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

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

#361

Post by datsci_est_2015 »

trick-or-treat wrote: post #234
wobfan wrote: post #187

Earlier quoted context omitted.

It feels like you're purposefully ignoring the logical points OP gives and you just really really want to anthropomorphize AlphaGo and make us appreciate how smart it (should I say he/she?) is ... while no one is even criticising the model's capabilities, but analyzing it.

Can you back that up with some logic for me? I don't really play Go but I play chess, and it seems to me that most of what humans consider creativity in GM level play comes not in prep (studying opening lines/training) but in novel lines in real games (at inference time?). But that creativity absolutely comes from recalling patterns, which is exactly what OP criticizes as not creative(?!) I guess I'm just having trou…

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 (anthropomorphization), but holds us back in critically assessing a model’s capabilities.

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

#362

Post by stavros »

staticassertion wrote: post #134
virgildotcodes wrote: post #56

I don't know why I am still perpetually shocked that the default assumption is that humans are somehow unique. It's this pervasive belief that underlies so much discussion around what it means to be intelligent. The null hypothesis goes out the window. People constantly make comments like "well it's just trying a bunch of stuff until something works" and it seems that they do not pause for a moment to consider whethe…

> I don't know why I am still perpetually shocked that the default assumption is that humans are somehow unique. Because, empirically, we have numerous unique and differentiable qualities, obviously. Plenty of time goes into understanding this, we have a young but rigorous field of neuroscience and cognitive science. Unless you mean "fundamentally unique" in some way that would persist - like "nothing could ever do w…

No, but it does mean that you should know we don't understand what intelligence is, and that maybe LLMs are actually intelligent and humans have the appearance of intelligence, for all we know.

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

#363

Post by virgildotcodes »

slopinthebag wrote: post #91
virgildotcodes wrote: post #69

Earlier quoted context omitted.

Every living thing on Earth is unique. Every rock is unique in virtually infinite ways from the next otherwise identical rock. There are also a tremendous number of similarities between all living things and between rocks (and between rocks and living things). Most ways in which things are unique are arguably uninteresting. The default mode, the null hypothesis should be to assume that human intelligence isn't intere…

I doubt you can even define intelligence sufficiently to argue this point. Since that's an ongoing debate without a resolution thus far. But you claimed that humans aren't unique. I think it's pretty obvious we are on many dimensions including what you might classify as "intelligence". You don't even necessarily have to believe in a "soul" or something like that, although many people do. The capabilities of a human f…

> I doubt you can even define intelligence sufficiently to argue this point.

Agreed.

> But you claimed that humans aren't unique.

I'm arguing that it is up to us to prove that they are interestingly unique in the context of this post. Which is pretty narrow - how do we solve problems?

The theme I was arguing against that I've seen repeated throughout this thread is that AIs are just recombining things they've absorbed and throwing those recombinations at the wall until they see what sticks.

It raises the question of why we presume that humans do things any differently, when it seems quite clear that we can only ever possibly do the same, unless we are claiming that knowledge of the universe can enter the human mind through some means other than through the known senses.

Not at all disputing that humans possess many capabilities that AIs do not.

> Do you ever wonder why that is? I often wonder why tech has so many reductionist, materialist, and quite frankly anti-human, thinkers.

I touched on this elsewhere, will go ahead and paste it here again:

The fundamental thing I'm speaking out against is the arrogance of human exceptionalism.

This whole debate about what it means to be intelligent or human just seems like we're making the same mistakes we've made over and over.

Earth as the center of the universe, sun as the center of the universe, man as the only animal with consciousness and intellect, the anthropomorphic nature of the majority of the deities in our religions and the anthropocentric purpose of the universe within those religions...

I think this desire to believe that we are special, that the universe in some way does ultimately revolve around us, is seemingly a deep need in our psyche but any material analysis of our universe shows that it is extremely unlikely that we hold that position.

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

#364

Post by amelius »

svara wrote: post #340

The capabilities of AI are determined by the cost function it's trained on. That's a self-evident thing to say, but it's worth repeating, because there's this odd implicit notion sometimes that you train on some cost function, and then, poof, "intelligence", as if that was a mysterious other thing. Really, intelligence is minimizing a complex cost function . The leadership of the big AI companies sometimes imply some…

> But there is no mechanism to generate a model with capabilities beyond what is useful to minimize a specific cost function.

Can you give some examples?

