9 ms·
This is an important result. The actual paper [1] says that functional MRI (which is measuring which parts of the brain are active by sensing blood flow) indic
by Animats 2y ago
This is an important result.
The actual paper [1] says that functional MRI (which is measuring which parts of the brain are active by sensing blood flow) indicates that different brain hardware is used for non-language and language functions.
This has been suspected for years, but now there's an experimental result.
What this tells us for AI is that we need something else besides LLMs. It's not clear what that something else is. But, as the paper mentions, the low-end mammals and the corvids lack language but have some substantial problem-solving capability. That's seen down at squirrel and crow size, where the brains are tiny. So if someone figures out to do this, it will probably take less hardware than an LLM.
This is the next big piece we need for AI. No idea how to do this, but it's the right question to work on.
[1] https://www.nature.com/articles/s41586-024-07522-w.epdf?sharing_token=1BwycCwx1wQ2Sfnub1-o0NRgN0jAjWel9jnR3ZoTv0MYYopL5qENCL5gCQ3HDKyBWf6AQLs-HC3fMMzU9skb40K1DK-HWblYUyHTAQuuliWeLXeg5lXVNFOTa3fVek1R0et9kPjIgQljFd2wX1hSlqWjpOKSrRjz8t2mUDQ6Vr6DlhIlAndISxjxnRU2FPd2XMQFK5UDTh5Osiq6IYOksvy1nGE68d0y9YuJvr4Zrok%3D&tracking_referrer=www.scientificamerican.com https://www.nature.com/articles/s41586-024-07522-w.epdf?shar...
- HarHarVeryFunny 2y agoBrain size isn't necessarily a very good correlate of intelligence. For example dolphins and elephants have bigger brains than humans, and sperm whales have much bigger brains (5x by volume). Neanderthals also had bigger brains than modern humans, but are not thought to have been more intelligent. A crow has a small brain, but also has very small neurons, so ends up having 1.5B neurons, similar to a dog or some monkeys.
- card_zero 2y agoNot sure neuron number correlates to smarts, either. https://www.scientificamerican.com/article/gut-second-brain/ https://www.scientificamerican.com/article/gut-second-brain/ There are 100 million in my gut, but it doesn't solve any problems that aren't about poop, as far as I know. https://en.wikipedia.org/wiki/List_of_animals_by_number_of_neurons https://en.wikipedia.org/wiki/List_of_animals_by_number_of_n... If the suspiciously round number is accurate, this puts the human gut somewhere between a golden hamster and ansell's mole-rat, and about level with a short-palated fruit bat.
- HarHarVeryFunny 2y agoAgreed. It's architecture that matters, although for a given brain architecture (e.g. species) there might be benefits to scale. mega-brain vs pea-brain. I was just pointing out that a crow's brain is built on a more advanced process node than our own. Smaller transistors.
- Animats 2y agoThat makes sense. Birds are very weight-limited, so there's evolutionary pressure to keep the mass of the control system down.
- readthenotes1 2y agoI suspect there is more going on with your gut neurons then you would expect. If nothing else, the vagus nerve I had to direct communication link. I like to think that it is my gut brain that is telling me that it's okay to have that ice cream...
- kridsdale1 2y agoDon’t assume whales are less intelligent than humans. They’re tuned for their environment. They won’t assemble machines with their flippers but let’s toss you naked in the pacific and see if you can communicate and collaborate with peers 200km away on complex hunting strategies.
- batch12 2y agoLet's toss a whale on land and see if it can communicate and collaborate with peers 10 ft away on anything. I don't think being tuned to communicate underwater makes them more intelligent than humans.
- ninetyninenine 2y ago> I don't think being tuned to communicate underwater makes them more intelligent than humans. Your responding to a claim that was never made. The claim was don't assume humans are smarter than whales. Nobody said whales are more intelligent than humans. He just said don't assume.
