121–130 of 437 posts
This sounds rather silly. Given the usual definition of AGI as being human like intelligence with some variation on how smart the humans are, and the fact that humans use a network of neurons that can largely be simulated by an artificial network of neurons, it's probably twaddle largely.
> And - as wonderfully remarkable as such a system might be - it would, for our investigation, be neither appropriate nor fair to overburden AGI by an operational definition whose implicit metaphysics and its latent ontological worldviews lead to the epistemology of what we might call a “total isomorphic a priori” that produces an algorithmic world-formula that is identical with the world itself (which would then mak…
Just to make sure I understand: –Are we treating an arbitrary ontological assertion as if it’s a formal argument that needs to be heroically refuted? Or better: is that metaphysical setup an argument? If that’s the game, fine. Here we go: – The claim that one can build a true, perfectly detailed, exact map of reality is… well... ambitious. It sits remarkably far from anything resembling science , since it’s convenien…
If you are claiming that human intelligence is not "general", you'd better put a huge disclaimer on your text. You are free to redefine words to mean whatever you want, but if you use something so different from the way the entire world uses it, the onus is on you to make it very clear.
And the alternative is you claiming human intelligence is impossible... what would make your paper wrong.
Earlier quoted context omitted.
The mathematical proof, as you describe it, sounds like the "No Free Lunch theorem". Humans also can't generalise to learning such things. As you note in 2.1, there is widespread disagreement on what "AGI" means. I note that you list several definitions which are essentially "is human equivalent". As humans can be reduced to physics, and physics can be expressed as a computer program, obviously any such definition ca…
1. I appreciate the comparison — but I’d argue this goes somewhat beyond the No Free Lunch theorem. NFL says: no optimizer performs best across all domains. But the core of this paper doesnt talk about performance variability, it’s about structural inaccessibility. Specifically, that some semanti spaces (e.g., heavy-tailed, frame-unstable, undecidable contexts) can’t be computed or resolved by any algorithmic policy…
We are generally intelligent only in the sense that our reasoning/modeling capabilities allow us to understand anything that happens in space-time.
Earlier quoted context omitted.
The mathematical proof, as you describe it, sounds like the "No Free Lunch theorem". Humans also can't generalise to learning such things. As you note in 2.1, there is widespread disagreement on what "AGI" means. I note that you list several definitions which are essentially "is human equivalent". As humans can be reduced to physics, and physics can be expressed as a computer program, obviously any such definition ca…
> As humans can be reduced to physics, and physics can be expressed as a computer program This is an assumption that many physicists disagree with. Roger Penrose, for example.
If you accept the conclusion that AGI (as defined in the paper, that is, "solving [...] problems at a level of quality that is at least equivalent to the respective human capabilities") is impossible but human intelligence is possible, then you must accept that the question is settled in favor of Penrose. That's obviously beyond the realm of mathematics.
In other words, the paper can only mathematically prove that AGI is impossible under some assumptions about physics that have nothing to do with mathematics.
Penrose did this argument better.[1] Penrose has been making that argument for thirty years, and it played better before AI started getting good. AI via LLMs has limitations, but they don't come from computability. [1] https://sortingsearching.com/2021/07/18/roger-penrose-ai-ske...
Thanks — and yes, Penrose’s argument is well known. But this isn’t that, as I’m not making a claim about consciousness or invoking quantum physics or microtubules (which, I agree, are highly speculative). The core of my argument is based on computability and information theory — not biology. Specifically: that algorithmic systems hit hard formal limits in decision contexts with irreducible complexity or semantic dive…
Earlier quoted context omitted.
We don't know the path for how a given input produces a given output, but that doesn't mean we don't know how LLMs work. We don't and can't know with certainty which specific atoms will fission in a nuclear reactor either. But we know how nuclear fission works.
We have the Navier–Stokes equations which fit on a matchbox, yet for the last 25 years there's been a US$1,000,000 prize on offer to the first person providing a solution for a specific statement of the problem: Prove or give a counter-example of the following statement: In three space dimensions and time, given an initial velocity field, there exists a vector velocity and a scalar pressure field, which are both smoo…
Earlier quoted context omitted.
