AGI is Mathematically Impossible 2: When Entropy Returns

Post by _0ffh » Mon, Jun 23, 2025, 6:55 AM UTC

ICBTheory wrote: post #68

vessenes wrote: post #32

Earlier quoted context omitted.

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…

Post by haneul » Mon, Jun 23, 2025, 6:56 AM UTC

vidarh wrote: post #173

ICBTheory wrote: post #2

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…

Unless you can prove that humans exceed the Turing computable, the headline is nonsense unless you can also show that the Church-Turing thesis isn't true. Since you don't even appear to have dealt with this, there is no reason to consider the rest of the paper.

Post by int_19h » Mon, Jun 23, 2025, 6:59 AM UTC

ICBTheory wrote: post #76

WhitneyLand wrote: post #48

Earlier quoted context omitted.

“This paper presents a theoretical proof that AGI systems will structurally collapse under certain semantic conditions…” No it doesn’t. Shannon entropy measures statistical uncertainty in data. It says nothing about whether an agent can invent new conceptual frames. Equating “frame changes” with rising entropy is a metaphor, not a theorem, so it doesn’t even make sense as a mathematical proof. This is philosophical m…

Correct: Shannon entropy originally measures statistical uncertainty over a fixed symbol space. When the system is fed additional information/data, then entropy goes down, uncertainty falls. This is always true in situations where the possible outcomes are a) sufficiently limited and b)unequally distributed. In such cases, with enough input, the system can collapse the uncertainty function within a finite number of s…

Post by Dylan16807 » Mon, Jun 23, 2025, 7:08 AM UTC

warpmellow wrote: post #175

cainxinth wrote: post #90

The crux here is the definition of AGI. The author seems to say that only an endgame, perfect information processing system is AGI. But that definition is too strict because we might develop something that is very far from perfect but which still feels enough like AGI to call it that.

Thats like calling a cupboard a fridge cuz you can keep food in it. The paper clearly sets out to try and prove that the ideal definition of AGI is practically impossible.

Post by harry8 » Mon, Jun 23, 2025, 7:14 AM UTC

rdescartes wrote: post #179

From that paper: There exists a class of questions in life that appear remarkably simple in structure and yet contain infinite complexity in their resolution space. Consider the familiar or even archetypal inquiry: "Darling, please be honest: have I gained weight?"