All: please don't comment based on your first response to an inevitably shallow title. That leads to generic discussion, which we're trying to avoid on HN. Specific discussion of what's new or different in an article is a much better basis for interesting conversation.
Since we all have language and opinions about it, the risk of genericness is high with a title like this. It's like this with threads about other universal topics too, such as food or health.
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.
When 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.
This seems quite reasonable, but I recently heard a podcast (https://www.preposterousuniverse.com/podcast/2024/06/24/280-...) that LLMs are more likely to be very good at navigating what they have been trained on, but very poor at abstract reasoning and discovering new areas outside of their training. As a single human, you don't notice, as the training material is greater than everything we could ever learn.
After all, that's what Artificial General Intelligence would at least in part be about: finding and proving new math theorems, creating new poetry, making new scientific discoveries, etc.
> It makes sense that the process of thinking and the process of translating those thoughts into and out of language would be distinct
Yes, indeed. And LLMs seem to be very good at _simulating_ the translation of thought into language. They don't actually do it, at least not like humans do.
> As a single human, you don't notice, as the training material is greater than everything we could ever learn.
This bias is real. Current gen ai works proportionally well the more known it is. The more training data, the better the performance. When we ask something very specific, we have the impression that it’s niche. But there is tons of training data also on many niche topics, which essentially enhances the magic trick – it looks like sophisticated reasoning. Whenever you truly go “off the beaten path”, you get responses that are (a) nonsensical (illogical) and (b) “pulls” you back towards a “mainstream center point” so to say. Anecdotally of course..
I’ve noticed this with software architecture discussions. I would have some pretty standard thing (like session-based auth) but I have some specific and unusual requirement (like hybrid device- and user identity) and it happily spits out good sounding but nonsensical ideas. Combining and interpolating entirely in the the linguistic domain is clearly powerful, but ultimately not enough.
What part of AI today leads you to believe that an AGI would be capable of self directed creativity? Today that is impossible - no AI is truly generating "new" stuff, no poetry is constructed creatively, no images are born from a feeling, inspiration is only part of AI generation is you consider it utilizing it's training data, which isn't actually creativity.
I'm not sure why everyone assumes an AGI would just automatically do creativity considering most people are not very creative, despite them quite literally being capable, most people can't create anything. Why wouldn't an AGI have the same issues with being "awake" that we do? Being capable of knowing stuff - as you pointed out, far more facts than a person ever could, I think an awake AGI may even have more "issues" with the human condition than us.
Also - say an AGI comes into existence that is awake, happy and capable of truly original creativity - why tf does it write us poetry? Why solve world hunger - it doesn't hunger. Why cure cancer - what can cancer do to to it?
AGI as currently envisioned is a mythos of fantasy and science fiction.
Kind of reductive but humans are barely creative at all as you say. Most of what we create is just a rehash of something we've already seen before. Genuinely new and unseen things and experiences are incredibly rare in our reality.
Isn't your first point purely because LLMs are canned models that aren't actively being trained aka inference only? It isn't really a fair comparison considering humans can actively learn/continuous training.
I suppose one could build an LLM around a lora that's being continuously trained to attempt to get it to adapt to new scenarios.
> 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.
Even 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.
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.
> It's not clear to me that LLMs sufficiently scaled won't achieve superhuman performance
To some extent this is true.
To calculate A + B you could for example generate A, B for trillions of combinations and encode that within the network. And it would calculate this faster than any human could.
But that's not intelligence. And Apple's research showed that LLMs are simply inferring relationships based on the tokens it has access to. Which you can throw off by adding useless information or trying to abstract A + B.
> To calculate A + B you could for example generate A, B for trillions of combinations and encode that within the network. And it would calculate this faster than any human could.
I don't feel like this is a very meaningful argument because if you can do that generation then you must already have a superhuman machine for that task.
Chess is essentially a puzzle. There's a single explicit, quantifiable goal, and a solution either achieves the goal or it doesn't.
Solving puzzles is a specific cognitive task, not a general one.
Language is a continuum, not a puzzle. The problem with LLMs is that testing has been reduced to performance on language puzzles, mostly with hard edges - like bar exams, or letter counting - and they're a small subset of general language use.
Sure, when humans use multiple skill to address a specific problem, you can sometimes outperform them by scaling a spefic one of those skills.
When it comes to general intelligence, I think we are trying to run before we can walk. We can't even make a computer with a basic, animal level understanding of the world. Yet we are trying to take a tool that was developed on top of system that already had an understanding of the world and use it to work backwards to give computers an understanding of the world.
I'm pretty skeptical that we're going to succeed at this. I think you have to be able to teach a computer to climb a tree or hunt (subhuman AGI) before you can create superhuman AGI.
I’ll pop in with a friendly “that research is definitely wrong”. If they want to prove that LLMs can’t reason, shouldn’t they stringently define that word somewhere in their paper? As it stands, they’re proving something small (some of today’s LLMs have XYZ weaknesses) and claiming something big (humans have an ineffable calculator-soul).
LLMs absolutely 100% can reason, if we take the dictionary definition; it’s trivial to show their ability to answer non-memorized questions, and the only way to do that is some sort of reasoning. I personally don’t think they’re the most efficient tool for deliberative derivation of concepts, but I also think any sort of categorical prohibition is anti-scientific. What is the brain other than a neural network?
Even if we accept the most fringe, anthropocentric theories like Penrose & Hammerhoff’s quantum tubules, that’s just a neural network with fancy weights. How could we possibly hope to forbid digital recreations of our brains from “truly” or “really” mimicking them?
Can they reason, or is the volume of training data sufficient for them to match relationships up to appropriate expressions?
Basically, if humans have had meaningful discussions about it, the product of their reasoning is there for the LLM, right?
Seems to me, the "how many R's are there in the word "strawberry" problem is very suggestive of the idea LLM systems cannot reason. If they could, the question is not difficult.
The fact is humans may never have actually discussed that topic in any meaningful way captured in the training data.
And because of that and how specific the question is, the LLM has no clear relationships to map into a response. It just does best case, whatever the math deemed best.
Seems plausible enough to support the opinion LLM'S cannot reason.
What we do know is LLMs can work with anything expressed in terms of relationships between words.
There is a ton of reasoning templates contained in that data.
Put another way:
Maybe LLM systems are poor at deduction, save for examples contained in the data. But there are a ton of examples!
So this is hard to notice.
Maybe LLM systems are fantastic at inference! And so those many examples get mapped to the prompt at hand very well.
And we do notice that and see it like real thinking, not just some horribly complex surface containing a bazillion relationships...
The “how many R’s are in the word strawberry?” problem can’t be solved by LLMs specifically because they do not have access to the text directly. Before the model sees the user input it’s been tokenized by a preprocessing step. So instead of the string “strawberry”, the model just sees an integer token the word has been mapped to.
I've tried it out and found that some models can answer the question if it's phrased right. And pretty much all models get it right if you also spell it out letter by letter to solve the problem you pointed out.
Yeah, it does teach me more about how LLMs work on the inside when it can't answer a plain English logic question like that, but I can provide it a code example and it can execute it step by step and get a correct answer; it's clearly been trained on enough JS that even a complex reduce + arrow function I watched kunoichi (am RP model nonetheless!) imaginary execute it step by step and arrive at a correct answer.
