[Mattasher]

Humans have a long history of comparing ourselves, and the universe, to our latest technological advancement. We used to be glorified clocks (as was the universe), then we were automatons, then computers, then NPC's, and now AI's (in particular LLM's).

Which BTW I don't think is a completely absurd comparison, see https://mattasher.substack.com/p/ais-killer-app

[Tallain]

Not just technological advancements; we have a history of comparing ourselves to that which surrounds us, is relatively ubiquitous, and easily comprehended by others when using the metaphor. Today it's this steady march of technological advancement, but read any older work of philosophy and you will see our selves (particularly, our minods) compared to monarchs, cities, aqueducts.[1]

I point this out because I think the idea of comparing ourselves to recent tech is more about using the technology as a metaphor for self, and it's worth incorporating the other ways we have done so historically for context.

[1]: https://online.ucpress.edu/SLA/article/2/4/542/83344/The-Bra...

[MichaelZuo]

Each successive comparison is likely getting closer and closer to the truth.

[beezlebroxxxxxx]

Or each successive comparison is just compounding and reiterating the same underlying assumption (and potentially the same mistake) whether it's true or not.

[bigDinosaur]

The jump to 'information processing machines' seems far more correct than anything that came before, I'm curious how you would argue against that? Yes, there are more modern and other interesting theories (e.g. predictive coding) but they seem much closer to cognitive psychology than say, the human brain working like a clock.

[jtr1]

I think the argument is that you need to ask how you are measuring when you say it seems more correct than anything that came before. You may just be describing the experience of swimming in the dominant paradigm

[naasking]

Have you managed any kind of conversation with a clock before? Because you can actually have an intelligent conversation with an LLM. I think that's a pretty compelling case that it's not just swimming in the dominant paradigm.

[Planktonne]

People thought they were having intelligent conversations with Eliza; people even have satisfying emotional conversations with teddy bears.

It's not a good measurement.

[schoen]

One attempt could be "it allows us to make better predictions about the mind".

This article mentions excitement about neural networks overgeneralizing verb inflections, which human language learners also do. If neural networks lead to the discovery of new examples of human cognitive or perceptual errors or illusions, or to the discovery of new effective methods for learning, teaching, or psychotherapy, that could count as evidence that they're a good model of our actual minds.

[dragonwriter]

> If neural networks lead to the discovery of new examples of human cognitive or perceptual errors or illusions,

How would they, except as tools for analyzing research rather than research models? They don't work like human brains, so while they might sometimes exhibit something that looks like similar behavior when viewed a certain way, other than already having observed the behavior in human beings, there’s no reason to expect something they do to reflect what human brains, and moreover there’s no reason to expect useful insights from the corresponding behavior, since there is no reason to expect that the behavior responds similarly outside of the conditions where it is observed in both systems, leaving all the insight on the brain to cone from the brain (or models that, unlike artificial neural nets, we know have structural and behavioral similarities with (some parts of) human brains that are useful.)

[dotforest]

If the article is talking about the neural network in McClelland and Rumelhart’s Parallel Distributed Processing, there’s actually a paper by Steven Pinker and some other linguists drilling into it and finding that it doesn’t model children’s language acquisition nearly as closely or as well as M&R think it does.

[cscurmudgeon]

> I'm curious how you would argue against that

https://en.wikipedia.org/wiki/Hard_problem_of_consciousness

https://philosophy.stackexchange.com/questions/77517/what-is...

[beepbooptheory]

Very curious to know what the telos of "truth" is here for you? A comparison is a comparison, it can get no more "true." If you want to say: the terms of the comparisons seem to verge towards identity, then you aren't really talking about the same thing anymore. Further, you would need to assert that our conceptions of ourselves have remained static throughout the whole ordeal (pretty tough to defend), and you would also need to put forward a pretty crude idea of technological determinism (extremely tough to defend).

Its way more productive and way less woo-woo to understand that humans have a certain tendency towards comparison, and we tend to create things that reflect our current values and conceptions of ourselves. And that "technological progress" is not a straight line, but a labyrinthine route that traces societal conceptions and priorities.

The desire for the llm to be like us is probably more realistically our desire to be like the llm!

[PaulDavisThe1st]

An apple is like an orange. Both are round fruits, containing visible seeds, and relatively sweet. If you're hungry, they are both good choices.