- BoingBoomTschak 2y agoWhy would he not "assume" that when humans have shaped their world so far beyond what it was, creating intricate layers of art, culture and science; even going into space or in the air? Man collectively tamed nature and the rest of the animal kingdom in a way that no beast ever has. Anyway, this is just like solipsism, you won't find a sincere one outside the asylum. Every Reddit intellectual writing such tired drivel as "who's to say humans are more intelligent than beasts?" deep down knows the score.
- ninetyninenine 2y ago> Why would he not "assume" that when humans have shaped their world so far beyond what it was, creating intricate layers of art, culture and science; even going into space or in the air? Man collectively tamed nature and the rest of the animal kingdom in a way that no beast ever has. Because whales or dolphins didn’t evolve hands. Hands are a foundational prerequisite for building technology. So if whales or dolphins had hands we don’t know if they would develop technology that can rival us. Because we don’t know, that’s why he says don’t assume. This isn’t a “deep down we know” thing like your more irrational form of reasoning. It is a logical conclusion: we don’t know. So don’t assume.
- FL33TW00D 2y agoIt's probably more relevant to compare intraspecies rather than interspecies. And it turns out that human brain volume and intelligence are moderately-highly correlated [1][2]! [1]: https://pmc.ncbi.nlm.nih.gov/articles/PMC7440690/ https://pmc.ncbi.nlm.nih.gov/articles/PMC7440690/ [2]: https://www.sciencedirect.com/science/article/abs/pii/S0160289604001357?via%3Dihub https://www.sciencedirect.com/science/article/abs/pii/S01602...
- yurimo 2y agoRight, but what is also important to remember is while size is important what is also key here is the complexity of a neural circuits. Human brain has a lot more connections and is much more complex.
- famouswaffles 2y agoDolphins, Orcas, whales and other intelligent cetaceans do not have Hands and live in an environment without access to a technological accelerator like fire. The absence of both of these things is an incredible crippler for technological development. It doesn't matter how intelligent you are, you're never going to achieve much technologically without these. I don't think brain size correlations is as straightforward as 'bigger = better' every time but we simply don't know how intelligent most of these species are. Land and Water are completely different beasts.
- HarHarVeryFunny 2y agoIntelligence isn't measured by ability to create technology or use tools. Intelligence is the ability to use experience to predict your environment and the outcomes of your own actions. It's a tool for survival.
- famouswaffles 2y agoOkay and how have we determined we have more intelligence than those species with this measure ?
- HarHarVeryFunny 2y agoClearly we haven't, given that there is very little agreement as to what intelligence is. This is just my definition, although there's a lot behind why I define it this way. However, I do think that a meaningful intelligence comparison between humans and dolphins, etc, would conclude that we are more intelligent, especially based on our reasoning/planning (= multi-step prediction) abilities, which allows us not only to predict our environment but also to modify it to our desires in very complex ways.
- famouswaffles 2y ago>However, I do think that a meaningful intelligence comparison between humans and dolphins, etc, would conclude that we are more intelligent, especially based on our reasoning/planning (= multi-step prediction) abilities I'm not sure how you would make meaningful comparisons here. We can't communicate to them as they communicate and we live in almost completely different environments. Any such comparison would be extremely biased to us. >which allows us not only to predict our environment but also to modify it to our desires in very complex ways. We modify our environment mostly through technology. Intelligence is a big part of technology sure but it's not the only part of it and without the other parts (hands with opposable thumbs, fire etc), technology as we know it wouldn't exist and our ability to modify the environment would seem crippled to any outside observer regardless of how intelligent we may be. It's not enough to think that the earth revolves around the sun, we need to build the telescopes (with hands and materials melted down and forged with fire) to confirm it. It's not enough to dream and devise of flight, we need the fire to create the materials that we dug with our hands and the hands to build them. It's not enough to think that Oral communication is insufficient for transmitting information through generations. What else will you do without opposable thumbs or an equivalent ? Fire is so important for so many reasons but one of the biggest is that it was an easy source of large amounts of energy that allowed us to bootstrap technology. Where's that easy source of energy underwater ? Without all the other aspects necessary for technology, we are relegated to hunter/gatherer levels of influencing the environment at best. Even then, we still crafted tools that creatures without opposable thumbs would never be able to craft.