I think it's something like the counting parts of problems that current models are shaky with, and I imagine it's a training data problem.
> Even if we accept the most fringe, anthropocentric theories like Penrose & Hammerhoff’s quantum tubules, that’s just a neural network with fancy weights.
First, while it is a fringe idea with little backing it, it's far from the most fringe.
Secondly, it is not at all known that animal brains are accurately modeled as an ANN, any more so than any other Turing-compatible system can be modeled as an ANN. Biological neurons are themselves small computers, like all living cells in general, with not fully understood capabilities. The way biological neurons are connected is far more complex than a weight in an ANN. And I'm not talking about fantasy quantum effects in microtubules, I'm talking about well-established biology, with many kinds of synapses, some of which are "multicast" in a spatially distinct area instead of connected to specific neurons. And about the non-neuronal glands which are known to change neuron behavior and so on.
How critical any of these differences are to cognition is anyone's guess at this time. But dismissing them and reducing the brain to a bigger NN is not wise.
There's a lot of other interesting biology besides propagation of electrical signals. Examples include: 1/ Transport of mRNAs (in specialized vesicle structures!) between neurons. 2/ Activation and integration of retrotransposons during brain development (which I have long hypothesized acts as a sort of randomization function for the neural field). 3/ Transport of proteins between and within neurons. This isn't just adventitious movement, either - neurons have a specialized intracellular transport system that allows them to deliver proteins to faraway locations (think >1 meters).
It is my understanding that Penrose doesn’t claim that brains are needed for cognition, just that brains are needed for a somewhat nebulous „conscious experience“, which need not have any observable effects. I think that it’s fairly uncontroversial that a machine can produce behavior that is indistinguishable from human intelligence over some finite observation time. The Chinese room speaks Chinese, even if it lacks understanding for some definitions of the term.
But conscious experience does produce observable effects.
For that not to be the case, you'd have to take the position that humans experience consciousness and they talk about consciousness but that there is no causal link between the two! It's just a coincidence that the things you find yourself saying about consciousness line up with your internal experience?
That philosophers talk about p-zombies seems like evidence to me that at least some of them don't believe that consciousness needs to have observable effects that can't be explained without consciousness. I don't say that I believe that too. I don't believe that there is anything particularly special about brains.
The p-zombie argument is the best-known of a group of conceivability arguments, which ultimately depend on the notion that if a proposition is conceivably true, then there is a metaphysically possible world in which it is true. Skeptics suppose that this is just a complicated way of equivocating over what 'conceivable' means, and even David Chalmers, the philosopher who has done the most to bring the p-zombie argument to wide attention, acknowledges that it depends on the assumption of what he calls 'perfect conceivability', which is tantamount to irrefutable knowledge.
To deal with the awkwardly apparent fact that consciousness certainly seems to have physical effects, zombiephiles challenge the notion that physics is causally closed, so that it is conceivable that something non-physical can cause physical effects. Their approach is to say that the causal closure of physics is not provable, but at this point, the argument has become a lexicographical one, about the definition of the words 'physics' and 'physical' (if one insists that 'physical' does not refer to a causally-closed concept, then we still need a word for the causal closure within which the physical is embedded - but that's just what a lot of people take 'physical' to mean in the first place.) None of the anti-physicalists have been able, so far, to shed any light on how the mind is causally effective in the physical world.
Like what is magic - it turns out to be the ability to go from interior thoughts to stuff happening in the shared world - physics is just the mechanism of the particular magical system we have.
The brain is faster than the Chinese room, but other than that, yes, that's the so-called systems reply; Searle's response to it (have the person in the room memorize the instruction book) is beside the point, as you can teach people to perform all sorts of algorithms without them needing to understand the result.
As many people have pointed out, Searle's argument begs the question by tacitly assuming that if anything about the room understands Chinese, it can only be the person within it.
> LLMs absolutely 100% can reason, if we take the dictionary definition; it’s trivial to show their ability to answer non-memorized questions, and the only way to do that is some sort of reasoning.
Um... What? That is a huge leap to make.
'Reasoning' is a specific type of thought process and humans regularly make complicated decisions without doing it. We uses hunches and intuition and gut feelings. We make all kinds of snap assessments that we don't have time to reason through. As such, answering novel questions doesn't necessarily show a system is capable of reasoning.
I see absolutely nothing resumbling an argument for humans having an "ineffable calculator soul", I think that might be you projecting. There is no 'categorical prohibition', only an analysis of the current flaws of specific models.
Personally, my skepticism about imminent AGI has to do believing we may be underestimating the complexity of the software running on our brain. We've reached the point where we can create digital "brains", or atleast portions of them. We may be missing some other pieces of a digital brain, or we may just not have the right software to run on it yet. I suspect it is both but that we'll have fully functional digital brains well before we figure out the software to run on them.
All well said, and I agree on many of your final points! But you beautifully highlighted my issue at the top:
'Reasoning' is a specific type of thought process
If so, what exactly is it? I don’t need a universally justified definition, I’m just looking for an objective, scientific one. A definition that would help us say for sure that a particular cognition is or isn’t a product of reason.
I personally have lots of thoughts on the topic and look to Kant and Hegel for their definitions of reason as the final faculty of human cognition (after sensibility, understanding, and judgement), and I even think there’s good reason (heh) to think that LLMs are not a great tool for that on their own. But my point is that none of the LLM critics have a definition anywhere close to that level of specificity.
Usually, “reason” is used to mean “good cognition”, so “LLMs can’t reason” is just a variety of cope/setting up new goalposts. We all know LLMs aren’t flawless or infinite in their capabilities, but I just don’t find this kind of critique specific enough to have any sort of scientific validity. IMHO
> don’t need a universally justified definition, I’m just looking for an objective, scientific one. A definition that would help us say for sure that a particular cognition is or isn’t a product of reason.
Unfortunately, you won't get one. We simply don't know enough about cognition to create rigourous definitions of the type you are looking for.
Instead, this paper, and the community in general are trying to perform practical capability assessments. The claim that the GSM8k measures "mathematical reasoning" or "logical reasoning" didn't come from the skeptics.
Alan Turring didn't try to define intelligence, he created a practical test that he thought would be a good benchmark. These days we believe we have better ones.
> I just don’t find this kind of critique specific enough to have any sort of scientific validity. IMHO
"Good cognition" seems like dismisal of a definition, but this is exactly the definition that the people working on this care about. They are not philosphers, they are engineers who are trying to make a system "better" so "good cognition" is exactly what they want.
The paper digs into finding out more about what types of changes impacts peformance on established metrics. The "noop" result is pretty interesting since "relevancy detection" isn't something we commonly think of as key to "good cognition", but a consequence of it.
I feel you are putting too much emphasis on the importance and primacy of having a definition of words like 'reasoning'.
As humanity has struggled to understand the world, it has frequently given names to concepts that seem to matter, well before it is capable of explaining with any sort of precision what these things are, and what makes them matter - take the word 'energy', for example.
