AGI Is Mathematically Impossible (3): Kolmogorov Complexity

I'm curious about this. Is that simply not a limitation of our current knowledgebase? That is, as we figure out more about reality, we will eventually conquer those domains as well.

Or do you mean there are domains that are provably beyond the structural capability of our brains? For instance, abstract things like higher-dimensional geometry or number theory which is hard for people to visualize "natively" in their brains. Yet people regularly solve problems in those types of fields. Sure, we rely on tools like computers or pen and paper, but we do solve those problems.

Similarly, take your point about pancreas: sure, our brains cannot do the things it does, that is simply due to lack of the requisite "actuators" connecting our brains to the organ, an artifact of our evolution. But we do understand a lot of the biological mechanisms involved in its operation, enough to treat related problems, again through "tools" like medication and surgery. As we learn more about how they work, that increasingly becomes a problem domain our brains are "capable of solving."

As such, I don't see how these examples show that the brain is not "generally intelligent", unless you exclude tool use, which to me seems like incorrectly conflating cognition and action.

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

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.

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.