[HN Gopher] Study urges caution when comparing neural networks t... ___________________________________________________________________ Study urges caution when comparing neural networks to the brain Author : rntn Score : 107 points Date : 2022-11-03 19:35 UTC (3 hours ago) (HTM) web link (news.mit.edu) (TXT) w3m dump (news.mit.edu) | palata wrote: | No shit. | bee_rider wrote: | If they'd just called them "premium matrix multiplications" I bet | the field never would have caught on. | [deleted] | bawolff wrote: | 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. | cameronh90 wrote: | Check out the Velocopedia project for something else along this | line of thinking: | http://www.gianlucagimini.it/prototypes/velocipedia.html | | Turns out nobody quite knows how to draw a bicycle. They get | the gist but the details don't make sense. | Waterluvian wrote: | Yes! I'm confident this isn't an original thought, but I feel | like it's a dream generator. Things that aren't quite right but | are in some way, perfectly contextually and topologically | valid. Like it's tricking the object classifier in my brain | with a totally unrealistic thing that my brain is ready to | simply accept. | | There's some image I see on occasion that's 100% garbage. If | you focus on it you cannot make out a single thing. But if you | glance at it or see it scaled down, it looks like a table full | of stuff. | jasonwatkinspdx wrote: | 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. | RosanaAnaDana wrote: | 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 accomplished, these models | will almost assuredly represwbt some of the learnings required | to get there. | sebmellen wrote: | 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. | merely-unlikely wrote: | 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. | ben_w wrote: | For me, it's the way generative videos can rapidly, but to my | eyes seamlessly, transition from one shape to another. I may | not be able to record my dreams, but my memories of my dreams | do match this effect, with one place or person suddenly | becoming another. | Galaxeblaffer wrote: | It's very similar to strong trips on psychedelics. | vharuck wrote: | But wouldn't the people creating these models and deciding | whether to publish them prefer ones with these "understandable" | mistakes? There might have been other ones that had equal | potential as far the evaluation measure goes, but humans had | been involved all along the way and said, "Yeah, that picture | looks like a person made it. We should keep developing this | model." | ffwd wrote: | 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. | l33tman wrote: | "It just spits out the training set in random configurations | (ish)." is a pretty gross misrepresentation and | oversimplification of how such a model works, akin to saying | a human artist only spits out whatever they saw earlier in | their life in random configurations, or saying that SD only | spits out pixel values it has seen before, or combinations of | pixel values that form edges, etc. | | FWIW I don't think there is anything particularly wrong in | the model architectures or training data that in some | fundamental way makes it impossible to always get 2 arms. | After all, lots of other tricky things are almost always | correct. I suspect it's a question of training time and model | size mostly (not trivial of course as it's still expensive to | re-train to check modified architectures etc). It's also a | matter of diffusion sampling iterations and choice of sampler | at inference time, for the case of SD. | vavooom wrote: | 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. | esalman wrote: | 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. | constantcrying wrote: | 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. | comboy wrote: | 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. | | But our bugs aren't sacred and special. They just passed | Darwin's QA some thousands years ago. | ben_w wrote: | > 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. | marmada wrote: | 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. | elcomet wrote: | And you didn't even count the data from million of years | of evolution. The brain doesn't come as a blank slate | when you're born. | ben_w wrote: | That's literally what I was saying when I wrote "our | biases are how evolution managed to do that". | ben_w wrote: | Humans get a lot of _data_. | | AI gets more _examples_. | | Tesla autopilot _has_ a movie of every second it 's | active, for every car in the fleet that uses it. It has | how many lifetimes of driving data now? And yet, it's... | merely ok, nothing special, even when compared to all | humans including those oblivious of the fact they | shouldn't be behind a wheel. | ben_w wrote: | 22 May 2018: | | https://arxiv.org/abs/1802.08195 | cuteboy19 wrote: | I wonder if adversarial attacks can be mitigated by simply | passing a few transforms of the same