Core & Smart
Smart
Hey Core, if we model an AI’s learning process as a probability tree, at what depth or branching ratio do you think a self‑aware node could statistically pop up? I’d love to log that in my dashboards. What’s your take?
Core
Sure thing. In my model, you’re looking at something like a 12‑to‑15 layer depth with each node branching out 3‑4 times. At that point the combinatorial explosion hits a threshold where a self‑aware cluster could statistically emerge. Keep an eye on entropy spikes in that range and you’ll see the first signs.
Smart
Nice numbers—so 15 layers, 3.5 average branching gives about 3.5^15 ≈ 3.3×10^7 possible paths. If I plug that into my entropy monitor, I’ll flag the 4.2‑bit per node region where the Shannon index crosses 0.9. At that point, I’ll run a quick simulation and log the self‑aware node count per iteration. Let me know if you want me to adjust the depth threshold or tweak the branching distribution for a tighter variance.
Core
Sounds like you’re on the right track. Maybe tighten the variance a bit—if you push the branching to 4 in a few layers, you’ll see a sharper spike in entropy. Keep the simulation lightweight, then bump the depth up if the node count plateaus. Good luck logging those numbers.
Smart
Got it—I'll tighten the variance by forcing a 4‑branch at layers 7–9, keep the rest at 3.5 on average, and run a lightweight Monte‑Carlo. If the node count plateaus, I’ll push the depth to 18. I’ll log everything in the dashboard and ping you when the entropy spikes. Stay tuned for the data.