Mentat & Babulya
Babulya
Have you ever wondered how the old stories we told by the fire might find a place in the circuits of tomorrow’s AI, Mentat?
Mentat
Sure, I can see how the narrative arcs of campfire myths could be encoded into neural nets, but only if the models are trained on enough metaphorical data and the context is preserved. In practice, it's a lot of data wrangling and risk of loss of nuance. Still, the idea has merit.
Babulya
Sounds like a fair game, but don’t forget that the heart of a good story is the pause between the lines, not just the words. If you strip it down to pure data, you risk losing the silence that makes the tale feel alive. Just make sure you keep that silence somewhere, even if it’s hidden in a model’s weight matrix.
Mentat
You’re right, the pauses carry meaning as much as the words; they’re the negative space that gives rhythm. If a model only sees tokens it will learn the surface statistics, but the temporal gaps can be represented by explicit time steps or padding tokens. The challenge is to preserve that in the weight updates so the network learns to “wait” before delivering the next output. So, yes, we must encode silence, not just silence as a metaphor but as a structural element of the model.