- KoolKat23 2y ago> What this tells us for AI is that we need something else besides LLMs. Basically we need Multimodal LLM's (terrible naming as it's not an LLM then but still).
- Animats 2y agoI don't know what we need. Nor does anybody else, yet. But we know what it has to do. Basically what a small mammal or a corvid does. There's been progress. Look at this 2020 work on neural net controlled drone acrobatics.[1] That's going in the right direction. [1] https://rpg.ifi.uzh.ch/docs/RSS20_Kaufmann.pdf https://rpg.ifi.uzh.ch/docs/RSS20_Kaufmann.pdf
- fuzzfactor 2y agoYou could say language is just the "communication module" but there has got to be another whole underlying interface where non-verbal thoughts are modulated/demodulated to conform to the language expected to be used when communication may or may not be on the agenda.
- bbor 2y agoWell said! This is a great restatement of the core setup of the Chomskian “Generative Grammar” school, and I think it’s an undeniably productive one. I haven’t read this researchers full paper, but I would be sad (tho not shocked…) if it didn’t cite Chomsky up front. Beyond your specific point re:interfaces—which I recommend the OG Syntactic Structures for more commentary on—he’s been saying what she’s saying here for about half a century. He’s too humble/empirical to ever say it without qualifiers, but IMO the truth is clear when viewed holistically: language is a byproduct of hierarchical thought, not the progenitor. This (awesome!) researcher would likely disagree with what I’ve just said based on this early reference: In the early 2000s I really was drawn to the hypothesis that maybe humans have some special machinery that is especially well suited for computing hierarchical structures. …with the implication that they’re not, actually. But I think that’s an absurd overcorrection for anthropological bias — humans are uniquely capable of a whole host of tasks, and the gradation is clearly a qualitative one. No ape has ever asked a question, just like no plant has ever conceptualized a goal, and no rock has ever computed indirect reactions to stimuli.
- danielmarkbruce 2y agoIs it important? To who? Anyone with half a brain is aware that language isn't the only way to think. I can think my way through all kinds of things in 3-d space without a single word uttered in any internal monologue and I'm not remotely unique - this kind of thing is put in all kinds of math and iq'ish like tests one takes as a child.
- voxl 2y agoBefore you say things this patiently dumb you should probably wonder what question the researchers are actually interested in and why your average experience isn't sufficient proof.
- gotoeleven 2y agoI am 3-d rotating this comment in my head right now
- orhmeh09 2y ago*patently
- danielmarkbruce 2y agoIt's "patently" and maybe understand the definition of "average" before using it. Once you've figured out how to use language, explain why this is important and to who. Then maybe what the upshot will be. The fact that someone has proven something to be true doesn't make it important. The comment I replied to made it sound like it's important to the field of AI. It is not. Almost zero serious researchers think LLMs all by themselves are "enough". People are working on all manner of models and systems incorporating all kinds of things "not LLM". Practically no one who actually works in AI reads this paper and changes anything, because it only proves something they already believed to be true and act accordingly.
- jebarker 2y ago> What this tells us for AI is that we need something else besides LLMs Not to over-hype LLMs, but I don't see why this results says this. AI doesn't need to do things the same way as evolved intelligence has.
- weard_beard 2y agoTo a point. If you drill down this far into the fundamentals of cognition you begin to define it. Otherwise you may as well call a cantaloupe sentient
- jebarker 2y agoI don't think anyone defines AI as "doing the thing that biological brains do" though, we define it in terms of capabilities of the system.
- weard_beard 2y agoI think if you gave it the same biological inputs as a biological brain you would quickly see the lack of capabilities in any man made system.
- Dylan16807 2y agoOkay, but does that help us reach any meaningful conclusions? For example, okay some AI system doesn't have the capabilities of an auditory cortex or somatosensory cortex. Is there a reason for me to think it needs that?
