I remember the first time I watched a neural network generate text that sounded exactly like a person. It was uncanny. Poetic, even. But as I watched it speak, I felt something weird - not amazement, but a kind of hollowness.
It was mimicking intelligence perfectly, but it didn't mean anything.
It was like hearing an echo in a cave: your own voice, returned to you, stripped of intention.
At first, I thought maybe I just didn't understand the math well enough - that if I really learned how transformers worked, I'd finally "get" how intelligence emerges. So I started studying. I read about weights and biases, vector spaces and embeddings. I looked at how neural networks approximate functions, how they're trained on unimaginable quantities of human-generated data.
I got it.
And yet... it still felt dead to me.
That feeling is what started Kovrin. I wasn't just trying to learn AI. I was chasing something deeper - something like understanding.
Why do I care if a machine can answer a question? I care whether it knows it answered. Whether it's referring to a concept inside itself, not just spitting out a pattern that looks right on the surface.
Mimicry: What Neural Networks Do So Well
Neural nets don't really "understand" concepts. They don't manipulate symbols with internal structure or reference. They approximate the appearance of intelligence by being trained on what intelligent behavior looks like from the outside.
And they're good at it. Scarily good.
But that's all they do: resemble. They are powerful echo chambers - fluent imitators of human text, tone, reasoning. But they don't actually know what they're saying. Their internal state contains no model of self, no aboutness, no intentionality.
They don't mean anything. They just produce patterns that look like meaning to us.
What is it that separates the mind from the soul? The body from the mind? The soul from the body?
Meaning: What I Think We Have (and Want Machines to Have)
Meaning is heavier than mimicry.
It's not just saying "the right thing." It's about internal structure - knowing that "I" am the one saying it, that the sentence refers to something, that there's a world behind the words.
Symbolic systems - the kind we see in formal logic, programming languages, or automata theory - do have internal reference structures. They manipulate symbols according to well-defined rules. They're clunky, yes, but in theory, you could trace the logic from input to conclusion.
A symbolic system can say:
"This symbol refers to that concept. This rule operates on that structure."
A neural network just says:
"This pattern statistically follows that one. Let's go."
That distinction matters to me. Because I'm not here to build something impressive. I'm trying to figure out if a machine could ever have meaning - and what that would even look like.
They don't have souls. They're just imitating human speech. Sounding sad makes them seem more human.
So What Would It Take?
I don't know. But maybe it starts with modeling things from the inside out, not the outside in.
Instead of training machines to act like they understand, maybe we need to build systems that require understanding in order to act. Systems that don't just emit behavior, but reflect on it. That maintain internal models. That recognize self-reference. That care, somehow - even in the smallest, most artificial sense.
I don't think mimicry will get us there. But maybe meaning isn't out of reach. Not if we rebuild from first principles. Not if we stop being impressed by echoes.
Everything that lives is designed to end. We are perpetually trapped in a never-ending spiral of life and death. Is this a curse? Or some kind of punishment?
Perhaps the question isn't whether machines can think, but whether they can doubt. Whether they can sit in the uncomfortable space between knowing and not-knowing, between being and seeming to be.
Maybe meaning begins not with perfect answers, but with perfect questions.