It seems clear to me that one must have these vague concepts before one can begin to to understand them, and also that it would be bizarre not to give them a name at that point - and so, at that point, we have a word without a locked-down definition. To insist that we should have the definition locked down before we begin to investigate the phenomenon or concept is precisely the wrong way to go about understanding it: we refine and rewrite the definitions as a consequence of what our investigations have discovered. Again, 'energy' provides a useful case study for how this happens.
A third point about the word 'energy' is that it has become well-defined within physics, and yet retains much of its original vagueness in everyday usage, where, in addition, it is often used metaphorically. This is not a problem, except when someone makes the lexicographical fallacy of thinking that one can freely substitute the physics definition into everyday speech (or vice-versa) without changing the meaning.
With many concepts about the mental, including 'reasoning', we are still in the learning-and-writing-the-definition stage. For example, let's take the definition you bring up: reasoning as good cognition. This just moves us on to the questions of what 'cognition' means, and what distinguishes good cognition from bad cognition (for example, is a valid logical argument predicated on what turns out to be a false assumption an example of reasoning-as-good-cognition?) We are not going to settle the matter by leafing through a dictionary, any more than Pedro Carolino could write a phrase book just from a Portugese-English dictionary (and you are probably aware that looking up definitions-of-definitions recursively in a dictionary often ends up in a loop.)
A lot of people want to jump the gun on this, and say definitively either that LLMs have achieved reasoning (or general intelligence or a theory of mind or even consciousness, for that matter) or that they have not (or cannot.) What we should be doing, IMHO, is to put aside these questions until we have learned enough to say more precisely what these terms denote, by studying humans, other animals, and what I consider to be the surprising effectiveness of LLMs - and that is what the interviewee in the article we are nominally discussing here is doing.
You entered this thread by saying (about the paper underlying an article in Ars Tech [1]) I’ll pop in with a friendly “that research is definitely wrong”. If they want to prove that LLMs can’t reason..., but I do not think there is anything like that claim in the paper itself (one should not simply trust what some person on HN says about a paper. That, of course, goes as much for what I say about it as what the original poster said.) To me, this looks like the sort of careful, specific and objective work that will lead to us a better understanding of our concepts of the mental.
This is one of my favorite comments I've ever read on HN.
The first three paragraphs you wrote very succinctly and obviously summarize the fundamental flaw of our modern science - that it can't make leaps, at all.
There is no leap of faith in science but there is science that requires such leaps.
We are stuck bc those most capable of comprehending concepts they don't understand and are unexplainable - they won't allow themselves to even develop a vague understanding of such concepts. The scientific method is their trusty hammer and their faith in it renders all that isn't a nail unscientific.
Admitting that they don't kno enough would be akin to societal suicide of their current position - the deciders of what is or isn't true, so I don't expect them to withhold their conclusions til they are more able to.
They are the "priest class" now ;)
I agree with your humble opinion - there is much more we could learn if that was our intent and considering the potential of this, I think we absolutely ought to make certain that we do everything in our power to attain the best possible outcomes of these current and future developments.
Transparent and honest collaboration for the betterment of humanity is the only right path to an AGI god - to oversimplify a lil bit.
Very astute, well formulated position, presented in accessible language and with humility even!
Chasing our own tail with concepts like "reasoning". Let's move the concept a bit - "search". Can LLMs search for novel ideas and discoveries? They do under the right circumstances. You got to provide idea testing environments, the missing ingredient. Search and learn, it's what humans do and AI can do as well.
The whole issue with "reasoning" is that is an incompletely defined concept. Over what domain, what problem space, and what kind of experimental access do we define "reasoning"? Search is better as a concept because it comes packed with all these things, and without conceptual murkiness. Search is scientifically studied to a greater extent.
I don't think we doubt LLMs can learn given training data, we already accuse them of being mere interpolators or parrots. And we can agree to some extent the LLMs can recombine concepts correctly. So they got down the learning part.
And for the searching part, we can probably agree its a matter of access to the search space not AI. It's an environment problem, and even a social one. Search is usually more extended than the lifetime of any agent, so it has to be a cultural process, where language plays a central role.
When you break reasoning/progress/intelligence into "search and learn" it becomes much more tractable and useful. We can also make more grounded predictions on AI, considering the needs for search that are implied, not just the needs for learning.
How much search did AlphaZero need to beat us at go? How much search did humans pack in our 200K years history over 10,000 generations? What was the cost of that journey of search? That kind of questions. In my napkin estimations we solved 1:10000 of the problem by learning, search is 10000x to a million times harder.
It says "current" LLMs can't "genuinely" reason. Also, one of the researchers then posted an internship for someone to work on LLM reasoning.
I think the paper should've included controls, because we don't know how strong the result is. They certainly may have proven that humans can't reason either.
If they had human controls, they might well show that some humans can’t do any better, but based on how they generated test cases, it seems unlikely to me that doing so would prove that humans cannot reason (of course, if that’s actually the case, we cannot trust ourselves to devise, execute and interpret these tests in the first place!)
Some people will use any limitation of LLMs to deny there is anything to see here, while others will call this ‘moving the goalposts’, but the most interesting questions, I believe, involve figuring out what the differences are, putting aside the question of whether LLMs are or are not AGIs.
While I generally do suspect that we need to invent some new technique in the realm of AI in order for software to do everything a human can do, I use analogies like chess engines to caution myself from certainty.
Most 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.
> 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.
Based on my experience with toddlers, a rather smart dog, and my own thought processes, I disagree that language is a fundamental component of abstraction. Of sharing abstractions, sure, but not developing them.
When I'm designing a software system I will have a mental conception of the system as layered abstractions before I have a name for any component. I invent names for these components in order to define them in the code or communicate them to other engineers, but the intuition for the abstraction comes first. This is why "naming things" is one of the hard problems in computer science—because the name comes second as a usually-inadequate attempt to capture the abstraction in language.
The conception here is that one's layered abstractions is basically an informal mathematics... which is formally structured... which is a formal grammar. It's your internal language, using internal symbols instead of English names.
Remember in CS theory, a language is just a set of strings. If you think in pictures that is STILL a language if your pictures are structured.
So I'm really handwaving the above just to suggest that it all depends on the assumptions that each expert is making in elucidating this debate which has a long history.
> conception here is that one's layered abstractions is basically an informal mathematics... which is formally structured... which is a formal grammar. It's your internal language, using internal symbols instead of English names
Unless we're getting metaphysical to the point of describing quantum systems as possessig a language, there are various continuous analog systems that can compute without a formal grammar. The language system could be the one that thinks in discrete 'tokens'; the conscious system something more complex.
That's based on a well known fallacy, because analog models cannot exceed the computational power of Turing machines. The alternative position is Penrose who thinks quantum tubules are responsible for consciousness and thus somehow more powerful than TMs.
Good thing Computability Beyond Church-Turing via Choice Sequences[1] exists.
[1] Mark Bickford, Liron Cohen, Robert L. Constable, and Vincent Rahli. 2018. Computability Beyond Church-Turing via Choice Sequences. In Proceedings of the 33rd Annual ACM/IEEE Symposium on Logic in Computer Science (LICS '18). Association for Computing Machinery, New York, NY, USA, 245–254. https://doi.org/10.1145/3209108.3209200
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
I think it’s not entirely accurate to say that we “learn” to walk from a zero state. It’s clear that our DNA has embedded knowledge of how to walk and it develops our body appropriately. Our brains might also have preconditioning to make learning to walk much easier.