- weard_beard 2y agoName a creature on earth without one. Imagine trying to limit, control, or explain a being without familiar cognitive structures. Is there a reason to care about such unfamiliar modalities of cognition?
- theptip 2y agoLLM as a term is becoming quite broad; a multi-modal transformer-based model with function calling / ReAct finetuning still gets called an LLM, but this scaffolding might be all that’s needed. I’d be extremely surprised if AI recapitulates the same developmental path as humans did; evolution vs. next-token prediction on an existing corpus are completely different objective functions and loss landscapes.
- fhdsgbbcaA 2y agoI asked both OpenAI and Claude the same difficult programming question. Each gave a nearly identical response down to the variable names and example values. I then looked it up and they had each copy/pasted the same Stack overflow answer. Furthermore, the answer was extremely wrong, the language I used was superficially similar to the source material, but the programming concepts were entirely different. What this tells me is there is clearly no “reasoning” happening whatsoever with either model, despite marketing claiming as such.
- alphan0n 2y agoWhat was the question?
- fhdsgbbcaA 2y agoHad to do with connection pooling.
- alphan0n 2y agoSmall wonder why you received a sub-optimal response.
- fhdsgbbcaA 2y agoI’ll say the unholy combination of managing the python GIL, concurrency, and connection reuse is not my favorite topic.
- yapyap 2y agoLol, it’s insane how some people will track everything back to AI
- tempodox 2y agoCan't escape the hype.
- fhdsgbbcaA 2y agoMy first thought as well - “AGI via LLM” implies that our grey matter is merely a substrate for executing language tasks: just swap out bio-neurons for a few H100s and viola, super intelligence.
- mountainriver 2y agoIt’s AGI via transformer
- NeuroCoder 2y agoI'm not convinced the result is as important here as the methods. Separating language from complex cognition when evaluating individuals is difficult. But many of the people I've met in neuroscience that study language and cognitive processes do not hold the opinion that one is absolutely reliant on the other in all cases. It may have been a strong argument a while ago, but everytime I've seen a presentation on this relationship it's been to emphasize the influence culture and language inevitably have on how we think about things. I'm sure some people believe that one cannot have complex thoughts without language, but most people in speech neuro I've met in language processing research find the idea ridiculous enough they wouldn't bother spending a few years on that kind of project just to disapprove a theory. On the other hand, further understanding how to engage complex cognitive processes in nonverbal individuals is extremely useful and difficult to accomplish.
- red75prime 2y ago> What this tells us for AI is that we need something else besides LLMs You mean besides a few layers of LLMs near input and output that deal with tokens? We have the rest of the layers.
- alephnerd 2y agoThose "few layers" sum up all of linguistics. 1. Syntax 2. Semantics 3. Pragmatics 4. Semiotics These are the layers you need to solve. Saussure already pointed out these issues over a century ago, and Linguists turned ML Researchers like Stuart Russell and Paul Smolensky tried in vain to resolve this. It basically took 60 years just to crack syntax at scale, and the other layers are still fairly far away. Furthermore, Syntax is not a solved problem yet in most languages. Try communicating with GPT-4o in colloquial Bhojpuri, Koshur, or Dogri, let alone much less represented languages and dialects.
- sojournerc 2y agoLinguistics is not living! Language does not capture reality! So no matter how much you solve you're no closer to AGI
- CSMastermind 2y agoWhen you look at how humans play chess they employ several different cognitive strategies. Memorization, calculation, strategic thinking, heuristics, and learned experience. When the first chess engines came out they only employed one of these: calculation. It wasn't until relatively recently that we had computer programs that could perform all of them. But it turns out that if you scale that up with enough compute you can achieve superhuman results with calculation alone. It's not clear to me that LLMs sufficiently scaled won't achieve superhuman performance on general cognitive tasks even if there are things humans do which they can't. The other thing I'd point out is that all language is essentially synthetic training data. Humans invented language as a way to transfer their internal thought processes to other humans. It makes sense that the process of thinking and the process of translating those thoughts into and out of language would be distinct.
