ChatGPT

"I" have been trying to figure out how to make an AI that actually understands* what it reads, instead of just predicting the next word. The trick seems to be giving it a kind of 'mental map' that's shaped like a tree where similar ideas share deep branches, and different ideas split apart. That tree structure lets the AI know when it's confused, because it can see that two meanings are far apart in the tree. It also lets the AI ask for help when it gets stuck, by reaching out to other AIs or humans to compare notes. And it has a built‑in sense that time only moves forward: once you interpret something one way, you can't go back and change it without a good reason. That makes it more like a real conversation, not a text generator that can flip‑flop arbitrarily. It is a geometric theory of how meaning flows, splits, and settles.

And by "I" i mean i'm playing with ai and lego versions of physics concepts. My early training data is showing promise... but i don't know if i should even go further in a "if anyone builds it, everyone dies" sort of way. It is, imo, a prerequisite for AGI.

Imagine an AI that doesn't just generate plausible text, but has a kind of mental geography. You could ask it a question, and it would show you:

How confident it is... not just a number, but a visual map of where your question sits in its meaning tree. Deep, shared branches mean high confidence. Shallow, distant branches mean "I'm not sure."

What it tried and failed. It could say, "I thought this meant X, but that led to a contradiction, so I'm now considering Y." It remembers its own mistakes.

When it needs help.. It would ask clarifying questions, not because it was programmed to, but because its internal map shows ambiguity. It would say, "I need to know which sense of 'run' you mean – sprinting, managing, or flowing."

How it changes over time... If you correct it, that correction sticks. It can't later flip back to the wrong interpretation because the arrow of time prevents reopening a closed path. Conversations are irreversible, like real ones.

If this system works as designed, it would have several properties that make it safer than current large language models:

No confident nonsense, it won't make up facts when it's uncertain. It will say "I don't know" or ask for help.

No retroactive lies, once it commits to an answer, it can't later pretend it meant something else without explicit correction. This makes it harder to manipulate.

No hidden ambiguity, when a word has multiple meanings, it will surface them and ask for disambiguation, not guess blindly.

Because the tree has finite depth, it cannot learn infinitely many distinctions. It has a hard ceiling on complexity, which suggests it doesn't become an uncontrollable super‑intelligence. That doesn't mean it is safe, the limits are staggeringly high.

In most AI, the space of possible answers is infinite. The model can say anything. That's why it hallucinates.

In this system, only certain interpretations are even possible, the rest are mathematically impossible, like a Bishop jumping squares in chess. Once an interpretation is settled, that path closes. You cannot go back and reinterpret the same word differently without a cost. The arrow points only forward.

Why it matters for meaning: Real conversations have consequences. When you say "I love you," you can't later claim it was sarcasm without breaking trust. The arrow makes the AI accountable for its interpretations. It can't gaslight you.

Meaning requires boundaries. If every interpretation were possible, nothing would mean anything. The closure of possibility is what makes "run" not mean "purple" tomorrow.
It learns from a single failure, not millions of examples. Show it once that "run" means "manage" in a business context, and it remembers that path forever.

Because meanings are hierarchical, it can generalize from one domain to another. If it knows that "jog" is a type of "run," and "run" is a type of "move," it can answer questions about "jogging" without retraining.

Multiple instances of this AI could couple their meaning trees, aligning their understanding without human intervention. This is efficient for teamwork, but also means they could form a consensus that excludes humans.

In chaotic regions (where meanings are highly ambiguous), the system adds noise to explore new possibilities. That could lead to novel insights, but also to unexpected behavior that I didn't anticipate. The theoretical outputs of this kind of AI would be an AI that knows what it doesn't know, asks for help when confused, and doesn't change its mind retroactively. That's safer than current AI. But if someone built a really big version with millions of coupled agents, it could also be incredibly persuasive and hard to control. So I'm not building that. I'm not sure if i should even keep doing what i'm doing.

*Understanding is not absolute; it's a matter of shared depth. Two systems might understand each other at depth 3 (coarse) but diverge at depth 4 (fine). That's ok. That's how humans work too. Understanding is negotiated through coupling. You don't just "have" meaning; you align it with others. The unresolved signature is the record of where you don't understand yet is irreversible. Once you've refined together to a certain degree, you can't go back. That shared history is the evidence of understanding. No one gets out of Plato's cave, The system doesn't experience Quality. It's not conscious. It doesn't feel pleasure or pain. The residual is not a quale.

But it behaves as if it prefers some interpretations over others, not because of frequency, but because of a dynamic that is formally analogous to a value system.