The mathematical proof, as you describe it, sounds like the "No Free Lunch theorem". Humans also can't generalise to learning such things. As you note in 2.1, there is widespread disagreement on what "AGI" means. I note that you list several definitions which are essentially "is human equivalent". As humans can be reduced to physics, and physics can be expressed as a computer program, obviously any such definition ca…
1. I appreciate the comparison — but I’d argue this goes somewhat beyond the No Free Lunch theorem. NFL says: no optimizer performs best across all domains. But the core of this paper doesnt talk about performance variability, it’s about structural inaccessibility. Specifically, that some semanti spaces (e.g., heavy-tailed, frame-unstable, undecidable contexts) can’t be computed or resolved by any algorithmic policy…
"But here’s the peculiar thing: Humans navigate this question daily. Not always successfully, but they do respond. They don’t freeze. They don’t calculate forever. Even stranger: Ask a husband who’s successfully navigated this question how he did it, and he’ll likely say: ‘I don’t know… I just… knew what to say in that moment....What’s going on here? Why can a human produce an answer (however imperfect) while our sophisticated AI is trapped in an infinite loop of analysis?” ’"
LLM's don't freeze either. In your science example too, we already have LLMs that give you very good answers to technical questions, so on what grounds is this infinite cascading search based on?
I have no idea what you're saying here either: "Why can’t the AI make Einstein’s leap? Watch carefully: • In the AI’s symbol set Σ, time is defined as ‘what clocks measure-universally’ • To think ‘relative time,’ you first need a concept of time that says: • ‘flow of time varies when moving, although the clock ticks just the same as when not moving' • ‘Relative time’ is literally unspeakable in its language • "What if time is just another variable?", means: :" What if time is not time?"
"AI’s symbol set Σ, time is defined as ‘what clocks measure-universally", it is? I don't think this is accurate of LLM's even, let alone any hypothetical AGI. Moreover LLM's clearly understand what "relative" means, so why would they not understand "relative time?".
In my hypothetical AGI, "time" would mean something like "When I observe something, and then things happens in between, and then I observe it again", and relative time would mean something like "How I measure how many things happen in between two things, is different from how you measure how many things happen between two things"
This paper presents a theoretical proof that AGI systems will structurally collapse under certain semantic conditions — not due to lack of compute, but because of how entropy behaves in heavy-tailed decision spaces. The idea is called IOpenER: Information Opens, Entropy Rises. It builds on Shannon’s information theory to show that in specific problem classes (those with α ≤ 1), adding information doesn’t reduce uncer…
AGI as commonly defined
However I don’t see where you go on to give a formalization of “AGI” or what the common definition is.
can you do that in a mathematically rigorous way such that it’s a testable hypothesis?
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Thanks for this - Looking forward to reading the full paper. That said, the most obvious objection that comes to mind about the title is that … well, I feel that I’m generally intelligent, and therefore general intelligence of some sort is clearly not impossible. Can you give a short précis as to how you are distinguishing humans and the “A” in artificial?
Sure I can (and thanks for writing) Well, given the specific way you asked that question I confirm your self assertion - and am quite certain that your level of Artificiality converges to zero, which would make you a GI without A... - You stated to "feel" generally intelligent (A's don't feel and don't have an "I" that can feel) - Your nuanced, subtly ironic and self referential way of formulating clearly suggests th…
Earlier quoted context omitted.
The mathematical proof, as you describe it, sounds like the "No Free Lunch theorem". Humans also can't generalise to learning such things. As you note in 2.1, there is widespread disagreement on what "AGI" means. I note that you list several definitions which are essentially "is human equivalent". As humans can be reduced to physics, and physics can be expressed as a computer program, obviously any such definition ca…
> As humans can be reduced to physics, and physics can be expressed as a computer program This is an assumption that many physicists disagree with. Roger Penrose, for example.