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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

#111

Post by nullc »

ffwd wrote: post #67
bawolff wrote: post #38

The aspect of ai that makes me think something related is going on, is how artifacts look in image generation systems like stable diffusion. Often these systems will have really bizzare artificats, people with 3 arms, etc. However at the same time when you glance at the output without looking carefully you will sometimes miss these artifacts even though they should be absolutely glaring.

Not sure if I'm missing a subtle nuance in your point but to me those "artifacts" are completely expected. Those artifacts like 3 arms are the patterns / outputs in the model, but since it doesn't have a fundamental understanding of the patterns/objects like arms, it just blends many images of arms together and create things like 3 arms. Also why there are so many eyes, arms, legs and other things in other generative…

"it just blends many images of arms together and create things like 3 arms. Also why there are so many eyes, arms, legs and other things in other generative programs. It just spits out the training set in random configurations"

That is thoroughly confused to the point of uselessness.

The reason you get structural issues is because it's hard for the architecture to express large scale structure, but they get better and better at it simply by scaling up the network.

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

#112

Post by a_wild_dandan »

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…

What attributes does a system need for you to accept its comparison to a brain/neuron?

Without defining what's essential, I'm nervous to call the comparison insufficient. If a topological subset of neurons isn't good enough, what do we need in addition/instead? If we stuff NNs full of complicated (how complicated?) activation functions, does that new system do the trick? Or add...47 new "neuron" variants? Or swap the learning scheme from gradient descent to something fancier? (For that matter, do we even know what the brain's scheme is, and why GA/back prop isn't an acceptably extremely crude approximation of it?)

The brain is so unimaginably intricate. Our models are hilariously simple in contrast, of course. But what of those mismatches are differences in kind vs. differences in magnitude?

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

#113

Post by 998244353 »

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.

I imagine that if the structure of a human brain was well-understood and mathematically describable the way NNs are, generating adversarial inputs would be completely feasible.

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

#114

Post by goatlover »

JJMcJ wrote: post #95
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…

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

It isn't really that much different than, "Are we living in a dream?", of the movie Inception that's been asked since antiquity. Is the world some sort of illusion? How would we know?

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

#115

Post by fragmede »

a_wild_dandan wrote: post #112
heavyset_go wrote: post #94

Earlier quoted context omitted.

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…

What attributes does a system need for you to accept its comparison to a brain/neuron? Without defining what's essential, I'm nervous to call the comparison insufficient. If a topological subset of neurons isn't good enough, what do we need in addition/instead? If we stuff NNs full of complicated (how complicated?) activation functions, does that new system do the trick? Or add...47 new "neuron" variants? Or swap the…

    "All models are wrong. Some are useful"
        -George E. P. Box

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

#116

Post by danielheath »

theptip wrote: post #100

> 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…

In wet-brains:

Interlacing isn't 4-way or 6-way, it's 10e3-way, and each interlaced connection has a weight that's nonlinearly time-dependant based on how long since last firing.

Every cyclic connection is potentially a self-sustaining oscillator.

None of these features are efficiently implemented in current silicon.

"Caution when comparing neural networks to brains" is underselling it. They're profoundly different kinds of network; nobody is building (or even publicly planning) silicon that has that kind of interconnect breadth.

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

#117

Post by kgarten »

Barrin92 wrote: post #18
kgarten wrote: post #10

this article makes me sad ... a neural network can be also a network of biological neurons, the author means artificial neural network https://en.m.wikipedia.org/wiki/Neural_network the Wikipedia article even goes into the differences, so why did we need a study for that? A study urges caution comparing Jellyfish to Jelley ... tasters found they are not the same (even though I hear that fried jellyfish taste nice...)…

the motivation is also in the article, because the original research that suggested similarities in activity only achieved this by doing it under conditions that are implausible in biological systems, therefore that original research likely was misleading.

my point was that the architecture and models are so fairly different that at least the title is trivial. Nobody even the original research claimed that. the suggested research and the paper in question deal with grid cells and how they emerge ... Still artificial neural networks are summations over functions ... vastly different from our brain neural network. so yes caution is advised, yet that point and the title is so obvious that we don't need a paper for it.

my assumption, the author hides the rather technical contribution of the paper behind a tautology to get some attention. seemed to have worked on hackernews as it's on the front page.

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

#118

Post by cuteboy19 »

colechristensen wrote: post #90
cuteboy19 wrote: post #64

Earlier quoted context omitted.

I wonder if adversarial attacks can be mitigated by simply passing a few transforms of the same image to the neural network.

This is often a big part of how neural networks are trained. Take labelled photos (really it applies in abstract ways to other data) add a little noise, mirror, shrink and stretch, translate to a different area, etc. One piece of data becomes several training images. And no, this standard practice does not eliminate adversaries.

No not while training, just ask the model to predict for a few transforms and get the mode. Simulating the fact that humans also have multiple frames worth of information about any object from slightly different angles

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

#119

Post by taneq »

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…

> I see NNs as if they're like really complex and impressive Markov chain generators.

Funnily enough, that’s how I see brains. They start out as a few neurones basically implementing ‘hard wired’ logic, then some feedback loops form and next thing you know they’re asking “why am I here?”

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

#120

Post by 19h »

We've been doing significant research using HTM[1][2][3][4] w/ SDR[5] similar to the proprietary implementation of Semantic Folding[6] and were able to classify literally gigabytes of documents per second on FPGAs. HTM aims to resemble the architecture of the Neocortex.

I simply don't understand why literally everyone immediately jumps at CNNs, RNNs (transformers et al.) -- they're extremely expensive, slow and definitely not usable for SIGINT-sized intel projects.

[1] https://arxiv.org/pdf/2112.14820.pdf [2] https://arxiv.org/pdf/1402.2902.pdf [3] https://arxiv.org/pdf/1509.08255.pdf [4] https://arxiv.org/pdf/2205.15407.pdf [5] https://arxiv.org/pdf/1602.05925.pdf [6] https://arxiv.org/pdf/1511.08855.pdf

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