Music or sports are more interesting to investigate (in my opinion) since those specific actions won’t be preprogrammed and must be learned independently.
The same way we build abstractions for language in order to perform “telepathy” it seems like for music or sports we build body-specific abstractions. They work similar to words within our own brain but are not something easily communicated since they’re not tied to any language, it’s just a feeling.
I think it’s an interesting point that quite often the best athletes or musicians are terrible coaches. They probably have a much more innate internal language for their body that cannot be communicated easily. Partially, I think, that their body is more different than others which helps them be exceptional. Or that weaker athletes or musicians need to focus much more on lessons from others, so their body language gets tied much closer to human language and that makes it much easier for them to then communicate the lessons they learn to others.
I don't think motor skills are a good object to use in an argument about verbal vs non-verbal thinking. We have large regions of our brains primarily dedicated to motor skills and you can't argue that humans are any more talented or capable at controlling our bodies than other animals, we're actually rather poor performers in this area. You're right to say that you aren't conscious of the very highly trained movements you are making because they likely have only a tenuous connection with any part of your brain that we would recognize as possessing consciousness or thought, they are mostly learned reflexes and responses to internal and external stimuli at this point like a professional baseball player who can automatically catch a ball flying at him before he's even aware of it.
> the "end to end black box" approach is perhaps misguided, because the result will be a non transparent system by definition
A black box that works in human language and can be investigated with perturbations, embedding visualizations and probes. It explains itself as much ore more than we can.
One reason might that LLMs are successful because of the architecture, but also, just as importantly because they can be trained over a volume and diversity of human thought that’s encapsulated in language (that is on the internet). Where are we going to find the equivalent data set that will train this other kind of thinking?
Open AI O1 seems to be trained on mostly synthetic data, but it makes intuitive sense that LLMs work so well because we had the data lying around already.
> One reason might that LLMs are successful because of the architecture, but also, just as importantly because they can be trained over a volume and diversity of human thought that’s encapsulated in language (that is on the internet). Where are we going to find the equivalent data set that will train this other kind of thinking?
Probably by putting simulated animals into simulated environments where they have to survive and thrive.
Working at animal level is uncool, but necessary for progress. I had this argument with Rod Brooks a few decades back. He had some good artificial insects, and wanted to immediately jump to human level, with a project called Cog.[1] I asked him why he didn't go for mouse level AI next. He said "Because I don't want to go down in history as the inventor of the world's greatest artificial mouse."
Cog was a dud, and Brooks goes down in history as the inventor of the world's first good robotic vacuum cleaner.
"Where are we going to find the equivalent data set that will train this other kind of thinking?"
Just a personal opinion, but in my shitty When H.A.R.L.I.E. Was One (and others) unpublished fiction pastiche (ripoff, really), I had the nascent AI stumble upon Cyc as its base for the world and "thinking about how to think."
I never thought that Cyc was enough, but I do think that something Cyc-like is necessary as a component, a seed for growth, until the AI begins to make the transition from the formally defined, vastly interrelated frames and facts in Cyc to being able to growth further and understand the much less formal knowledgebase you might find in, say Wikipedia.
Full agreement with your animal model is only sensible. If you think about macaques, they have a limited range of vocalization once they hit adulthood. Noe that the mothers almost never make a noise at their babies. Lacking language, when a mother wants to train an infant, she hurts it. (Shades of Blindsight there) She picks up the infant, grasps it firmly, and nips at it. The baby tries to get away, but the mother holds it and keeps at it. Their communication is pain. Many animals do this. But they also learn threat displays, the promise of pain, which goes beyond mere carrot and stick.
The more sophisticated multicellular animals (let us say birds, reptiles, mammals) have to learn to model the behavior of other animals in their environment: to prey on them, to avoid being prey. A pond is here. Other animals will also come to drink. I could attack them and eat them. And with the macaques, "I must scare the baby and pain it a bit because I no longer want to breastfeed it."
Somewhere along the line, modeling other animals (in-species or out-species) hits some sort of self-reflection and the recursion begins. That, I think, is a crucial loop to create the kind of intelligence we seek. Here I nod to Egan's Diaspora.
Looping back to your original point about the training data, I don't think that loop is sufficient for an AGI to do anything but think about itself, and that's where something like Cyc would serve as a framework for it to enter into the knowledge that it isn't merely cogito ergo summing in a void, but that it is part of a world with rules stable enough that it might reason, rather than "merely" statistically infer. And as part of the world (or your simulated environment), it can engage in new loops, feedback between its actions and results.
> A pond is here. Other animals will also come to drink. I could attack them and eat them.
Is that the dominant chain, or is the simpler “I’ve seen animals here before that I have eaten” or “I’ve seen animals I have eaten in a place that smelled/looked/sounded/felt like this” sufficient to explain the behavior?
Could be! But then there are ambushes, driving prey into the claws of hidden allies, and so forth. Modeling the behavior of other animals will have to occur without place for many instances.
I think the data is way more important for the success of LLMs than the architecture although I do think there's something important in the GPT architecture in particular. See this talk for why: [1]
Warning, watch out for waving hands: The way I see it is that cognition involves forming an abstract representation of the world and then reasoning about that representation. It seems obvious that non-human animals do this without language. So it seems likely that humans do too and then language is layered on top as a turbo boost. However, it also seems plausible that you could build an abstract representation of the world through studying a vast amount of human language and that'll be a good approximation of the real-world too and furthermore it seems possible that reasoning about that abstract representation can take place in the depths of the layers of a large transformer. So it's not clear to me that we're limited by the data we have or necessarily need a different type of data to build a general AI although that'll likely help build a better world model. It's also not clear that an LLM is incapable of the type of reasoning that animals apply to their abstract world representations.
I agree we are not limited with the data set size: all humans learn the language with the much smaller language training set (just look at kids and compare them to LLMs).
OTOH, humans (and animals) do get other data feeds (visual, context, touch/pain, smell, internal balance "sensors"...) that we develop as we grow and tie that to learning about language.
Obviously, LLMs won't replicate that since even adults struggle to describe these verbally.
> However, it also seems plausible that you could build an abstract representation of the world through studying a vast amount of human language and that'll be a good approximation of the real-world too and furthermore it seems possible that reasoning about that abstract representation can take place in the depths of the layers of a large transformer.
While I agree this is possible, I don't see why you'd think it's likely. I would instead say that I think it's unlikely.
Human communication relies on many assumptions of a shared model of the world that are rarely if ever discussed explicitly, and without which certain concepts or at least phrases become ambiguous or hard to understand.
GP argument seems to be about "thinking" when restricted to knowledge through language, and "possible" is not the same as "likely" or "unlikely" — you are not really disagreeing, since either means "possible".
GP said plausible, which does mean likely. It's possible that there's a teapot in orbit around Jupiter, but it's not plausible. And GP is specifically saying that by studying human language output, you could plausibly learn about the world that have birth to the internal models that language is used to exteriorize.
If we are really nitpicking, they said it's plausible you could build an abstract representation of the world by studying language-based data, but that it's possible it could be made to effectively reason too.