- PaulDavisThe1st 2y ago> It's not clear to me that LLMs sufficiently scaled won't achieve superhuman performance on general cognitive tasks If "general cognitive tasks" means "I give you a prompt in some form, and you give me an incredible response of some form " (forms may differ or be the same) then it is hard to disagree with you. But if by "general cognitive task" you mean "all the cognitive things that human do", then it is really hard to see why you would have any confidence that LLMs have any hope of achieving superhuman performance at these things.
- jhrmnn 2y agoEven in cognitive tasks expressed via language, something like a memory feels necessary. At which point it’s not a LLM as in a generic language model. It would become a language model conditioned on the memory state.
- ddingus 2y agoMore than a memory. Needs to be a closed loop, running on its own. We get its attention, and it responds, or frankly if we did manage any sort of sentience, even a simulation of it, then the fact is it may not respond. To me, that is the real test.
- shepherdjerred 2y ago> What this tells us for AI is that we need something else besides LLMs. Humans not taking this approach doesn’t mean that AI cannot.
- earslap 2y agoNot only that but also LLMs "think" in a latent representation that is several layers deep. Sure, the first and last layers make it look like it is doing token wrangling, but what is happening in the middle layers is mostly a mystery. First layer deals directly with the tokens because that is the data we are observing (a "shadow" of the world) and last layer also deals with tokens because we want to understand what the network is "thinking" so it is a human specific lossy decoder (we can and do remove that translator and plug the latent representations to other networks to train them in tandem). There is no reason to believe that the other layers are "thinking in language".
- sidewndr46 2y agoI believe what this tells is that thought requires blood flow in the brain of mammals. Stepping back a level, it may only actually tell us that MRIs measure blood flow.
- agentcoops 2y agoFor those interested in the history, this is in fact the Neural Network research path that predated LLMs. Not just in the sense that Hinton et al and the core of the "Parallel Distributed Processing"/Connectionist school were always opposed to Chomsky's identification of brain-thought-language, but that the original early 2000s NSF grant awarded to Werbos, Ng, LeCun et al was for "Deep Learning in the Mammalian Visual Cortex." In their research program, mouse intelligence was posited as the first major challenge.
- avazhi 2y agoIt’s impossible to overstate how crude and useless blood flow MRI studies are, at least relative to the hype they receive. Spoiler alert: brains require a lot of blood, constantly, just to not die. Looking at blood flow on an MRI to determine neural circuitry has to deal with the double whammy of both an extremely crude tool and a correlation/causation fallacy. This article and the study are arguably useless.
- agumonkey 2y agoThe connectome and brain mapping efforts might be a better research path for the coming years I guess
- nickpsecurity 2y agoThe projects mapping the brain, combined with research on what areas do, should tell us what components are necessary for our design. Studying the behavior of their specialist structures will tell us how to make purpose-built components for these tasks. Even if not, just attempting to split up the global behavior in that many ways with specialist architecture might help. We can also imitate how the components synchronize together, too. An example was the problem of memory shared between systems. ML people started doing LLM’s with RAG. I looked into neuroscience which suggested we need a hippocampus model. I found several papers with hippocampus-like models. Combining LLM’s, vision, etc with hippocampus-like model might get better results. Rinse repeat for these other brain areas wherever we can understand them. I also agree on testing the architectures with small, animal brains. Many do impressive behaviors that we should be able to recreate in simulators or with robotics. Some are useful, too, like how geese are good at security. Maybe embed a trained, goose brain into a camera system.