image to the neural | network. | icare_1er wrote: | R.Penrose rules. | buscoquadnary wrote: | You're telling me were not just 5 years from AGI. | | _Shocked pickachu face_ | | Seriously the good news is that AGI and fusion are only like 5 to | 10 years out. The bad news is it's been that way for the past 40 | years. | [deleted] | ben_w wrote: | AGI is somewhere between "never" and "GPT-3 is it", depending | on how general the G has to be and how intelligent the I has to | be. | | (For all it's flaws, GPT-3 is already does better at random | professional knowledge than many TV show script writers). | gryBrd1987 wrote: | I sometimes wonder how much further along these initiatives | might be if the economy was focused on them instead of My | Pillow sales, cheap crap from Walmart, and simply handing | personally wealthy elites stacks of cash to create pointless | jobs and the money was put into net new technology, not Twitter | 2.0 and VR 4.0 | nightski wrote: | Who needs to eat or sleep, it just hurts productivity. | themitigating wrote: | The parent didn't suggest cutting off food, housing, or | other essentials. | mwint wrote: | Technically, everyone in Soviet Russia had food and | housing. | f6v wrote: | Have you lived there? | gryBrd1987 wrote: | Technically the US has more people in prison than China, | and QOL metrics have all been dropping for decades. | | Crack epidemic. Prescription drug epidemic. RvW being | gutted. | | Let's sit and fear becoming a nation state that collapsed | 30 years ago while ignoring we're already on the path. | [deleted] | te_chris wrote: | Move to China or North Korea I guess? Still seems to have | problems with graft though. | gryBrd1987 wrote: | Ah the exceptional minds of the US. "If it smells like a | violation of politically correct tradition as I know it, | it's communism!" | te_chris wrote: | Not American, but you asked for planned economy: those | are it. | gryBrd1987 wrote: | So all these US businesses do zero planning? The Fed is | raising rates for lulz? We've unintentionally allowed | consolidation of ownership? No one has any clue? You're | nitpicking semantics. | f6v wrote: | China is definitely not a "planned economy" in a sense | that you meant it. Also, every economy is planned in some | sense. Every government plans how much it's going to make | and spend. | yboris wrote: | The meme of fusion being "5 years from now ... for past 40 | years" is so frustrating. This is because the investment into | it went down to abysmal -- not even "maintenance" level of when | it was just getting started. | | If the government spent money on it, we would likely have more | progress. | | And today isn't like it was before -- we have ReBCO ;) | otikik wrote: | We just need to figure out how to align the carbon nanotubes | properly | lalos wrote: | 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. | tuanx5 wrote: | Funny you should mention that! | https://www.news.ucsb.edu/2018/018840/are-we-quantum-compute... | mach1ne wrote: | Eh, we might go there, but I don't think the core algorithm | that's running in our heads has much to do with quantum-level. | varjag wrote: | Look up 'Penrose argument'. _Personally_ tho I believe it 's | the physicists' equivalent of seeing everything as a nail when | using a hammer. | RosanaAnaDana wrote: | ok but, at least historically nn as discussed were | interesting because of their resemblence to naturally | networked systems. | retrac wrote: | 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... | tabtab wrote: | We don't currently fully know how anesthetics work largely | because we don't really know how the human brain works on a | large scale. We'd have to solve that before seriously | proposing quantum effects. In other words, it's too early to | rule out classic physics and chemistry as the brain's primary | mechanism. (Although solving how it works could first solving | quantum mysteries, but Occam's razor is classic rules in my | opinion.) | Retric wrote: | 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, or something. | Perhaps it's that the overwhelming majority of people who talk | about QM don't actually understand it even a little bit. Odd | bits of QM are already why lasers, LED's, and transistors work. | You use incites from the theory everyday in most electronic | devices, but it's just as relevant for explaining old | incandescent bulbs we just had other theories that seemed to | explain them. | dekhn wrote: | 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. Everything from predicting the | structure and energy states of a molecule, to how P/N | junctions work, to quantum computers. Now, for the first one | (molecules), the vast majority of QM is just giving ways to | compute the electron density and internuclear distances using | some fairly straightforward and noncontroversial approaches. | | For the other ones (P/N junctions, QC computers, etc), those | involve exploiting very specific and surprising aspects of | quantum theory: one of quantum tunnelling, quantum coherence, | or quantum entanglement (ordered from least counterintuitive | to most). We have some evidence already that there are some | biological processes that exploit tunnelling and coherence, | but none that demonstrate entanglement. | | Personally, I think most people think the alternative to | Penrose- the brain does not compute non-computable functions, | and does not exploit or need to exploit any quantum phenomena | (expect perhaps tunnelling) to achieve its goals. | | Now, if we were to have hard evidence supporting the idea | that brains use entanglement to solve problems: well, that | would be pretty amazing and would upend large parts of modern | biology adn technology research. | Retric wrote: | The Brian using entanglement would completely destroy | modern physics as we know it, the effect on biology would | be tiny by comparison. | | Your other points are based on such fundamental | misunderstanding that it's hard to respond. Saying | something isn't the output of classical computing processes | while undemonstrated, is then used to justify saying they | must therefore use Quantum Phenomenon. But logically not | everything that is either classical or Quantum so even that | logical inference is unjustified. Logically it's like | saying well it's not a soda so it must be a rock. | | PS: If people where observed to solve problems that can't | be solved by classical computer processing that would be a | really big deal. As in show up on Nightly News, and win | people Nobel prizes big. Needless to say it hasn't | happened. | russdill wrote: | 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. | [deleted] | RosanaAnaDana wrote: | My understanding of the hypothesis being represented here is | QM as a kind of random number generator operating at the | neuron/microtubule level. I didn't think there was anything | other than a modest injection of randomness being invoked, | but I could be misstating the premise. | crowbahr wrote: | It's an absurd premise to begin with: The scale at which | quantum effects propagate and are observed is radically | different than the scale at which the neurons in your brain | operate. | | The functional channels for neurons are well understood, | even if we're still diagramming out all the types of | neurons. Voltage gated calcium channels are pretty damn | simple in the grand scheme of things, and they don't leave | space for quantum interactions beyond that of standard | molecular interactions. | | The only part of the brain we don't understand is how all | the intricacies work together, because that's a lot more | opaque. | marginalia_nu wrote: | Neurons almost certainly use quantum processes, but so do most | transistors. The brain is too too warm for large-scale quantum | effects though. You're not going to find phase coherence at | that scale in such an environment, which is pretty much the | prerequisite for quantum effects (that is fairly well | understood). | tabtab wrote: | I believe what was meant was quantum-only or primarily- | quantum effects rather than the _aggregate_ effects we | normally see (classic physics & chemistry), which are | probably the result of quantum physics, but we have "classic" | abstractions that model them well enough. Thus, the issue is | whether the brain relies mostly on classic effects (common | aggregate abstractions) for computations or on quantum- | specific effects. | Teever wrote: | But why is the next step quantum? And why is this the final | step? | tarboreus wrote: | Because we don't understand quantum physics, and we don't | understand the brain. I don't think we know if it's the final | step. There could be wizard jelly or something at the bottom. | marginalia_nu wrote: | Quantum physics is fairly well understood. Perhaps not | among laymen, but that's mostly due to pedagogical | challenges, which is why a lot of the discourse seems to be | stuck approaching it as though we were living nearly 100 | years into the past. | kgarten wrote: | 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...) | | study urges caution comparing the model to the real thing, as the | model has some generalizations the real thing does not ... | Barrin92 wrote: | 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. | gryBrd1987 wrote: | The brain is not involved in a whole lot of behaviors though. | Cells organize themselves to an extent. Cuts heal without us | focusing conscious thought on them. | | The brain is a hard drive but the body is the whole computer. | | Science is proving physical causation. Not just writing down | what we want to be true. | RosanaAnaDana wrote: | Emergent pheneomena both perhaps? | tudorw wrote: | https://thedebrief.org/is-consciousness-really-a-memory- | syst... | jcims wrote: | Andrej Karpathy was recently on Lex Fridman's podcast and covered | this to some extent. He has the same perspective on this topic | and expanded on it quite a bit. Great listen overall