not. Sometimes the things coming out of the AI or the Human might be smart in a way we recognize, sometimes not. And recognizing that humans already exist on quite a broad scale of intelligences in many axes.

Re: Epoch confirms GPT5.4 Pro solved a frontier math open problem

#433

Post by Paradigma11 »

thegabriele wrote: post #351
Paradigma11 wrote: post #219

Earlier quoted context omitted.

That question is equivalent to asking a human to add the wavelengths of those two colors and divide it by 3.

Why is that?

Because LLMs dont have a textual representation of any text they consume. Its just vectors to them. Which is why they are so good at ignoring typos, the vector distance is so small it makes no difference to them.

Re: Epoch confirms GPT5.4 Pro solved a frontier math open problem

#434

Post by gpderetta »

gf000 wrote: post #237
razorbeamz wrote: post #133

Earlier quoted context omitted.

Do you know what "LLM" stands for? They are large language models, built on predicting language. They are not capable of mathematics because mathematics and language are fundamentally separated from each other. They can give you an answer that looks like a calculation, but they cannot perform a calculation. The most convincing of LLMs have even been programmed to recognize that they have been asked to perform a calcu…

What calculations? Do you mean "3+5" or a generic Turing-machine like model? In either case, this "it's a language model" is a pretty dumb argument to make. You may want to reason about the fundamental architecture, but even that quickly breaks down. A sufficiently large neural network can execute many kinds of calculations. In "one shot" mode it can't be Turing complete, but in a weird technicality neither does your…

> In "one shot" mode it can't be Turing complete, but in a weird technicality neither does your computer have an infinite tape

Nor our brains, in fact.

Re: Epoch confirms GPT5.4 Pro solved a frontier math open problem

#435

Post by LatencyKills »

saagarjha wrote: post #420
LatencyKills wrote: post #333

Earlier quoted context omitted.

I've been working on a utility that lets me "see through" app windows on macOS [1] (I was a dev on Apple's Xcode team and have a strong understanding of how to do this efficiently using private APIs). I wondered how Claude Code would approach the problem. I fully expected it to do something most human engineers would do: brute-force with ScreenCaptureKit. It almost instantly figured out that it didn't have to "see th…

Why is ScreenCaptureKit a bad choice for performance?

Because you can't control what the content server is doing. SCK doesn't care if you only need a small section of a window: it performs multiple full window memory copies that aren't a problem for normal screen recorders... but for a utility like mine, the user needs to see the updated content in milliseconds.

Also, as I mentioned above, when using SCK, the user cannot minimize or maximize any "watched" window, which is, in most cases, a deal-breaker.

My solution runs at under 2% cpu utilization because I don't have to first receive the full window content. SCK was not designed for this use case at all.

Re: Epoch confirms GPT5.4 Pro solved a frontier math open problem

#437

Post by logicprog »

Yizahi wrote: post #404
qnleigh wrote: post #288

I am kind of amazed at how many commenters respond to this result by confidently asserting that LLMs will never generate 'truly novel' ideas or problem solutions. > AI is a remixer; it remixes all known ideas together. It won't come up with new ideas > it's not because the model is figuring out something new > LLMs will NEVER be able to do that, because it doesn't exist It's not enough to say 'it will never be able t…

LLMs can generate anything by design. LLMs can't understand what they are generating so it may be true, it may be wrong, it may be novel or it may be known thing. It doesn't discern between them, just looks for the best statistical fit. The core of the issue lies in our human language and our human assumptions. We humans have implicitly assigned phrases "truly novel" and "solving unsolved math problem" a certain mean…

If LLMs can come up with formerly truly novel solutions to things, and you have a verification loop to ensure that they are actual proper solutions, I don't understand why you think they could never come up with solutions to impressive problems, especially considering the thread we are literally on right now? That seems like a pure assertion at this point that they will always be limited to coming up with truly novel solutions to uninteresting problems.

Re: Epoch confirms GPT5.4 Pro solved a frontier math open problem

#438

Post by gpderetta »

brenschluss wrote: post #149
razorbeamz wrote: post #104

Earlier quoted context omitted.

The point I'm trying to make is that all LLM output is based on likelihood of one word coming after the next word based on the prompt. That is literally all it's doing. It's not "thinking." It's not "solving." It's simply stringing words together in a way that appears most likely. ChatGPT cannot do math. It can only string together words and numbers in a way that can convince an outsider that it can do math. It's a p…

sigh; this argument is the new Chinese Room; easily described, utterly wrong. https://www.youtube.com/watch?v=YEUclZdj_Sc

After dismissing it for a long time, I have come around to the philosophical zombie argument. I do not believe that LLMs are conscious, but I also no longer believe that consciousness is a prerequisite for intelligence. I think at this point it is hard to deny that LLMs do not possess some form of intelligence (although not necessarily human-like). I think P-zombies is a fitting description.

Re: Epoch confirms GPT5.4 Pro solved a frontier math open problem

#439

Post by cyanydeez »

qnleigh wrote: post #288

I am kind of amazed at how many commenters respond to this result by confidently asserting that LLMs will never generate 'truly novel' ideas or problem solutions. > AI is a remixer; it remixes all known ideas together. It won't come up with new ideas > it's not because the model is figuring out something new > LLMs will NEVER be able to do that, because it doesn't exist It's not enough to say 'it will never be able t…

When I read through what they're doing? It sure doesn't sound like it's generating something new as people typically think of it. The link, they provide a very well defined problem and they just loop through it.

I think you're arguing with semantics.

Re: Epoch confirms GPT5.4 Pro solved a frontier math open problem

#440

Post by datsci_est_2015 »

hackinthebochs wrote: post #429
datsci_est_2015 wrote: post #361

Earlier quoted context omitted.

How a model is trained is different than how a model is constructed. A model’s construction defines its fundamental limitations, e.g. a linear regressor will never be able to provide meaningful inference on exponential data. Depending on how you train it, though, you can get such a model to provide acceptable results in some scenarios. Mixing the two (training and construction) is rhetorically convenient (anthropomor…

Linear regression has well characterized mathematical properties. But we don't know the computational limits of stacked transformers. And so declaring what LLMs can't do is wildly premature.

> And so declaring what LLMs can't do is wildly premature.

The opposite is true as well. Emergent complexity isn’t limitless. Just like early physicists tried to explain the emergent complexity of the universe through experimentation and theory, so should we try to explain the emergent complexity of LLMs through experimentation and theory.

Specifically not pseudoscience, though.

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