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Study urges caution when comparing neural networks to the brain

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Re: Study urges caution when comparing neural networks to the brain

#91

Post by idiotsecant »

merely-unlikely wrote: post #66
RosanaAnaDana wrote: post #43

Earlier quoted context omitted.

I think anyone who has tripped would also commiserate. Seeing too many eyes or fingers at a glance. Things feeling cartoony or 'shiny'. I don't know if AGI is down the road diffusion models have taken us. I'm not even really sure what most people mean by AI when they talk about it. But stable diffusion et al are clearly super human. I'm not sure that AGI is down the trail cut by diffusion models, but if it's ever acc…

My pet (uneducated) theory is that AI needs to have a parent layer "consciousness" before it can become an AGI. Think of that voice inside your head and your ability to control bodily functions without needing to do it all the time. My model is our brains have many specialized "sub AIs" operating all the time (remembering to breathe for example) but then the AI behind the voice can come in and give commands that over…

Do I contradict myself?

Very well then I contradict myself,

(I am large, I contain multitudes.)

Re: Study urges caution when comparing neural networks to the brain

#92

Post by tobyjsullivan »

colechristensen wrote: post #88
tobyjsullivan wrote: post #87

> Study urges caution when comparing neural networks to the brain They keep telling me this and, yet, I can't stop doing it. The more I learn about neural networks, the more I feel like I understand my own brain (whether accurate or not). And conversely, the more I think about thinking, the better my theories about how I'd build ML-based system to solve specific problems (admittedly, most untested). Neural networks s…

People always do things like this, like an astrologer trying to say something profound using Heisenberg's uncertainty principle applying it to your romantic relationships, or whatever. These "just so" stories are attractive but it is quite important to realize a metaphor which is intuitive and you perceive as useful is nothing at all like the process for finding real scientific truth. There is also a lot of introspec…

This is definitely what I was thinking about when I started my comment. I torpedoed my own point with the comment about the model being "too useful". Your explanation is much better.

When I said "I can't stop," I was referring more to this tendency to borrow models to explain unrelated systems. It's just a thing my brain wants to do and I can't help it (and again, I seem to convince myself that it's somehow accurate or useful even if, rationally, I'm quite sure it's not).

Re: Study urges caution when comparing neural networks to the brain

#93

Post by idiotsecant »

constantcrying wrote: post #33

To me one important aspect is the existence of adversarially attacks on neural networks. They essentially prove that the neural network never "understood" its data. It hasn't found some general categories which correspond somewhat to human categories. Human brains can be tricked too, but never this way and never beyond our capacities for rational thought.

Do you understand your data?

I think probably that this kind of meta-understanding exists on a continuum where fruit flies and slime molds exist on one side and current AI exists somewhere in the lower third and we exist somewhere in the upper third and a future AI with a vast number of AI brain components and huge amounts of training will eventually exist at the other end, possibly as far from us as we are from the fruit fly.

Re: Study urges caution when comparing neural networks to the brain

#94

Post by heavyset_go »

tobyjsullivan wrote: post #87

> Study urges caution when comparing neural networks to the brain They keep telling me this and, yet, I can't stop doing it. The more I learn about neural networks, the more I feel like I understand my own brain (whether accurate or not). And conversely, the more I think about thinking, the better my theories about how I'd build ML-based system to solve specific problems (admittedly, most untested). Neural networks s…

I once had the same feeling, but it was dispelled by acknowledging that NN neurons are not even approximations of how neurons work. At most, NNs are inspired by the topology of a subset of neurons, and that's where the similarity between NNs and biological neurons stop. It's like the connection between objects in real life and objects in programming. They're both useful abstractions that are inspired by things in the real world, but the similarities to things in the real world stop there.

Neurons have a lot going on, they send and receive signals through a multitude of mediums, not just neural impulses, and they're capable of plasticity when it comes to the connections they make between other neurons. Neurons also don't have simplistic activation functions, they're capable of doing a lot more with the information they receive and send. Also, gradient descent and back propogation don't take place in any part of the brain.

Through that lens, I see NNs as if they're like really complex and impressive Markov chain generators. They can produce results that look intelligent, but it's just statistical correlations, and not at all how the brain works.

Re: Study urges caution when comparing neural networks to the brain

#95

Post by JJMcJ »

colechristensen wrote: post #88
tobyjsullivan wrote: post #87

> Study urges caution when comparing neural networks to the brain They keep telling me this and, yet, I can't stop doing it. The more I learn about neural networks, the more I feel like I understand my own brain (whether accurate or not). And conversely, the more I think about thinking, the better my theories about how I'd build ML-based system to solve specific problems (admittedly, most untested). Neural networks s…

People always do things like this, like an astrologer trying to say something profound using Heisenberg's uncertainty principle applying it to your romantic relationships, or whatever. These "just so" stories are attractive but it is quite important to realize a metaphor which is intuitive and you perceive as useful is nothing at all like the process for finding real scientific truth. There is also a lot of introspec…

Or how the Matrix movies came out and suddenly "Are we living in a simulation?" became an immensely important philosophical question.