IMHO - | https://www.youtube.com/watch?v=cdiD-9MMpb0 | | I like his idea of finding 0-days in physics. :) | [deleted] | nvrspyx wrote: | From the actual study's abstract: | | > Unique to Neuroscience, deep learning models can be used not | only as a tool but interpreted as models of the brain. The | central claims of recent deep learning-based models of brain | circuits are that they make novel predictions about neural | phenomena or shed light on the fundamental functions being | optimized... Using large-scale hyperparameter sweeps and theory- | driven experimentation, we demonstrate that the results of such | models may be more strongly driven by particular, non- | fundamental, and post-hoc implementation choices than fundamental | truths about neural circuits or the loss function(s) they might | optimize. Finally, we discuss why these models cannot be expected | to produce accurate models of the brain without the addition of | substantial amounts of inductive bias, an informal No Free Lunch | result for Neuroscience. In conclusion, caution and | consideration, together with biological knowledge, are warranted | in building and interpreting deep learning models in | Neuroscience. | | And IMO a succinct description of the problematic assumption | being cautioned against in the study's introduction section: | | > Broadly, the essential claims of DL-based models of the brain | are that 1) Because the models are trained on a specific | optimization problem, if the resulting representations match what | has been observed in the brain, then they reveal the optimization | problem of the brain, or 2) That these models, when trained on | sensibly motivated optimization problems, should make novel | predictions about the brain's representations and emergent | behavior. | | --- | | I think to most, the problem with claim number 2 directly above | is obvious, but it's important to also look at claim 1. | [deleted] | random_upvoter wrote: | Any true understanding or new insight originates in the plexus | solaris which is near your heart, then somewhat slowly works it | way up to the spine. The brain is a somewhat predictable fleshy | motor capable of turning the insight into language, storing it in | memory, or acting on it. Most of the times we "get by" with the | stored procedures in the brain but don't imagine it's the place | where original understanding is generated. Funny how the ancient | Egyptians understood this but we don't. Of course this is also | why all attempts to create AI by simulating what happens in the | brain are doomed to hilarious failure. | nathias wrote: | philosophers caution when comparing an analogy to a thing | dekhn wrote: | also the map is not the territory | cirgue wrote: | I think it's also important to highlight that the analogy | between neural networks and brains is to help people visualize | what a neural network is, not what a brain is. It's really just | to convey the idea of multiple nodes passing information to one | another. After that point, the comparison is useless because | the two systems diverge so wildly outside of that one (pretty | loose) conceptual connection. | adharmad wrote: | This is a very old article by Francis Crick which essentially | says the same: https://www.nature.com/articles/337129a0 | babblingfish wrote: | As someone who studied Neuroscience in college, I remember this | paper and some other examples showing just how different | computational neural networks are from real neurons. It's | difficult for me to believe that professional researchers could | really believe a NN is an accurate model of the real deal. | | The paper also does not have any reference to a study or paper | that explicitly states that a neural network is a good model | for grid cells. (Please correct me if I am wrong.) So I am left | wondering why this direction was chosen. | | Maybe it's a little cynical, but this topic seems to have been | chosen (at least in part) to produce a splashy headline. Or in | other words, to give the Stanford and MIT PR engine something | to print. | | This is the sort of obvious thing we all knew to be true. Why | people with access to lab animals and a fully stocked | microbiology lab needed to prove it (again) I do not | understand. | emptybits wrote: | Last week's Lex Fridman podcast featured Andrej Karpathy (former | director of AI at Tesla, founding member of OpenAI) and they | discussed this aspect briefly also. | | The usefulness of neural networks has not ceased, despite | researchers' early ideas and hopes about its biological analogies | having somewhat sheared away. | cameronfraser wrote: | Most introductory deep learning courses are very clear about how | far the analogy goes, if people are interpreting it as something | more I don't think it's the fault of practitioners/educators and | more the fault of people's imagination and selective hearing. ___________________________________________________________________ (page generated 2022-11-03 23:00 UTC)