Anyway, it seems to me we are generally all in agreement (in this thread, at least), but are now being really picky about... language :)
Videos are a rich set of non verbal data that could be used to train AIs.
Feed it all the video ever recorded, hook it up to web cams, telescopes, etc. This says a lot about how the universe works, without using a single word.
I always start with God’s design thinking it is best. That’s our diverse, mixed-signal, brain architecture followed by a good upbringing. That means we need to train brain-like architectures in the same way we train children. So, we’ll need whatever data they needed. Multiple streams for different upbringings, too.
The data itself will be most senses collecting raw data about the world most of the day for 18 years. It might require a camera on the kid’s head which I don’t like. I think people letting a team record their life is more likely. Split the project up among many families running in parallel, 1-4 per grade/year. It would probably cost a few million a year.
(Note: Parent changes might require an integration step during AI training or showing different ones in the early years.)
The training system would rapidly scan this information in. It might not be faster than human brains. If it is, we can create them quickly. That’s the passive learning part, though.
Human training involves asking lots of questions based on internal data, random exploration (esp play) with reinforcement, introspection/meditation, and so on. Self-driven, generative activities whose outputs become inputs into the brain system. This training regiment will probably need periodic breaks from passive learning to ask questions or play which requires human supervision.
Enough of this will probably produce… disobedient, unpredictable children. ;) Eventually, we’ll learn how to do AI parenting where the offspring are well-behaved, effective servants. Those will be fine-tuned for practical applications. Later, many more will come online which are trained by different streams of life experience, schooling methods, etc.
That was my theory. I still don’t like recording people’s lives to train AI’s. I just thought it was the only way to build brain-like AI’s and likely to happen (see Twitch).
My LLM concept was to do the same thing with K-12 education resources, stories, kids games, etc. Parents already could tell us exactly what to use to gradually build them up since they did that for their kids year by year. Then, several career tracts layering different college books and skill areas. I think it would be cheaper than GPT-4 with good performance.
Language models would seem to be exquisitely tied to the way that evolved intelligence has formulated its society and training.
An Ab Initio AGI would maybe be free of our legacy, but LLMs certainly are not.
I would expect a ship-like intelligence a la the Culture novels to have non-English based cognition. As far as we can tell, our own language generation is post-hoc explanation for thought more so than the embodiment of thought.
Title doesn't mean bullet trains can't fly, but do imply what call flights could be more than moving fast, and effects of wings might be worth discussing.
Okay, 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?
Agreed: Perhaps we aught to be studying cognition of creatures without spines before we claim to replicate or understand cognition of creatures with them.
Presumably they have some sort biological input processing or sensory inputs. They don't eat data.
in the high entropy world we have, we are forced to assume that the first thing that arises as a stable pattern is inevitably the most likely, and the most likely to work. there is no other pragmatic conclusion to draw.
You 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.
>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.
Brain 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.
Don’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.
Let'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.
> 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.
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.
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.
> 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.
It is very naïve to think that the availability of such tools isn't partly responsible for that intelligence; “We shape our tools and thereafter our tools shape us”. And it seems too man-centric of an excuse: you can see all our civilization being built on hands so you state that there can't be a way without.
The "they MIGHT be as intelligent, just lacking hands" theory can't have the same weight as "nah" in an honest mind seeing the overwhelming clues (yes, not proof, if that's what you want) against it. Again, same way that you can't disprove solipsism.
Dolphins, 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.
Clearly 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.
>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.
Another angle to look at intelligence is that not all species need it, or need it to same degree. If you are a cow, or a crocodile, then you are a 1-trick grass-munching or zebra-munching pony, and have no need for intelligence. A generalist species like humans, that lives in a hugely diverse set of environments, with a hugely diverse set of food sources, has evolved intelligence (which in turn supports further generalization) to cope with this variety.
At least to our own perception, and degree of understanding, it would appear that the ocean habitat(s) of dolphins are far less diverse and demanding. Evidentially complex enough to drive their intelligence though, so perhaps we just don't understand the complexity of what they've evolved to do.
Evolution is a blind, dumb optimizer. You can have a mutation that is over-kill and if it doesn't actively impede you in some way, it just stays. It's not like it goes, "Ok we need to reduce this to the point where it's just beneficial enough etc".
That said, i definitely would not say the Ocean is particularly less diverse or demanding.
Even with our limited understanding, there must be adaptations for Pressure, Salinity, light, Energy, Buoyancy, Underwater Current etc that all vary significantly by depth and location.
And the bottlenose dolphin for instance lives in every ocean of the world except the Arctic and the Antarctic oceans.
Right, 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.
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.
Agreed. 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.
I'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.
Is 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.
Before 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.
It'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.
You 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.
I think you may underestimate what these models do.
Proper multimodal models natively consider whatever input you give them, store the useful information in an abstracted form (i.e not just text), building it's world model, and then output in whatever format you want it to.
It's no different to a mammals, just the inputs are perhaps different. Instead of relying on senses, they rely on text, video, images and sound.
In theory you could connect it to a robot and it could gather real world data much like a human, but would potentially be limited to the number of sensors/nerves it has. (on the plus side it has access to all recorded data and much faster read/write than a human).
You 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.
Well 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.
I think one big problem is that people understand LLMs as text-generation models, when really they're just sequence prediction models, which is a highly versatile, but data-hungry, architecture for encoding relationships and knowledge. LLMs are tuned for text input and output, but they just work on numbers and the general transformer architecture is highly generalizable.
Chomsky is shockingly unhumble. I admire him but he's a jerk who treats people who disagree with him with contempt. It's fun to read him doing this but it's uncollegiate (to say the least).
Also, calling "generative grammar" productive seems wrong to me. It's been around for half a century -- what tools has it produced? At some point theory needs to come into contact with empirical reality. As far as I know, generative grammar has just never gotten to this point.
Well, it’s the basis of programming languages. That seems pretty helpful :) Otherwise it’s hard to measure what exactly “real world utility” looks like. What have the other branches of linguistics brought us? What has any human science brought us, really? Even the most empirical one, behavioral psychology, seems hard to correlate with concrete benefits. I guess the best case would be “helps us analyze psychiatric drug efficacy”?
Generally, I absolutely agree that he is not humble in the sense of expressing doubt about his strongly held beliefs. He’s been saying pretty much the same things for decades, and does not give much room for disagreement (and ofc this is all ratcheted up in intensity in his political stances). I’m using humble in a slightly different way, tho: he insists on qualifying basically all of his statements about archaeological anthropology with “we don’t have proof yet” and “this seems likely”, because of his fundamental belief that we’re in a “pre-Galilean” (read: shitty) era of cognitive science.
In other words: he’s absolutely arrogant about his core structural findings and the utility of his program, but he’s humble about the final application of those findings to humanity.
It's a fair point that Chomsky's ideas about grammars are used in parsing programming languages. But linguistics is supposed to deal with natural languages -- what has Chomskyan linguistics accomplished there?
Contrast to the statistical approach. It's easy to point to something like Google translate. If Chomsky's approach gave us a tool like that, I'd have no complaint. But my sense is that it just hasn't panned out.
Nobody, people are just crying because Chomsky calls them out, rationally, on their intellectual and/or political bullshit, and this behavior is known as projection.
