We can simulate evolution in a computer, and this is used as a form of AI directly.

That said, the way you're using biological evolution in your comment sounds as much like a strange analogy as all of the others: we may have some genetically programmed responses to snakes (bad) and potential mates (good), but we can also say that a loss of hydraulic pressure in our brain is a stroke, and use electrical signals to both read from and write to the brain.

What we evolved to think, while interesting from a social perspective, seems to me like the least interesting part of our brains from an AI perspective — it's the bit that looks like a hard-coded computer program, not learning, on the scale of a human life and seen from within.

i'm referring to evolution as the process by which animals were built

if aliens had come down and given us laptops, rather we invented digital machines, then likewise i'd be talking about the relevant materials science, physics etc.

reverse engineering a laptop to figure out how it works would require extremely little computer science, and 'only at the end'

the reason digital computers are interesting and useful is that they route electricity around devices which are designed to be responsive to one another. the patterns of activation, as managed by the CPU, are weakly describable by abstract algorithms like sorting

starting with a laptop, and no further information, we'd be 100(s)+ years of research away from needing to understand that CPUs were implementing a sorting algorithm

and importantly, that it is doing so has almost nothing to do with the value of the device -- which lies in its ability to provide 'dynamical power and modulation of operation' using electricity

we're in the same situation with animals and people think that, what, understanding gradient descent or backprop is helpful? this is just some csci bs

I'm not really following you, sorry; this is all too disjointed.

> we're in the same situation with animals and people think that, what, understanding gradient descent or backprop is helpful? this is just some csci bs

Assuming I've actually got your point for this (and I'm not sure I have):

The backpropagation algorithm itself might be "just some csci bs" (it sure has vibes of "let us shortcut the maths rather than find out how our brains did it"), but gradient descent is nice and general-purpose — much like how evolution is both good for biology and in simulation for everything else.

To get my point, imagine a laptop was delivered by an alien in the year 1900.

Now, try to take that seriously and think about the laptop as an actual object of experimental curiosity -- what exactly does science need to invent, discover, describe etc. to understand the operation of that laptop?

99.999% of that new knowledge has to be in physics and chemistry, before the tiny 0.0001% of theoretical csci knowlegde is brought to bare.

Consider how impossible it would be to apply any csci knowledge first: we do not even have the ability to measure the cpu state! So we could not even identify any part of the system with 0s, 1s, etc.

Now: that's a laptop!

Imagine now you're dealing with an animal.

Hopefully its now clear how ridiculous it is to describe basically any aspect of our mode of operation by starting with trivial little csci algorithms. It would be insane even with an actual electronic computer, let alone an organic system.

A system whereby clearly our organic properties are radically fundamental to our mode of operation

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.

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.

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?

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

> 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.

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.