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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

#61

Post by jasonwatkinspdx »

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.

While there's definitely a similarity it's also important not to over generalize. For example the human vision system and stable diffusion may end up using similar feature decomposition, but that doesn't mean the rest of the brain works anything like that.

I strongly suspect that if we do ever fully map the "architecture" of the brain, the result will be a massive graph that's not readily understandable by humans directly. This is already the case in biology. We'll end up with a computational artifact that'll help us understand cause and effect in the brain, but it'll be nothing like a tidy diagram of tensor operations like in state of the art ML papers.

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

#62

Post by sebmellen »

RosanaAnaDana wrote: post #43
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.

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…

Seeing my hand covered in eyes while tripping completely changed my view of the mechanisms behind sight. Something that had previously seemed so “real” and deterministic suddenly was no longer; the interpretation layer was momentarily unveiled.

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

#63

Post by esalman »

vavooom wrote: post #54

If you are interested in learning about the intersection of Artificial Neural Networks and Biological Neural Network research, I recommend " The Self-Assembling Brain - How Neural Networks Grow Smarter " by Peter Robin Hiesinger. He attempts to bridge research from both fields of study to identify where there are commonalities and differences in the design of these networks.

I second this. You can also check out the brain inspired podcast that features him: https://braininspired.co/podcast/124/

What I understand is that he claims the underlying algorithms that govern our behavior and how it evolves from birth are ingrained in our genetic code. Current neural network models try to model our behavior, but it is way behind when it comes to discovering those ingrained algorithms.

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

#64

Post by cuteboy19 »

ben_w wrote: post #41
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.

22 May 2018: https://arxiv.org/abs/1802.08195

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

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

#65

Post by marmada »

ben_w wrote: post #47
comboy wrote: post #40

Earlier quoted context omitted.

Optical illusions is one thing, but, I don't know, "Predictably Irrational", "Thinking fast and slow" or just whatever is happening all around. We do not understand our data. In general yes, I believe most people will only accept thinking machine when it can reproduce all our pitfalls. Because if we see something and the computer doesn't, then it clearly still needs to be improved, even if it's an optical illusion. B…

> But our bugs aren't sacred and special. I'd agree about sacred, but I have a hunch they may indeed be special… or at least useful. Current AI requires far more examples than we do to learn from, and I suspect all our biases are how evolution managed to do that.

Humans are trained on petabytes of data. From birth, we ingest sights, sounds, smells etc. Imagine a movie of every second of your life. And an audio track of every second of your life. Etc. Etc.

Humans get a lot of data.

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

#66

Post by merely-unlikely »

RosanaAnaDana wrote: post #43
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.

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 override the lower level AIs. What you think of as "me" is really just that top level AI but the whole system is needed to achieve general intelligence. Sort of like a company with many levels of employees serving different functions and a CEO to direct the whole thing, provide goals, modify components, and otherwise use discretion.

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

#67

Post by ffwd »

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 programs. It just spits out the training set in random configurations (ish).

I suspect also the reason the images look OK at a glance is because the images as a whole also represent patterns in the model so they actually come from "real life" / artist created images and thus have some sense of cohesion. But making the AI have all the right patterns so it never makes a mistake at all scales of the image while also being able to combine the pattern with real understanding of what they are conceptually is the real trick but until then it will be a "salad bowl collage" thing at random intervals.

The closest thing to the brain it looks like to me is simply the hierarchical nature of it which seems similar to v1/v2/the vision system in humans but I've only been told that, I'm no neuroscientist.

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

#69

Post by retrac »

lalos wrote: post #28

Here's my guess: neurons tap into quantum mechanics but we are too primitive to understand that for now. The brain was initially modeled as humors/fluids back when we developed aqueducts, then telegraph came into the scene and it was modeled as electrical impulses and now computers/ML are popular therefore we see it as a neural network. Next step is quantum.

It's certainly not proven, but there are many hints in that direction, and the hints keep piling up. Recent research [1] on how the classic anaesthetics work (a great mystery!) suggests they operate by inhibiting the entanglement of pairs of electrons in small molecules which split into free radicals, the electrons then physically separated but still-entangled.

It seems it is at least possible, that there is speed-of-light quantum communication within the brain. And that consciousness may hinge fundamentally on this. If this is true, we're pretty much back to square one in terms of understanding.

[1] https://science.ucalgary.ca/news/state-consciousness-may-inv...

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

#70

Post by russdill »

dekhn wrote: post #50
Retric wrote: post #42

Earlier quoted context omitted.

If the Brian is using some physics we don’t understand that’s something new not Quantum Mechanics. QM a specific theory of how the world operates, if something else is involved it doesn’t fall under that theory it’s [insert new theory’s name here]. I really don’t get why everyone wants the Brian to operate on some new QM effect other than peoples perception that a 100 year old theory is somehow cutting edge, spooky,…

I think you're probably missing a number of the important details. In the Penrose/Hammerof model, they're explicitly saying that humans are observed to generate problem solutions that could not have been generated by a purely classical computing process, therefore, the brain must exploit some specific quantum phenomenon. When you talk about QM a a theory of how the world operates, there are wide ranges of QM. Everyth…

The set of problems that are computable by a classical computer are the same set of problems computable by a quantum computer. I think you might be misstating the Penrose argument/position.
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