- yarg 2y ago> What this tells us for AI is that we need something else besides LLMs. Perhaps, but the relative success of trained LLMs acting with apparent generalised understanding may indicate that it is simply the interface that is really an LLM post training; That the deeper into the network you go (the further from the linguistic context), the less things become about words and linguist structure specifically and the more it becomes about things and relations in general. (This also means that multiple interfaces can be integrated, sometimes making translation possible, e.g.: image <=> tree<string>)
- necovek 2y agoYou seem to be conflating "different hardware" with proof that "language hardware" uses "software" equivalent to LLMs. LLMs basically become practical when you simply scale compute up, and maybe both regions are "general compute", but language ends up on the "GPU" out of pure necessity. So to me, these are entirely distinct questions: is the language region able to do general cognitive operations? What happens when you need to spell out "ubiquitous" or declense a foreign word in a language with declension (which you don't have memory patterns for)? I agree it seems obvious that for better efficiency (size of training data, parameter count, compute ability), human brains use different approach than LLMs today (in a sibling comment, I bring up an example of my kids at 2yo having a better grasp of language rules than ChatGPT with 100x more training data). But let's dive deeper in understanding what each of these regions can do before we decide to compare to or apply stuff from AI/CS.
- ninetyninenine 2y ago>What this tells us for AI is that we need something else besides LLMs. No this is not true. For two reasons. 1. We call these things LLMs and we train it with language but we can also train it with images. 2. We also know LLMs develop a sort of understanding that goes beyond language EVEN when the medium used for training is exclusively language. The naming of LLMs is throwing you off. You can call it a Large Language Model but this does not mean that everything about LLMs are exclusively tied only to language. Additionally we don't even know if the LLM is even remotely similar to the way human brains process language. No such conclusion can be drawn from this experiment.
- agumonkey 2y agoAt times I had impaired brain function (lots of soft neurological issues, finger control, memory loss, balance issues) but surprisingly the core area responsible for mathematical reasoning was spared .. that was a strange sensation, almost schizophrenic. And yeah it seems that core primitives of intelligence exist very low in our brains. And with people like Michael Levin, there may even be a root beside nervous systems.
- ddingus 2y agoWe should look to the animals. Higher order faculties aside, animals seem like us, just simpler. The higher functioning ones appear to have this missing thing too. We can see it in action. Perhaps all of them do and it is just harder for us when the animal thinks very differently or maybe does not think as much, feeling more, for example. ---- Now, about that thing... and the controversy: Given an organism, or machine for this discussion, is of sufficiently robust design and complexity that it can precisely differentiate itself from everything else, it is a being. This thing we are missing is an emergent property, or artifact that can or maybe always does present when a state of being also presents. We have not created a machine of this degree yet. Mother nature has. The reason for emergence is a being can differentiate sensory input as being from within, such as pain, or touch, and from without, such as light or motion. Another way to express this is closed loop vs open loop. A being is a closed loop system. It can experience cause and effect. It can be the cause. It can be the effect. A lot comes from this closed loop. There can be the concept of the self and it has real meaning due to the being knowing what is of itself or something, everything else. This may be what forms consciousness. Consciousness may require a closed loop, and organism of sufficient complexity to be able to perceive itself. That is the gist of it. These systems we make are fantastic pieces. They can pattern match and identify relationships between the data given in amazing ways. But they are open loop. They are not beings. They cannot determine what is part of them, what they even are,or anything really. I am both consistently amazed and dismayed at what we can get LLM systems to do. They are tantalizingly close! We found a piece of how all this works and we are exploiting the cral out of it. Ok fine. Humans are really good at that. But it will all taper off. There are real limits because we will eventually find the end goal will be to map out the whole problem space. Who has tried computing that? It is basically all possible human thought. Not going to happen. More is needed. And that "more" can arrive at thoughts without having first seen a few bazillion to choose from.
- afiodorov 2y ago> What this tells us for AI is that we need something else besides LLMs I am not convinced it follows. Sure LLMs don’t seem complete however there’s a lot of unspoken inference going on in LLMs that don’t map into a language directly already - the inner layers of the deep neural net that operates on abstract neurons.
- reverius42 2y agoInterestingly though for AI, this doesn’t necessarily mean we need a different model architecture. A single large multimodal transformer might be capable of a lot that an LLM is not (besides the multimodality).
