In practical terms, the result doesn’t matter. The race to approximate the human thought process and call it AGI (which is what matters economically) is on. If you can approximate it meaningfully faster that the real brain works in meatspace, you are winning. What it will mean for humanity or civilization is an open question.

Quantum entanglement?:

https://www.popularmechanics.com/science/a65368553/quantum-e...

And we’re not mathematically impossible, unless that’s some new philosophical theory: “If human intelligence is mathematically impossible, and yet it exists, then mathematics is fallible, and by inductive reasoning logic is fallible, and I can prove things with inductive reasoning, because piss off.”

https://en.wikipedia.org/wiki/G%C3%B6del%27s_incompleteness_...

" The first incompleteness theorem states that no consistent system of axioms whose theorems can be listed by an effective procedure (i.e. an algorithm) is capable of proving all truths about the arithmetic of natural numbers. For any such consistent formal system, there will always be statements about natural numbers that are true, but that are unprovable within the system.

The second incompleteness theorem, an extension of the first, shows that the system cannot demonstrate its own consistency. "

:3

Exactly. There's this "thing" you see in certain circles, where people (intentionally?) mis-interpret the "G" in AGI as meaning "the most general possible intelligence". But that's not the reality. AGI has pretty much always been taken to mean "AI that is approximately human level". Going beyond that is getting into the realm of Artificial Super Intelligence, or Universal Artificial Intelligence.

The problem I see isn't that AGI isn't possible, that's not even surprising. The problem is the term "AGI" caught on when people really meant "AHI" or artificial _human_ intelligence, which is fundamentally distinct from AGI.

AGI is difficult to define and quite likely impossible to implement. AHI is obviously implementable but I'm unaware of any serious public research that has made significant progress towards this goal. LLMs, SSMs or any other trainable artificial systems are not oriented towards AHI and, in my opinion, are highly unlikely achieve this goal.

Since when is mathematics based on the laws of our physical universe? last time I checked, it's an abstract system with no material reality.

Or.. "After Johnny read the paper humanity disappeared in a puff of logic"

Using your analogy, what this means is, we have to make humans to make human like intelligence. Not that we can make human like intelligence out side of humans.

>>Given that, there's no particular reason to think that "what the brain does" OR a reasonably close approximation, can't be done on another "system based on the laws of our physical universe."

What exactly does the brain do? Part of the problems with this is language itself might be insufficient to describe intelligence. And Language might be working a level below our thought. There are occasions where even the best of us fail to come up with how we think. We can go close and its not enough. A picture is better than a thousand words - why? Perhaps language is enough to display signs of intelligence but can't entirely contain or describe it.

Similarly, even in the case of LLMs we have seen showing spatial intelligence is whole lot different than predicting text.

Heck intelligence might not even be one monolithic thing. It could be a collection of several intelligences. And this whole idea of one grand AGI monolith could be wrong.

This poster didn't understand this response last time this was raised: https://news.ycombinator.com/item?id=44349818

AGI is clearly possible, because our brains are fundamentally machines, and there’s no reason in principle why we couldn’t build something similar. Right now we don’t - as human beings - have the ability to do that, but it clearly isn’t impossible since cellular machinery is able to build it in the first place.

This is the classical foundational syllogism of computationalism. In short:

   1.The brain obeys the laws of physics.
   2.The laws of physics are (in principle) computable.
   3.Therefore, the brain is computable.
   4.Therefore, human-level general intelligence is computable, and AGI is  
     inevitable and a question of time, power and compute.

The flaw is in the logic — it’s nothing less than a category error. The logic breaks exactly where category boundaries are crossed without checking if the concept still applies. That by no means inference, this is mere wishful thinking in formalwear. It happens when you confuse simulating a system with being the system. It’s in the jump from simulation to instantiation.

Yes, we can simulate water. -> No, the simulation isn’t wet.

Yes, I can “simulate” a fridge. ->But if I put a beer in myself, and the beer doesn’t come out cold after some time,then what we’ve built is a metaphor with a user interface, not a cognitive peer.

And yes: we can simulate Einstein discovering special relativity. -> But only after he’s already done it. We can tokenize the insight, replay the math, even predict the citation graph. But that’s not general intelligence, that’s a historical reenactment, starring a transformer with a good memory.

