I don't see the problem. Your learning algorithm doesn't have to be "very" complicated. It has to work. Machine learning models don't consist of million lines of code. I do see the problem where one might expect evolution not to be very good at doing that compression, but I find the argument that there would actually be lots of bits needed very unconvincing.
One claim I found very surprising:
...To make computationalism well-defined, we need to define what it means for a computation to be instantiated or not. Most of the philosophical arguments against computationalism attempt to render it trivial by showing that according to any reasonable definition, all computations are occurring everywhere at all times, or at least there are far more computations in any complex object than a computationalist wants to admit. I won't be reviewing those arguments here; I personally think they fail if we define computation caref
I am confused what the state space Φ is adding to your formalism and how it is supposed to solve the ontology identification problem. Based on what I understood, if I want to use this for inference, I have this prior ξ∈□c(Φ,Θ), and now I can use... (read more)