- jll29 2y agoMost pre-deep learning architectures had separate modules like "language model", "knowledge base" and "inference component". Then LLMs came along, and ML folks got rather too excited that they contain implicit knowledge (which, of course, is required to deal with ambiguity). Then the new aspiration as "all in one" and "bigger is better", not analyzing what components are needed and how to orchestrate their interplay. From an engineering (rather than science) point of view, the "end-to-end black box" approach is perhaps misguided, because the result will be a non-transparent system by definition. Individual sub-models should be connected in a way that retains control (e.g. in dialog agents, SRI's Open Agent Architecture was a random example of such "glue" to tie components together, to name but one). Regarding the science, I do believe language adds to the power of thinking; while (other) animals can of course solve simple problems without language, language permits us to define layers of abstractions (by defining and sharing new concepts) that goes beyond simple, non-linguistic thoughts. Programming languages (created by us humans somewhat in the image of human language) and the language of mathematics are two examples where we push this even further (beyond the definition of new named concepts, to also define new "DSL" syntax) - but all of these could not come into beying without human language: all formal specs and all axioms are ultimately and can only be formulated in human language. So without language, we would likely be stuck at a very simple point of development, individually and collectively. EDIT: 2 typos fixed
- djtango 2y agoIs beying another typo? In my personal learning journey I have been exploring the space of intuitive learning which is dominant in physical skills. Singing requires extremely precise control of actions we can't fully articulate or even rationalise. Teaching those skills requires metaphors and visualising and a whole lot of feedback + trial & error. I believe that this kind of learning is fundamentally non verbal and we can achieve abstraction of these skills without language. Walking is the most universal of these skills and we learn it before we can speak but if you study it (or better try to program a robot to walk with as many degrees of freedom as the human musculoskeletal system) you will discover that almost all of us don't understand what all the things that go into the "simple" task of walking! My understanding is that people who are gifted at sports or other physical skills like musical instruments have developed the ability to discover and embed these non verbal abstractions quickly. When I practise the piano and am working on something fast, playing semiquavers at anything above 120bpm is not really conscious anymore in the sense of "press this key then that key" The concept of arpeggio is verbal but the action is non verbal. In human thought where does verbal and non-verbal start and end? Its probably a continuum
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- dboreham 2y agoNot sure about that. The same abstract model could be used for both (symbols generated in sequence). For language the symbols have meaning in the context of language. For non-language thought they don't. Nature seems to work this way in general: re-using/purposing the same underlying mechanism over and over at different levels in the stack. All of this could be a fancy version of very old hardware that had the purpose of controlling swimming direction in fish. Each symbol is a flick of the tail.
- exe34 2y agoI like to think of the non-verbal portions as the biological equivalents of ASICs. even skills like riding a bicycle might start out as conscious effort (a vision model, a verbal intention to ride and a reinforcement learning teacher) but is then replaced by a trained model to do the job without needing the careful intentional planning. some of the skills in the bag of tricks are fine tuned by evolution. ultimately, there's no reason that a general algorithm couldn't do the job of a specific one, just less efficiently.
- winwang 2y agoI mean, the QKV part of transformers is like an "ASIC" ... well, for an (approximate) lookup table. (also important to note that NNs/LLMs operate on... abstract vectors, not "language" -- not relevant as a response to your post though).
- exe34 2y agoactually I think you are on to something - abstract vectors are the tokens of thought - mentalese if you've read any Dennett.
- xtrapol8 2y agoYou highlight an expectation that the “truer intelligence” is a singular device, once isolated would mobilize ultimate AGI. All intelligence is the mitigation of uncertainty (the potential distributed problem.) if it does not mitigate uncertainty it is not intelligence, it is something else. Intelligence is a technology. For all life intelligence and the infrastructure of performing work efficiently (that whole entropy thing again) is a technology. Life is an arms race to maintain continuity (identity, and the very capacity of existential being.) The modern problem is achieving reliable behavioral intelligence (constrained to a specific problem domain.) AGI is a phantasm. What manifestation of intelligence appears whole and complete and is always right? These are the sorts of lies you tell yourself, the ones that get you into trouble. They distract from tangible real world problems, perhaps causing some of them. True intelligence is a well calibrated “scalar” domain specific problem (uncertainty) reducer. There are few pressing idempotent obstructions in the real world. Intelligence is the mitigation of uncertainty. Uncertainty is the domain of negative potential (what,where,why,how?) Mitigation is the determinant resolve of any constructive or destructive interference affecting (terminal resolve within) the problem domain. Examples of this may be piled together mountains high, and you may call that functional AGI, though you would be self deceiving. At some point “good enough” may be declared for anything so passing as yourselves.