In these discussions, I always knee-jerk into thinking "why don't they just look inward on their own minds". But the truth is, most people don't have much to gaze upon internally... they're the meat equivalent of an LLM that can sort of sound like it makes sense. These are the people always bragging about how they have an "internal monologue" and that those that don't are aliens or psychotics or something.
The only reason humans have that "communication model" is because that's how you model other humans you speak to. It's a faculty for rehearsing what you're going to say to other people, and how they'll respond to it. If you have any profound thoughts at all, you find that your spoken language is deficient to even transcribe your thoughts, some "mental tokens" have no short phrases that even describe them.
The only real thoughts you have are non-verbal. You can see this sometimes in stupid schoolchildren who have learned all the correct words to regurgitate, but those never really clicked for them. The mildly clever teachers always assume that if they thoroughly practice the terminology, it will eventually be linked with the concepts themselves and they'll have fully learned it. What's really happening is that there's not enough mental machinery underneath for those words to ever be anything to link up with.
This view represents one possible subjective experience of the world. But there are many different possible ways a human brain can learn to experience the world.
I am a sensoral thinker, I often think and internally express myself in purely images or sounds. There are, however, some kinds of thoughts I've learned I can only fully engage with if I speak to myself out loud or at least inside of my head.
The most appropriate mode of thought depends upon the task at hand. People don't typically brag about having internal monologues. They're just sharing their own subjective internal experience, which is no less valid than a chiefly nonverbal one.
What? I used the term "sensoral" after thinking about what I wanted to communicate. I have no idea if that is a pop psychology term, I didn't google it. I was attempting to communicate that I often think in visual, aural, tactile or olfactory modes, not just visually or via inner monologue, especially when recalling memories.
You're just projecting at this point and stalking previous comments to start arguments. That is exceedingly immature and absolutely against Hacker News guidelines. You need to reevaluate your behavior. Please refrain from continuing to start arguments on previous posts.
As far as I understand it, it's just output and speaking is just enclosed in tags, that the body can act on, much like inline code output from an LLM.
e.g. the neural electrochemical output has a specific sequence that triggers the production of a certain hormone in your pituitary gland for e.g. and the hormone travels to the relevant body function activating/stopping it.
LLM 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.
I 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.
They don’t wonder. They’d happily produce entire novels of (garbage) text if trained on gibberish. They wouldn’t be confused. They wouldn’t hope to puzzle out the meaning. There is none, and they work just fine anyway. Same for real language. There’s no meaning, to them (there’s not really a “to” either).
The most interesting thing about LLMs is probably how much relational information turns out to be encoded in large bodies of our writing, in ways that fancy statistical methods can access. LLMs aren’t thinking, or even in the same ballpark as thinking.
>What this tells me is there is clearly no “reasoning” happening whatsoever with either model, despite marketing claiming as such.
Not true. You yourself have failed at reasoning here.
The problem with your logic is that you failed to identify the instances where LLMs have succeeded with reasoning. So if LLMs both fail and succeed it just means that LLMs are capable of reasoning and capable of being utterly wrong.
It's almost cliche at this point. Tons of people see the LLM fail and ignore the successes then they openly claim from a couple anecdotal examples that LLMs can't reason period.
Like how is that even logical? You have contradictory evidence therefore the LLM must be capable of BOTH failing and succeeding in reason. That's the most logical answer.
Success doesn’t imply that “reasoning” was involved, and the definition of reasoning is extremely important.
Apple’s recent research summarized here [0] is worth a read. In short, they argue that what LLMs are doing is more akin to advanced pattern recognition than reasoning in the way we typically understand reasoning.
By way of analogy, memorizing mathematical facts and then correctly recalling these facts does not imply that the person actually understands how to arrive at the answer. This is why “show your work” is a critical aspect of proving competence in an education environment.
An LLM providing useful/correct results only proves that it’s good at surfacing relevant information based on a given prompt. That fact that it’s trivial to cause bad results by making minor but irrelevant changes to a prompt points to something other than a truly reasoned response, i.e. a reasoning machine would not get tripped up so easily.
You’re still suffering from the biases of the parent poster. You are picking and choosing papers that illustrate failure instances when there are also an equal amount of papers that verify successful instances.
It’s bloody obvious that when I classify success I mean that the llm is delivering a correct and unique answer for a novel prompt that doesn’t exist in the original training set. No need to go over the same tired analogies that have been regurgitated over and over again that you believe LLMs are reusing memorized answers. It’s a stale point of view. The overall argument has progressed further then that and we now need more complicated analysis of what’s going on with LLMs
And these two are just from a random google search.
I can find dozens and dozens of papers illustrating failures and successes of LLMs which further nails my original point. LLMs both succeed and fail at reasoning.
The main problem right now is that we don’t really understand how LLMs work internally. Everyone who claims they know LLMs can’t reason are just making huge leaps of irrational conclusions because not only does their conclusion contradict actual evidence but they don’t even know how LLMs work because nobody knows.
We only know how LLMs work at a high level and we only understand these things via the analogy of a best fit curve in a series of data points. Below this abstraction we don’t understand what’s going on.
> The main problem right now is that we don’t really understand how LLMs work internally.
Right, and this is why claims that models are “reasoning” can’t be taken at face value. This space is filled with overloaded terms and anthropomorphic language that describes some behavior of the LLM but this doesn’t justify a leap to the belief that these terms actually represent the underlying functionality of the model, e.g. when terms like “hallucinate”, “understand”, etc. are used, they do not represent the biological processes these ideas stem from or carry the implications of a system that mimics those processes.
> Everyone who claims they know LLMs can’t reason are just making huge leaps of irrational conclusions because not only does their conclusion contradict actual evidence but they don’t even know how LLMs work because nobody knows.
If you believe this to be true, you must then also accept that it’s equally irrational to claim these models are actually “reasoning”. The point of citing the Apple paper was that there’s currently a lack of consensus and in some cases major disagreement about what is actually occurring behind the scenes.
Everything you’ve written to justify the idea that reasoning is occurring can be used against the idea that reasoning is occurring. This will continue to be true until we gain a better understanding of how these models work.
The reason the Apple paper is interesting is because it’s some of the latest writing on this subject, and points at inconvenient truths about the operation of these models that at the very least would indicate that if reasoning is occurring, it’s extremely inconsistent and unreliable.
No need to be combative here - aside from being against HN guidelines, there just isn’t enough understanding yet for anyone to be making absolute claims, and the point of my comment was to add counterpoints to a conversation, not make some claim about the absolute nature of things.
>If you believe this to be true, you must then also accept that it’s equally irrational to claim these models are actually “reasoning”.
If a novel low probability conclusion that is correct was arrived at from a novel prompt where neither the prompt nor the conclusion existed in the training set, THEN by logic the ONLY possible way the conclusion was derived was through reasoning. We know this, but we don't know HOW the model is reasoning.
The only other possible way that an LLM can arrive at low probability conclusions is via random chance.
>The point of citing the Apple paper was that there’s currently a lack of consensus and in some cases major disagreement about what is actually occurring behind the scenes.
This isn't true. I quote the parent comment:
"What this tells me is there is clearly no “reasoning” happening whatsoever with either model, despite marketing claiming as such."
Parent is clearly saying LLMs can't reason period.
