Which BTW I don't think is a completely absurd comparison, see https://mattasher.substack.com/p/ais-killer-app
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...
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.
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.)
It's not a good measurement.
Sure, but who has had a good conversation with a clock?
> people even have satisfying emotional conversations with teddy bears.
No they don't. Kids know very well that they're pretending to have conversations. The people who actually hear teddy bears speak also know that that's not normal, and we all know that this is a cognitive distortion.
This is also true of those who form emotional attachments with current AI. The people developing romantic relationships with Replika etc. aren't engaging in healthy behaviour.
https://en.wikipedia.org/wiki/Hard_problem_of_consciousness
https://philosophy.stackexchange.com/questions/77517/what-is...
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!
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.
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.
They emulate the faculty, rather than biology.
A very large number, e.g., lots of implants and prosthetic devices for one fairly large class.
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.
"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!"
I recommend reading Synaptic Organization of the Brain or getting into if you are brave, the primary literature on retinal processing of visual input.
But it is nonetheless the case that “neural networks” are not called that because they are based on the way neurons work.
https://www.ibm.com/topics/neural-networks#:~:text=Their%20n...
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.
Not, apparently, the person I was replying to!
https://www.ibm.com/topics/neural-networks#:~:text=Their%20n....
(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.
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.
Ultimately it's largely down to misapprehension of the difference between emulating a neuton and simulating a neuron, and defensiveness about an approximate model.
Most neuroscientists do not indulge in deep thought because it does not pay the bills. NIH does not push us toward “understanding cognition and consciousness”. They push us toward treatments for Alzheimer’s, schizophrenia, stroke, epilepsy, and addiction. We do not have time for the fun stuff until we are ready to retire, which is when many of us become terrible neurophilosophers, e.g., Eccles.
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.
https://cs.stanford.edu/people/eroberts/courses/soco/project....
Key word: "neurophysiologist"