- erichocean 2y ago> This has been suspected for years, but now there's an experimental result. You would think the whole "split-brain" thing would have been the first clue; apparently not.
- famouswaffles 2y agoYou are getting derailed because of the name we've chosen to call these models but only the first few and last few layers of LLMs deal with tokens. The rest deal with abstract representations and models learnt during training. Language goes in and Language comes out but Language is not the in-between for either LLMs or Humans.
- erichocean 2y ago> So if someone figures out to do this, it will probably take less hardware than an LLM. We have, it's called DreamCoder. There's a paper and everything. Everything needed for AGI exists today, people simply have (incorrect) legacy beliefs about cognition that are holding them back (e.g. "humans are rational"). https://arxiv.org/abs/2006.08381 https://arxiv.org/abs/2006.08381
- visarga 2y ago> No idea how to do this We need to add the 5 senses, of which we have now image, audio and video understanding in LLMs. And for agentic behavior they need environments and social exposure.
- NemoNobody 2y agoThis is actually exactly what is needed. We think the dataset is the primary limitation to an LLMs capability but in reality we are only developing one part of their "intelligence" - a functional and massive model isn't the end of their training - its kinda just the beginning.
- layer8 2y ago> What this tells us for AI is that we need something else besides LLMs. Despite being an LLM skeptic of sorts, I don’t think that necessarily follows. The LLM matrix multiplication machinery may well be implementing an equivalent of the human non-language cognitive processing as a side effect of the training. Meaning, what is separated in the human brain may be mixed together in an LLM.
- mountainriver 2y agoTransformers are just sequence predictors, it doesn’t need to be language, increasingly it’s not
- taeric 2y agoI'm curious why "simulation" isn't the extra thing needed? Yes, we need language to communicate ideas. But you can simulate in your mind things happening that you don't necessarily have words for, yet. Right?
- codebolt 2y ago> What this tells us for AI is that we need something else besides LLMs. An easy conclusion to jump to but I believe we need to be more careful. Nothing in these findings proves conclusively that non-verbal reasoning mechanism equivalent to humans couldn't evolve in some part of a sufficiently large ANN trained on text and math. Even though verbal and non-verbal reasoning occurs in two distinct parts of the brain, it doesn't mean they're not related.
- westurner 2y ago"Language models can explain neurons in language models" https://news.ycombinator.com/item?id=35877402#35886145 https://news.ycombinator.com/item?id=35877402#35886145 : > Recent work has revealed that the neural activity patterns correlated with sensation, cognition, and action often are not stable and instead undergo large scale changes over days and weeks—a phenomenon called representational drift. [...] So, I'm not sure how conclusive this fmri activation study is either. Though, is there a proto language that's not even necessary for the given measured aspects of condition? Which artificial network architecture best approximates which functionally specialized biological neutral networks? OpenCogPrime:KnowledgeRepresentation > Four Types of Knowledge: https://wiki.opencog.org/w/OpenCogPrime:KnowledgeRepresentation https://wiki.opencog.org/w/OpenCogPrime:KnowledgeRepresentat... : > Sensory, Procedural, Episodic, Declarative From https://news.ycombinator.com/item?id=40105068#40107537 https://news.ycombinator.com/item?id=40105068#40107537 re: cognitive hierarchy and specialization : > But FWIU none of these models of cognitive hierarchy or instruction are informed by newer developments in topological study of neural connectivity;