It is not trivial that not everything can be written as an optimization problem.

Even at the time advanced generalizations such as complex numbers can be said to optimize something, e.g. the number of mathematical symbols you need to do certain proofs, etc.

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

#365

Post by stavros »

snemvalts wrote: post #76
virgildotcodes wrote: post #56

I don't know why I am still perpetually shocked that the default assumption is that humans are somehow unique. It's this pervasive belief that underlies so much discussion around what it means to be intelligent. The null hypothesis goes out the window. People constantly make comments like "well it's just trying a bunch of stuff until something works" and it seems that they do not pause for a moment to consider whethe…

The ability to learn and infer without absorbing millions of books and all text on internet really does make us special. And only at 20 watts!

Most people have absorbed way too few books to be able to infer properly. Hell, most people are confused by TV remotes.

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

#366

Post by gf000 »

razorbeamz wrote: post #301
gf000 wrote: post #247

Earlier quoted context omitted.

So you don't think 50T parameter neural networks can encode the logic for adding two n-bit integers for reasonably sized integers? That would be pretty sad.

They do not. The fundamental technology behind LLMs does not allow that to be the case. You are hoping that an LLM can do something that it cannot do.

https://arxiv.org/html/2502.16763v2

You are wrong. Especially that we are talking about models with 50T parameters.

Can they do arbitrary computations for arbitrarily long numbers? Nope. But that's not remotely the same statement, and they can trivially call out to tools to do that in those cases.

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

#367

Post by LatencyKills »

stavros wrote: post #349
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…

What was the solution?

Well, I'm not going to share either solution as this is actually a pretty useful utility that I plan on releasing, but the short answer is: 1) don't use ScreenCaptureKit, and 2) take advantage of what CGWindowListCreateImage() offers through the content server. This is a simple IPC mechanism that does not trigger all the SKC limitations (i.e., no multi-space or multi-desktop support). In fact, when using SKC, the user cannot even minimize the "watched" window.

Claude realized those issues right from the start.

One of the trickiest parts is tracking the window content while the window is moving - the content server doesn't, natively, provide that information.

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

#368

Post by pu_pe »

energy123 wrote: post #68
maxrmk wrote: post #61

Earlier quoted context omitted.

I don't think this is a correct explanation of how things work these days. RL has really changed things.

Models based on RL are still just remixers as defined above, but their distribution can cover things that are unknown to humans due to being present in the synthetic training data, but not present in the corpus of human awareness. AlphaGo's move 37 is an example. It appears creative and new to outside observers, and it is creative and new, but it's not because the model is figuring out something new on the spot, it's…

How do you know that? We don't have access to the logs to know anything about its training, and it's impossible for it to have trained on every potential position in Go.

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

#369

Post by stavros »

gf000 wrote: post #285
virgildotcodes wrote: post #69

Earlier quoted context omitted.

Every living thing on Earth is unique. Every rock is unique in virtually infinite ways from the next otherwise identical rock. There are also a tremendous number of similarities between all living things and between rocks (and between rocks and living things). Most ways in which things are unique are arguably uninteresting. The default mode, the null hypothesis should be to assume that human intelligence isn't intere…

Humans are obviously unique in an interesting way. People only "move the goalpost" because it's not an interesting question that humans can do some great stuff, the interesting question is where the boundary is. (Whether against animals or AI). Some example goals which makes human trivially superior (in terms of intelligence): invention of nuclear bomb/plants, theory of relativity, etc.

But that's unique in the sense of "you have a bag of ten apples and I have a bag of eleven apples, therefore my bag is unique". It's not qualitatively different intelligence than a dog's, you just have more of it.

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

#370

Post by datsci_est_2015 »

parasubvert wrote: post #157
razorbeamz wrote: post #154

Earlier quoted context omitted.

Next-token-prediction cannot do calculations. That is fundamental. It can produce outputs that resemble calculations. It can prompt an agent to input some numbers into a separate program that will do calculations for it and then return them as a prompt. Neither of these are calculations.

Humans can't do calculations either, by your definition. Only computers can.

Third things can exist. In other words, you’re implying a false dichotomy between “human computation” and “computer computation” and implying that LLMs must be one or the other. A pithy gotcha comment, no doubt.

Edit: the implication comes from demanding that the OP’s definition must be rigorous enough to cover all models of “computation”, and by failing to do so, it means that LLMs must be more like humans than computers.

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