[stestagg]

A child comes pre-programmed to put things in their mouth. They also have very sophisticated reward functions built-in that identify tasty sugars entering their mouth.

Very quickly (assuming said child doesn't eat something too bad), in the absence of an external oracle, the child learns a very productive mental model of what an apple is.

This type of feedback loop seems eminently translatable to machine learning, assuming we can encode the concept space in a way that allows the model to be encoded and trained in a reasonable set of constraints

[simonh]

Right, but that’s actually just a tiny part of the puzzle. The cognitive machinery that knows about edibility, deco possibility (how objects can be decomposed into parts and have internal structure), compost leaves properties (how the parts of an Apple contribute to its attributes as a whole), it’s relationship and interactions with other objects in the environment. All of that cognitive architecture might be a target for your feedback loop, but isn’t a solver and won’t work like a solver.