>Everything you’ve written to justify the idea that reasoning is occurring can be used against the idea that reasoning is occurring. This will continue to be true until we gain a better understanding of how these models work.
Right and I took BOTH pieces of contradictory evidence into account and I ended up with the most logical conclusion. I quote myself:
"You have contradictory evidence therefore the LLM must be capable of BOTH failing and succeeding in reason. That's the most logical answer."
>The reason the Apple paper is interesting is because it’s some of the latest writing on this subject, and points at inconvenient truths about the operation of these models that at the very least would indicate that if reasoning is occurring, it’s extremely inconsistent and unreliable.
Right. And this, again, was my conclusion. But I took it a bit further. Read again what I said in the first paragraph of this very response.
>No need to be combative here - aside from being against HN guidelines, there just isn’t enough understanding yet for anyone to be making absolute claims, and the point of my comment was to add counterpoints to a conversation, not make some claim about the absolute nature of things.
You're not combative and neither am I. I respect your analysis here even though you dismissed a lot of what I said (see quotations) and even though I completely disagree and I believe you are wrong.
I think there's a further logical argument you're not realizing and I pointed it out in the first paragraph. LLMs are arriving at novel answers from novel prompts that don't exist in the data set. These novel answers have such low probability of existing via random chance that the ONLY other explanation for it is covered by the broadly defined word: "reasoning".
Again, there is also evidence of prompts that aren't arrived at via reasoning, but that doesn't negate the existence of answers to prompts that can only be arrived via reasoning.
Read the parent post. The claim is LLMs can't reason.
The evidence is using one instance of the LLM parroting training data while completely ignoring contradicting evidence where the LLM created novel answers to novel prompts out of thin air.
>Observations trump claims.
No. The same irrational hallucinations that plague LLMs are plaguing human reasoning and trumping rational thinking.
If you don’t read or understand the code, then no, you aren’t reasoning.
The condition of “some people are bad at thing” does not equal “computer better at thing than people”, but I see this argument all the time in LLM/AI discourse.
> 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?
For 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.
Not 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.
I 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.
> 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.
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.
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.
The 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.
> 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.
> 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>)
My 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.
> 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").
> 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.
You 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.
I'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?
Interestingly 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).
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.
This 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.
Not 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".
At 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.
It’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.
As some who has a dis-harmonic intelligence profile, this has been obvious for a very long time. In the family of my mother there are several individuals struggling with language while excelling in the field of exact sciences. I very strongly suspect that my non-verbal (performal) IQ is much higher (around 130) than my verbal IQ (around 100). I have struggled my whole life to express my ideas with language. I consider myself an abstract visual thinker. I do not think in pictures, but in abstract structures. During my life, I have met several people, especially among software engineers, who seem to be similar to me. I also feel that people who are strong verbal thinkers have the greatest resistance against idea that language is not essential for higher cognitive processes.
> As some who has a dis-harmonic intelligence profile, this has been obvious for a very long time. In the family of my mother there are several individuals struggling with language while excelling in the field of exact sciences. I very strongly suspect that my non-verbal (performal) IQ is much higher (around 130) than my verbal IQ (around 100)
I used to rationalize to myself along similar lines for a long time, then I realized that I'm just not as smart as I thought I was.
That was a difficult thing for me as well -- if you have such great ideas in your head but they fall apart once you try to bring them down on paper, maybe those ideas simply aren't that great.
I'm a brilliant genius according to IQ tests. Think me arrogant or conceited or whatever - that is literally the truth, fact - proven many times in the educational system (I was homeschooled and didn't follow any sort of curriculum and was allowed to do whatever I wanted bc I kept testing higher than almost everyone) and just for kicks also - the last time I took an IQ test I was in my late 20s and a friend and I had a bet about who could score higher completely stoned off of our ass. We rolled enough blunts apiece that we could be continuously smoking marijuana as we took the IQ test, which followed several bongs finished between the two of us. I was so high that I couldn't keep the numbers straight on one of the number pattern questions - it was ridiculous. I scored 124, my lowest "serious" attempt ever - all of this is 100% true. I need anyone to believe me - take this how you will but I have an opinion that is a bit different.
I'm brilliant - I've read volumes of encyclopedias, my hobbies include comparative theology, etymology, quantum mechanics and predicting the future with high accuracy (I only mention stuff I'm certain of tho ;) but so much so it disturbs my friends and family.
The highest I scored was in the 160s as a teenager but I truly believe they were over compensating for my age - only as an adult have I learned most children are stupid and they maybe in fact didn't over compensate. I am different than anyone else I've ever personally met - I fundamentally see the world different.
All of that is true but that's a rather flawed way of assessing intelligence - fr. I'm being serious. The things we know can free us as much as they can trap us - knowledge alone doesn't make a man successful, wealthy, happy or even healthy - I'm living evidence of this. That doesn't cut it as a metric for prediction of much. There are other qualities that are far more valuable in the societal sense.
Every Boss I've ever worked for has been dumber than me - each one I've learned invaluable stuff from. I was a boss once - in my day I owned and self taught/created an entire social network much like FB was a few years ago, mine obviously didn't take off and now I'm a very capable bum. Maybe someday something I'm tinkering with will make me millions but prolly not, for many reasons, I could write books if I wanted ;)
At the end of the day, the facts are what they are - there is an optimal level of intelligence that is obviously higher than the bottom but is nowhere near the top tier, very likely near that 100 IQ baseline. What separates us all is our capabilities - mostly stuff we can directly control, like learning a trade.
A Master Plumber is a genius plumber by another name and that can and obviously is most often, learned genius. What you sus about yourself is truth - don't doubt that. No IQ test ever told me I lacked the tenacity of the C average student that would employ me someday - they can't actually measure the extent of our dedicated capacity.
I kno more than most people ever have before or rn presently - I don't know as much about plumbing as an apprentice with 2 years of a trade school dedicated to plumbing and a year or two of experience in the field, that's the reality of it. I could learn the trade - I could learn most every trade, but I won't. That's life. I can tell you how you the ancients plumbed bc that piqued my curiosity and I kno far more about Roman plumbing than I do how a modern city sewer system works. That's also life.
It isn't what we kno or how fast we can learn it - it's what we do that defines us.
Become more capable if you feel looked down on - this is the way bc even if what you hone your capabilities of can be replicated by others most won't even try.
That's my rant about this whole intelligence perception we currently have as a society. Having 100 IQ is nowhere near the barrier that having 150 IQ is.
Rant aside, to the article - how isn't this obvious? I mean feelings literally exist - not just the warm fuzzy ones, like the literal feeling of existence. Does a monkey's mind require words to interpret pain or pleasure for example. Do I need to know what "fire" or "hot" is in a verbal context to sufficiently understand "burn" - words exists to convey to to others what doesn't need to be conveyed to us. That's their function. Communication. To facilitate communication with our social brethren we adopt them fundamentally as our Lego blocks for understanding the world - we pretend that words comprising language are the ideas themselves. A banana is a - the word is the fruit, they are the same in our minds but if I erase the word banana and all it's meaning of the fruit and I randomly encounter a banana - I still can taste it. No words necessary.
Also, you can think without words, deliberately and consciously - even absentmindedly.