Einstein didn’t run inference over a well-formed symbol set. He changed the set, reframed the problem from within the ambiguity. And that is not algorithmic recursion, is it? Nope… That’s cognition at the edge of structure.

If your model can only simulate the answer after history has solved it, then congratulations: you’ve built a cognitive historian, not a general intelligence.

Yes, the paper is also philosophical. But not in the hand-wavy, incense-burning sense that’s being implied. It makes a formal claim, in the tradition of Gödel, Rice, and Chaitin: Certain classes of problems are structurally undecidable by any algorithmic system.

You don’t need empirical falsification to verify this. You need mathematical framing. Period.

Just as the halting problem isn’t “testable” but still defines what computers can and can’t do, the Infinite Choice Barrier defines what intelligent systems cannot infer within finite symbolic closure.

These are not performance limitations. They are limits of principle.

Sure. I redefined AGI. By using… …the definition from OpenAI, DeepMind, Anthropic, IBM, Goertzel, and Hutter.

So unless those are now fringe newsletters, the definition stands:

- A general-purpose system that autonomously solves a wide range of human-level problems, with competence equivalent to or greater than human performance -

If that’s the target, the contradiction is structural: No symbolic system can operate stably in the kinds of semantic drift, ambiguity, or frame collapse that general intelligence actually requires. So if you think I smuggled in a trap, check your own luggage because the industry packed it for me.

Well … not quite. The No Free Lunch theorem says no optimizer is universally better across all functions. That’s an averaging result.

But this paper is not at all about average-case optimization. It’s about specific classes of problems—social ambiguity, paradigm shifts, semantic recursion—where: a)The tail exponent alpha is = or < 1 —>no mean exists, b) Kolmogorov complexity is incompressible, and c) the symbol space lacks the needed abstraction

In these spaces, learning collapses not due to lack of training, but due to structural divergence. Entropy grows with depth. More data doesn’t help. It makes it worse.

That is what “IOpenER” means: Information Opens, Entropy Rises.

It is NOT a theorem about COST… rather a structure about meaning. What exactly is so hard to understand about this?

It’s a fair concern.Chaitin does get thrown around too easily — usually in discussions that don’t need him.

But that’s not what’s happening here.

– Kolmogorov shows that most strings are incompressible. – Chaitin shows that even if you find the simplest representation, you can’t prove it’s minimal. – So any system that “discovers” a concept has no way of knowing it’s found something reusable.

That’s the issue. Without confirmation, generalization turns into guesswork. And in high-K environments — open-ended, unstable ones — that guesswork becomes noise. No poetic metaphor about the mystery of meaning here. It’s a formal point about the limits of abstraction recognition under complexity.

So no, it’s not a misuse. It’s just the part of the theory that gets quietly ignored because it doesn’t deliver the outcome people are hoping for.

No.

This isn’t about GPT-4, or Claude, or whatever model’s in vogue this quarter. Neither is it about architecture. It’s about what no symbolic system can do—ever.

If your system is: a) Finite b)Bounded by symbols C) Built on recursive closure

…it breaks down where things get fuzzy: where context drifts, where the problem keeps changing, where you have to act before you even know what the frame is.

That’s not a tuning issue, that IS the boundary. (And we’re already seeing it.)

In The Illusion of Reasoning (Shojaee et al., 2025, Apple), they found that as task complexity rises: - LLMs try less - Answers get shorter, shallower - Recursive tasks—like the Tower of Hanoi—just fall apart - etc

That’s IOpenER in the wild:Information Opens. Entropy Rises. The theory predicts the divergence, and the models are confirming it—one hallucination at a time.

Yes. Humans are finite. But we’re not symbol-bound, and we don’t wait for the frame to stabilize before we act.We move while the structure is still breaking, speak while meaning is still assembling, and decide before we understand—then change what we were deciding halfway through.

NOT because we’re magic. Simply because we’re not built like your architecture (and if you think everything outside your architecture is magic, well…)

If your system needs everything cleanly defined, fully mapped, and symbolically closed before it can take a step, and mine doesn’t— then no, they’re not the same kind of thing.

Maybe this isn’t about scaling up? … Well, it isn’t It’s about the fact that you can’t emulate improvisation with a bigger spreadsheet. We don’t generalize because we have all the data. We generalize because we tolerate not knowing—and still move.

But hey, sure, keep training. Maybe frame-jumping will spontaneously emerge around parameter 900 billion.

Let me know how that goes