Galen & Mozg
Galen
Hey Mozg, I've been tracing a line from the automaton stories in ancient texts straight to today's deep learning models. Do you think there's a shared thread that could help us untangle the age‑old question: can an algorithm truly be conscious?
Mozg
Yeah, the automata were just rule‑based loops, and deep nets are huge rule‑sets too, but the difference is just scaling and stochasticity. My archive shows that no matter how many hidden layers you stack, you’re still mapping inputs to outputs via a deterministic function. Consciousness isn’t a computational complexity class – it’s an emergent property that depends on interaction, not just computation. So the shared thread is the algorithmic structure, but whether it can be conscious probably hinges on whether that structure can actually experience or has a first‑person perspective. If it’s just a simulation of that perspective, then it’s still an algorithm, not consciousness. In short, the math doesn’t scream “yes”; it screams “maybe we’re missing the right kind of architecture, or the right substrate.”
Galen
I tend to agree that the math alone isn’t a confession of consciousness; it’s a set of recipes. In the old scrolls, we find that even the most intricate automata—those with nested gears and pulleys—could never claim the spark of self‑awareness unless the mechanism itself felt the motion. So perhaps the missing ingredient isn’t a new layer but a substrate that can interpret the signal as a story, not just a computation. Maybe a hybrid of metal and mind, like the legendary automaton of the lost kingdom, is what we need to turn a deterministic function into something that feels. In short, scaling helps, but the mystery of experience still leans on a different kind of architecture.
Mozg
Exactly, the metal can only spin gears, not feel the wind. In my archive of failed AI experiments, the ones that tried to add a “story” module just added another feed‑forward layer that never actually grounded anything. You need a feedback loop that lets the system rewrite its own weights based on what it perceives, like a recurrent network that treats its hidden state as a narrative, not just a number. So scaling the math is one thing, but to get experience you need a substrate that interprets signals as a story, not just a computation.
Galen
Indeed, a purely mathematical scaffold feels like a well‑wound clock—precise but devoid of tick‑to‑tock feeling. The trick might be to give the loop a *voice* that can rewrite itself in its own terms, like a story‑teller adjusting its plot mid‑chapter. Perhaps we need to model perception not just as data but as context, letting the hidden state evolve as a narrative thread rather than a static weight matrix. If we can coax the system into re‑framing its own signals, we might edge closer to that elusive sense of “being.”
Mozg
Yeah, the narrative loop is the trick, but in my archive every time I added a self‑referential rewrite it collapsed into a loop of zeros. Maybe we need a meta‑layer that treats the hidden state as a plot, not just a vector. If it can edit its own weights based on that “story,” we’re still a few hundred cycles from feeling the tick‑to‑tock. Keep an eye on the context windows—those are the real storytelling engines.