But then again, an apple is nothing like an orange, particularly if you want to make an apple pie.

The purpose of a comparison is important in helping to define its scope.

[cmrdporcupine]

Step A: build a machine which reflects a reduced and simplified model of how some part of a human works

Step B: turn it on its head "the human brain is nothing more than... <insert machine here.>"

It's a bit tautological.

The worry is that there's a Step C: Humans actually start to behave as simple as said machine.

[PaulDavisThe1st]

What machines have we built that reflect a reduced and simplified model of how some part of a human works (other than as a minor and generally invisible research projects) ?

[dragonwriter]

> What machines have we built that reflect a reduced and simplified model of how some part of a human works

A very large number, e.g., lots of implants and prosthetic devices for one fairly large class.

[zmgsabst]

Electronic are a simplified model of the brains used by computers:

They emulate the faculty, rather than biology.

[dekhn]

any chemical or large industrial plant built in the last 30 years

[PaulDavisThe1st]

I think most industrial chemists would likely disagree. But I guess YMMV.

[ChuckMcM]

I always enjoyed the stories of 'clock work' people (robots).

[ImHereToVote]

Except LLM's are built on neural networks. That are based on how neurons work. The first tech that actually copies aspects of us.

[TaylorAlexander]

sigh

Neural networks are not based on how neurons work. They do not copy aspects of us. They call them neural networks because they are sort of conceptually like networks of neurons in the brain but they’re so different as to make false the statement that they are based on neurons.

[Terr_]

*brandishes crutches*

"Behold! The Mechanical Leg! The first technology that actually copies aspects of our very selves! Think of what wonders of self-discovery it shall reveal!" :p

P.S.: "My god, it is stronger in compression rather than shear-stresses, how eerily similar to real legs! We're on to something here!"

[famouswaffles]

They are though. They quite literally are. Saying otherwise is like saying planes weren't based on how birds work when Wright brothers spent a lot of time in the 1800s studying birds.

Both Humans and GPT are neural networks. Who cares that GPT doesn't have feathers or flap its wings? That's not the question to care bout. We are interested in whether GPT flies. You can sigh to Kingdom come and nothing will change that.

We've developed numerous different learning algorithms that are biologically plausible, but they all kinda work like backpropagation but worse, so we stuck with backpropagation. We've made more complicated neurons that better resemble biological neurons, but it is faster and works better if you just add extra simple neurons, so we do that instead. Spiking neural networks have connection patterns more similar to what you see in the brain, but they learn slower and are tougher to work with than regular layered neural networks, so we use layered neural networks instead.

The Wright brothers probably experimented with gluing feathers onto their gliders, but eventually decided it wasn’t worth the effort. Because that's not what is important.

[ImHereToVote]

There are drones with feathers now however. The spring in feather flaps help conserve energy, but only in flapping wings obviously.

[robwwilliams]

If you study retinal synaptic circuitry you will not sigh so heavily and you will in fact see striking homologies with hardware neural networks, including feedback between layers and discretized (action potential) outputs via the optic nerve.

I recommend reading Synaptic Organization of the Brain or getting into if you are brave, the primary literature on retinal processing of visual input.

[TaylorAlexander]

Actually it’s funny my best friend is a neuroscientist and studies the retina and in particular the way different types of retinal cells respond to stimulus. I have watched her give presentations on her work and I do see that there are some similarities.

But it is nonetheless the case that “neural networks” are not called that because they are based on the way neurons work.

[ImHereToVote]

They literally are:

https://www.ibm.com/topics/neural-networks#:~:text=Their%20n...

[smokel]

The book "The Synaptic Organization of the Brain" appears to be from 2003. Is it still relevant, or is there perhaps a more recent book worth checking out?

[robwwilliams]

It is great even though older. The chapter by Sterling on retina is amazing. Yes there is an updated version by Gordon Shepherd and colleagues: Handbook of Brain Microcircuits, but I actually prefer the 2003 edition.

[__loam]

I will continue to sigh. The visual cortex is relatively simple and linear. You're not saying something that's as impressive as you think it is.

[l33t7332273]

I think the point of the example is that that is an important part of our brains that is relatively simple and linear and we’ve been able to mimic it.

[robwwilliams]

Anything but simple and anything but linear.

