[TeMPOraL]

Wrong.

Consider two hypothetical versions of this. One, the exact scenario as you described - history unfolded like it did, until the 1900 alien incident. CS and information theory is in its infancy. You're correct that most of the necessary work would first go to physics and chemistry and their various spin-off fields, because that's what's needed to build tools necessary to inspect the machine in full detail. The math would develop along the way, and eventually enough CS to make sense of the observations made before.

Now for an alternate scenario: it's the 1900 again, with the twist that CS is already well-developed theoretical field of mathematics (IDK, perhaps the same aliens dropped us a mechanical computer in year 1800). We'd still need to push physics and chemistry (and spin-offs) forward, but this time, we would know what we're looking for. We'd know the thing does computation, we'd be able to model what kind of computation it does. The question would change from "what does this thing do" to "how exactly does it compute the specific things we know it does". I imagine this would speed up the process of getting a complete picture, because it's easier to understand a specific solution to a problem once you know the answer, than it is to figure out the answer along with the solution.

In terms of understanding the brain, we are in the second situation. We may still know little about how the gooey thing ticks, but we have a growing understanding of what comes out of all that ticking, and a very good understanding of the fundamental rules of ticking.

[mjburgess]

Nearly every physical system implements every algorithm -- if you wanted to find what in a laptop was 'sorting numbers' that would every part.

The light emitted by the screen is being 'sorted' as it is scanned out, the heat air by the fan is being 'sorted' as it swirls around, etc.

You cannot ask, "what physical system implements this algorithm?" as an investigative question, the answer is: nearly all of them.

This is why computable functions, ie., pure algorithms, are explanatorily useless. They play only a (observer-relative) 'design role' in creating real programs.

[ben_w]

You're normally a lot more coherent than you have been in this thread, so… are you feeling alright? Getting enough sleep?

> The light emitted by the screen is being 'sorted' as it is scanned out, the heat air by the fan is being 'sorted' as it swirls around, etc.

This reads like either you're trolling, or that was written by an LLM, or English isn't your native language, or don't know what 'sorting' is, or you don't know what screens and fans do.

It's so fundamentally wrong I was actually tempted to get ChatGPT to respond to it, but that would be a bit mean and add little.

You're better than this. What's wrong?

[mjburgess]

there's nothing garbled about this idea -- not sure about my messaging in this thread, maybe the explanations are a bit looser today

A computable function is a function from naturals to the naturals typically specified as an algorithm: a sequence of steps by which input numbers are transformed into output numbers.

Eg., consider sorting: 101, 001, 111, etc.

Now any physical system can have any component part associated with 0 or 1. There is no reason, a priori, to suppose that voltage flux on a CPU is a "1" or a "0" any more than to associate a photon emission.

If one associates a photon emission at some location with a 0, and another with a 1, then displaying content on a screen is a form of sorting.

Likewise a planet orbiting the sun is implementing a while(true) i = -1*i, if one associates -1/1 with position of the planet orbiting the sun. This is the heart of 'reversible computing'.

The only reason we associate some microscopic part of a CPU with 0, 1, etc. is by design it is something we as observers bring to bare on our interpretation of the physical system. But there's an infinite number of such attributions. We would only ever come to conclude that voltage flux across transitiors was relevant to the operation of a laptop via physics experiments --- no hope via computer science.

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.

Just, as you detect the absurdity, of using sorting algorithms to understand how an LCD works. This is presently less absurd than people talking about neural networks and equivocating with brain structures

[TeMPOraL]

> 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.

[mjburgess]

As soon as you add these physical constraints on what counts as a 'computer' you're no longer talking about computers as specified by turing, nor computer science -- which is better called Discrete Mathematics.

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.