[ben_w]

I mildly disagree (although your final conclusion is correct: it indeed does nothing for science).

The deepest fundamental structures in the brain[0] are quantum fields, which are also the deepest fundamental structures in everything else.

There is no known quantum field of "soul" or "intelligence".

The right abstraction is higher, and could still be a whole lot of things; but as maths can be implemented in logic, which can be implemented in electronics or clockwork or hydraulics, it doesn't matter what analogy is used — and my mild disagreement here is that such inspiration has been useful and gotten us this far.

[0] that we know of

[mjburgess]

The process of evolution acts on organic systems, it doesn't act on quantum fields.

I appreciate there's some (imv strange) sense of 'intelligence' where 'finding the right puzzle piece' counts. I cannot fathom why we care about such a notion, and it seems to have almost nothing to do with what we do care about re 'intelligence'.

We care about that thing animals do, that thing which some do better than others. That thing which evolution brought about for (rapid) adaptive fitness to one's environment.

'Everything else is stamp collecting'

We already have a perfectly good understanding of puzzles and their solutions -- animals are their inventors

Intelligence isnt in the solution to a puzzle it's in its design, and especially, in what one does when one cannot solve it -- ie., how one adapts

The csci view of 'intelligence' is an act of self-aggrandising, it turns out to be: csci!

This is none-sense.

[ben_w]

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.

[mjburgess]

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

[ben_w]

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.

[mjburgess]

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

[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.