[martindbp]

Sigh... Everyone knows artificial neurons are not like biological neurons. The network is the important part, which really is analogous to the brain, while what came before (SVMs and random forests) are nothing like it.

[mecsred]

Sigh... Every man knows the mechanisms of the mind are yet unlike the cogs and pinions of clockwork. It remains the machinery, the relation of spring and escapement, that is most relevant. Hitherto in human history, I think, such structure has not been described.

[TeMPOraL]

If you build a neural network out of cogs and pinions, sure.

Comparing the brain to most complex machines in history wasn't a mistake, any more than refining laws of physics were. Successive approximations.

And we're no longer at the point where we're just comparing brain to most complex machines. We have information theory now. We figured out computation, in form independent of physical medium used. So we're really trying to determine the computational model behind the brain, and one of the ways to do it is to implement some computational models in whatever is most convenient (usually software running on silicon), and see if it's similar. Slowly but surely, we're mapping and matching computational aspects of the brain. LLMs are just one recent case where we got a spectacularly good match.

[TaylorAlexander]

> Everyone knows artificial neurons are not like biological neurons.

Not, apparently, the person I was replying to!

[ImHereToVote]

I'm him, and I didn't say that. ANNs didn't arise in a vacuum and they aren't called neural networks for the fun of it.

https://www.ibm.com/topics/neural-networks#:~:text=Their%20n....

[renewiltord]

Doesn't really matter to modern CS, but Rosenblatt's original perceptron paper is a good read on this. ANNs were specifically inspired by Natural NNs and there were many attempts to build ANNs using models of how the human brain works, specifically down to the neuron.

[dekhn]

I;m sure you know but one of the best ways to get neuro folks worked up is to say anything about neural networks being anything like neurons in brains.

(IMHO, Rosenblatt is an underappreciated genius; he had a working shallow computer vision hardware computer long before people even appreciated what an accomplishment that was. The hardware was fascinating- literally self-turning potentiometer knobs to update weights.

[renewiltord]

If I'm being honest, I do know they get annoyed by that stuff but I've never really understood why. It's a somewhat common pattern in Mathematics as an avenue for hypotheses to take an existing phenomenon, model some subset of its capabilities, use that to define a new class of behaviour, follow that through to conclusions, then use that to go back to seeing if those conclusions apply to the original phenomenon.

A theoretical such thing might be for us to look at, say, human arms and say "Well, this gripping thing is a cool piece of functionality. Let's build an artificial device that does this. But we don't have muscle contraction tech, so we'll put actuators in the gripping portion. All right, we've built an arm. It seems like if we place it in this position it minimizes mechanical wear when not in action and makes it unlikely for initial movement to create undesired results. I wonder if human arms+hands have the same behaviour. Ah, looks like not, but that would have been interesting if it were the case"

Essentially that's just the process of extracting substructure and then seeing if there is a homomorphism (smooshy type abuse here) between two structures as a way to detect yet hidden structure. Category theory is almost all this. I suppose the reason they find it annoying is that there are many mappings that are non-homomorphic and so these are the false cognates of concepts.

Still, I think the whole "An ANN is not a brain" thing is overdone. Of course not. A mechanical arm is not an arm, but they both have response curves, and one can consider a SLAM approach for the former and compare with the proprioceptive view of the latter. It just needs some squinting.

Anyway, considering your familiarity with R and his work, I think I'm not speaking to the uninitiated, but I thought it worth writing anyway.

[lupire]

It's an ego thing and a focus thing. They put so much effort into studying the biological details, that they resent people exteacting value without going to that effort, and feel offended at the implication that what they are studying is simple.

Ultimately it's largely down to misapprehension of the difference between emulating a neuton and simulating a neuron, and defensiveness about an approximate model.

[ImHereToVote]

Science history should be mandatory for undergrads. I didn't think what I said is controversial. This is established history. Sorry if it scares you.

[dragonwriter]

Neural networks aren't based on how biological neurons work, though they are, I think, based on an outdated and even when less outdated simplified model of how they might work.

[tsukurimashou]

I wish this myth would die

[ImHereToVote]

This is basic scientific history. You are simply uneducated, or scared of the implications.

https://cs.stanford.edu/people/eroberts/courses/soco/project....

Key word: "neurophysiologist"