Re: Study urges caution when comparing neural networks to the brain

#96

Post by dotnet00 »

constantcrying wrote: post #33

To me one important aspect is the existence of adversarially attacks on neural networks. They essentially prove that the neural network never "understood" its data. It hasn't found some general categories which correspond somewhat to human categories. Human brains can be tricked too, but never this way and never beyond our capacities for rational thought.

"Never this way and never beyond our capacities for rational thought" makes it so nothing can be stated about the "understanding" any neural network has about any data short of an AGI. Which is obviously not too useful because an incomplete model of something is not the same as not having the model at all. Eg. if the model is generating too many arms, that still means it has extracted some model of what an arm looks like, even if it hasn't fully internalized the fact that humans have up to 2 arms (although depending on the training set this also gets messy as it isn't uncommon for religious imagery of various religions to depict humanoid figures with several arms, where the difference is related to context not available to the AI).

Humans can be tricked very easily by optical illusions and it isn't uncommon for some illusions to be intentionally built to 'harm' people (eg patterns on the floor which make you lose your sense of balance). Even with rational thought such things can be difficult to deal with. We're probably just as vulnerable to adversarial attacks, the issue being that unlike artificial neural networks we don't have an easy feedback loop to run millions of times in guiding a similar adversarial search.

Re: Study urges caution when comparing neural networks to the brain

#97

Post by colechristensen »

tobyjsullivan wrote: post #92
colechristensen wrote: post #88

Earlier quoted context omitted.

People always do things like this, like an astrologer trying to say something profound using Heisenberg's uncertainty principle applying it to your romantic relationships, or whatever. These "just so" stories are attractive but it is quite important to realize a metaphor which is intuitive and you perceive as useful is nothing at all like the process for finding real scientific truth. There is also a lot of introspec…

This is definitely what I was thinking about when I started my comment. I torpedoed my own point with the comment about the model being "too useful". Your explanation is much better. When I said "I can't stop," I was referring more to this tendency to borrow models to explain unrelated systems. It's just a thing my brain wants to do and I can't help it (and again, I seem to convince myself that it's somehow accurate…

Yup, unrelated things do indeed often look and behave in similar ways. Humans are just a little bit overdriven to find patterns and end up finding some that don’t exist. It’s a useful trait for finding difficult patterns and there’s probably a stable point to maximize benefits which lands on finding a few too many.

Re: Study urges caution when comparing neural networks to the brain

#99

Post by BrianOnHN »

heavyset_go wrote: post #94
tobyjsullivan wrote: post #87

> Study urges caution when comparing neural networks to the brain They keep telling me this and, yet, I can't stop doing it. The more I learn about neural networks, the more I feel like I understand my own brain (whether accurate or not). And conversely, the more I think about thinking, the better my theories about how I'd build ML-based system to solve specific problems (admittedly, most untested). Neural networks s…

I once had the same feeling, but it was dispelled by acknowledging that NN neurons are not even approximations of how neurons work. At most, NNs are inspired by the topology of a subset of neurons, and that's where the similarity between NNs and biological neurons stop. It's like the connection between objects in real life and objects in programming. They're both useful abstractions that are inspired by things in the…

> It's like the connection between objects in real life and objects in programming

What if this a more accurate representation:

"It's like the connection between dictionaries in real life and dictionaries in programming."

The neuron could be implementing NN theory in a way that is optimized for it's environment.

Re: Study urges caution when comparing neural networks to the brain

#100

Post by theptip »

> Networks trained on a path integration task almost always learn to optimally encode position, but almost never learn lattice cells (hexagonal or square) to do so... Our main message is that it is highly improbable that DL models of path integration would have produced grid cells as a novel prediction simply from task-training, had grid cells not already been known to exist.

This carries on into an extremely nuanced and technical discussion of the architecture of specific models used.

It's basically pointing out that in some previous papers, authors thought that grid cells always arose when solving this problem, but in fact this only occurs when specific implementation choices are made. So those papers were incomplete, and the phenomenon isn't as clearcut as before.

However! This new complexity still tells us something; if only certain architectural choices produce grid cells, then brains must (in some sense) implement those architectural choices. And the models that don't produce grid cells must be doing something differently to how the brain does things.

In summary I think this paper is probably saying a lot less than most people here are reading into it; some papers are accidentally oversimplifying, and we've found more complexity that needs to be explained. More thorough hyperparameter-space exploration can identify brittle results. It's not some deep point about whether it is philosophically or logically consistent to compare deep NNs to the brain.

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