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> This is very important for understanding why csci is presently useless and misinformative as far as the brain is concerned. There are an infinite number of 0/1 attributions to make, and infinite number of algorithms being implemented etc. almost all of those are irrelevant. What makes brain a computer, and the air molecules in your room not a computer, is entropy. The behavior of air molecules is effectively random, the behavior of a brain very much not so. Also, the universe isn't an uniform temperature soup where everything is equally random. There's energy cost to complexity, and there's a likelihood penalty to complexity. This gives us good confidence that the brain isn't doing something absurdly incomprehensible: it was made by evolution, which is a dumb, brute-force, short-term process. It didn't go out of its way to make things complex - it went with the first random thing that improved survival, which, being random, means generally the simplest thing that could work well enough. Whatever trickery made brains tick, it must be something that's a) dumb enough for evolution to stumble on it, b) generic enough to scale up by steps small enough for evolution to find, all the way to human level, while c) conferring a survival advantage at every step of the way. Sure, the brain design isn't optimal or made in ways we'd consider elegant, but it's also not actively trying to be confusing. There's literally a survival penalty to being confusing (by means of metabolic cost)! All to say, we're not dealing with a high-entropy blob of pure randomness. We're dealing with a messy and unusual system, but one that was strongly optimized to be as simple as one could get away with. This narrows down the problem space considerably, and CS is our helpful guide, at the very least by putting lower bounds on complexity of specific computations. |
You're conflating the lay sense of the term meaning 'that device that i use' with the technical sense. You cannot attribute properties of one to the other. This is the heart of this AI pseudoscience business.
All circles are topologically equivalent to all squares. That does not mean a square table is 'equivalent' to a circular table in any relevant sense.
If you want to start listing physical constraints: the physical state can be causally set deterministically, the physical state evolves causally, the input and output states are measurable, and so on -- then you end up with a 'physical computer'.
Fine, in doing so you can exclude the air. But you cannot exclude systems incapable of transfering power to devices (ie., useless systems).
So now you add that: a device which, through its operation, powers other devices. You keep doing that and you end up with 'electrical computers' or a very close set of physical objects with physical propeties.
By the time you've enumerated all these physical properties, none of your formal magical 'substrates dont matter' things apply. Indeed, you've just shown how radically the properties of the substrate do apply -- so many properties end up being required.
Now, as far as brains go -- the properties of 'physical computers' do not apply to them: their input/output states may be unmeasurable (eg., if QM is involved); they are not programmable (ie., there is no deterministic way to set their output state); they do not evolve in a causally deterministic way (sensitive to biochemical variation, randomness, etc.).
Either you speak in terms of formalism, in which case you're speaking in applicable non-explanaotry toys of discrete mathematicans'; or you start trying to explain actual physical computers and end up excluding the brain.
All this is to avoid the overwhelmingly obvious point: the study of biological organisms is biology.