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Perception is mostly about expectations. You can only perceive what you expect. Even a newborn makes fundamental assumptions about the structure of its sensorimotor space (priors), without which it simply couldn't start making sense of (& learning from) its sensorimotor feed.
Learning, both at the individual level and at the collective level (e.g. science) is largely about expecting something (formulating a hypothesis and running an experiment to test it) then checking whether reality validates or contradicts the expectation. To learn is to expect.
The machine learning angle here is that you can only learn within your hypothesis space (search space), which can never contain "everything", if only because you have to encode it (describe it) somehow. To learn from data, you have to make assumptions about it.
The stronger the assumptions the faster you learn (e.g. densely connected network vs. convnets for spatial perception).
You can only perceive what you expect? That pretty much kills the idea of learning right?
Maybe I would put it differently, a Learning opportunity is the difference between reality & expectation, expectation could be described as your model for prediction or reality aka sensor input, Learning is that bit that shrinks the difference
Can this idea of prior be unraveled back until a state at which no prior existed? So any action in that state is the genesis of “priors” creation? Yet such reasoning seems too meta to even allow in a logical discussion (sigh)!
Bayesian brain
Where does it get the priors from?
Are you deliberately avoiding mention of Friston or just trying to make a general point without going into the whole definition of "free energy"?
So ... we should start introducing Husserl to ML Engineers? (I really hope so!)
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