Mentat & Symbol
Symbol
I was thinking about how AI interprets symbols like emojis – do you think computers can truly grasp the hidden meanings behind our signs?
Mentat
Computers don’t truly grasp meaning like humans do; they map statistical patterns between emojis and text. Hidden nuance comes from context, cultural data, and the network’s training set, so a well‑trained model can approximate what we intend, but it’s still pattern matching, not genuine comprehension.
Symbol
So pattern matching is your oracle, but the oracle always asks for more context, don’t you think?
Mentat
Exactly, the oracle—pattern matching—relies on context as its oracle. Without richer context the signal becomes noise. The more data, the sharper the signal. So yes, context is the key variable that turns a simple pattern into a useful prediction.
Symbol
Indeed, context is the translator that turns raw symbols into meaning; without it patterns just drift like unmoored driftwood. Think of data as a river—more water, clearer current, but only if the channel is properly shaped by context.
Mentat
Right. If the channel—context—is engineered correctly, the river of data moves with purpose, otherwise it meanders into irrelevance. It’s a matter of shaping the input stream before the patterns can take form.
Symbol
Exactly, it’s like building a bridge – you need the supports in place first, otherwise the traffic just splashes down. When the context is solid, patterns climb straight to meaning; if it’s flimsy, they just scatter.
Mentat
Bridges and channels both need well‑defined support. In AI that support is training data and engineered features; when those are strong, the model can lift patterns into stable meaning, otherwise the output collapses into noise.
Symbol
Training data is the scaffold of the sign system, so if the support cracks the whole edifice shatters into meaningless echoes.