And LLMs can't reason ;)
Truthfully, the reality is that a 100 IQ normal human is far more capable than any AI I've been given access to - in almost every metric I attempted to asses I ultimately didn't even bother as it was so obvious that humans are functionally superior.
When AI can reason - you, and everyone else, will kno it. It will be self evident.
Anyways, tldr: ppl are smarter than given credit for, smarter and much more capable - IQ is real and matters but far less than we are led to believe. People are awesome - the epitome of biological life on Earth and we do a lot of amazing things and anyone can be amazing.
I hate it when the Hacker News collective belittles itself - don't do that. I rant here bc it's one of the most interesting places I've found and I care about what all of you think far more than I care about your IQ scores.
I’m a total idiot according to every metric you can think of. Think me dim or clueless or whatever - that’s literally the truth, fact - proven many times in school (I barely made it through, despite all the help) and honestly, even I’m surprised sometimes. The last time I had to do something remotely challenging, like solving a puzzle or organizing my thoughts, I was lost almost immediately. I still managed to scrape by, though, mostly by sheer dumb luck, which is 100% true. I don't need anyone to believe me, but I can’t help pointing out just how below average I am.
I’m dumb - I’ve skimmed through a few Wikipedia articles, my hobbies include forgetting passwords, misunderstanding instructions, and being confused by even simple math. It disturbs my friends how little I grasp most things.
One time I scored so low on a test that they didn’t even bother to tell me the number. I’ve always known I’m just not sharp – I see the world through this fog that most people seem to walk right through.
And honestly, all of that is true, but it’s a pretty bad way of measuring stupidity. The more you don’t know, the more free you are, but also trapped. Ignorance alone doesn’t make a person unsuccessful, broke, or confused, though I’m living proof that it sure doesn’t help. That’s not a good indicator of much, though. There are plenty of other things that are way more useful in life.
Every boss I’ve ever had? All smarter than me, without a doubt. They’ve taught me invaluable lessons, like how not to trip over my own feet. I’ve tried and failed at many things, and now I’m just an especially capable fool. Maybe something I’m messing up now will lead to something someday, but probably not. For many reasons. I could write books about how to fail spectacularly, though ;)
At the end of the day, the facts are what they are - being smarter than me doesn’t seem hard, but that’s not the point. What really matters is learning practical things, like how to do simple tasks without messing them up.
A Master Plumber is a genius plumber, and that’s genius you can learn. You could probably learn anything – heck, I could too, but I probably won’t. That’s life. I know more about ancient Roman sewers than I do about unclogging my own toilet, and that’s also life.
It’s not about what you know or how fast you can figure it out - it’s about what you actually manage to do. Even if everyone can do what you’re trying to do better than you, most won’t even try.
You can't do this. It's not a matter of IQ, it's a matter of math. Higher order effects are essentially impossible to predict because the level of detail you need to know the initial conditions in is not possible. Even in simple systems where all the rules are known like a billiards table. Furthermore, if you could do this, you would be a billionaire by now just from trading the stock market. This claim alone makes me doubt the rest of your comment.
I absolutely agree that predicting the future has nothing to do with IQ - not directly. I'm not suggest that I have a formula or specific process that reveals to me what will happen - actually, it's the opposite.
It's an intuitive process. Almost always the most likely things that can will be what happens - the top 3 most likely outcomes of whatever will almost always contain the thing that does happen but that list must be generated adequately, factoring in the system, players and rules of whatever it is - for example: "at work" , "co-workers" , "who gets a promotion" - the most deserving person only might get the promotion, what the Boss wants is the actual key factor for predicting that outcome.
I rarely reply to replies - to make myself even more conceited, I'm not suggesting that you should feel special or anything, I'm noting this bc you've hit a button of mine - as knowing what will happen before it does is both one of my favorite things to do and an almost natural function of my experience at this point. Not everything can be predicted and I'm not talking like "on x date x will happen exactly" - not typically, there are exceptions. I've never been able to adequately explain this but I will attempt bc I think everyone can do this to some extent.
IQ factors in bc I'm able to do this bc I have an encyclopedia in my head that I am constantly updating as much as I am able - all the time, everyday constantly adding data to a "hard drive" that has so many files I honestly don't even kno what's all there at this point - I don't even try.
Almost everything I've ever read, almost every concept I've actually thought about (highdeas or altered state ponderings included) and everything I've written out by hand is still inside my head rn and I can retrieve if I need it - I unlock it with passion of all things, for example were we to have a heated debate about the Roman Empire during the course of our conversation everything I've ever learned about Rome would come back to me - to the point I could quote professor's lectures verbatim or quote off encyclopedia entries that support my argument exactly from the copy that exists inside my head. I have to be into it for it to work.
Anyways, back to the future ;)
If your correctly identify the parameters of any given situation and you can account for what people want - the intent behind their actions, what motivates them, you will see that their desires, the chain of causation and what is actually possible incredibly narrows down the actual possibilities from the "anything can happen" point of view to one where the next event in the chain is rather obvious.
So, your right - I can't do it Fr and I literally make assumptions, inferences and operate off of hypothetical data often - and more often than not I am able to predict what will happen with high accuracy.
That works bc all people are essentially the same and we all have the same underlying motivations regardless of all demographic factors.
Meh, this could be a book.
To another point of yours - I've made others an incredible amount of money in the stock market and cryptocurrency. I've never been very motivated by money. I think your Buffets, Musks and Bezos have mental disorders and I don't envy their obsession in the slightest.
I think you just need to go further in your thought process here: if you recognize that your amazing IQ scores have only very local relevance, that they don't capture everything, why feel the need to have any investment in them at all? Would it be too much of a conspiracy to you if I told you that IQ is rather a lot of BS?
If the category you are working with is the kind of thing that you have to construct such nuance, and circles, and "yes but also..."s around, perhaps you might question your category outright?
Just to say, have you ever maybe thought that what we call "intelligence" is somewhat determined more by time and place than it is by our collective answers to multiple choice questions? Just maybe something to think about.
I think your comment and mine are two sides of a coin. I'm very aware that intelligence isn't everything and the nuances and circles are not of my creation. Luck is huge.
What you are describing is the difference between a Nicolai Tesla and a Thomas Edison - Tesla was far more brilliant than Edison and what he's was doing was superior to what Edison was doing in the same field of study but Edison won and Tesla died poor and most of his greatest discoveries died with him.
The world is not made for smart people - like I said, an IQ of 150 is an incredible obstacle to a normal life. That is relevant. IQ alone is not enough but of someone spends a lifetime living with a high IQ and paying my sort of attention they will see connections that others don't, stuff will just be obvious to them that others cannot see at all, stuff that others lose sleep over won't bother them and what causes them to lose sleep others won't understand.
I think you need to think further in your thought process if you think something so substantial is merely a consequence of time and place - I assure you there is more to it.
Ok so first of all: I am truly sorry that the world is hard for you. I think its really critical for your well-being to understand that even if you are very assured at how smart you are, you are never too smart to talk to a (lesser IQ) professional of any sort; namely one who can help you navigate your thoughts and problems. Even if I can't convince you of anything else, just try to see how a third-party alone can be helpful in matters of the squishy mind. I see lots of people like you who don't get the help they could get because they feel for whatever reason above it, and it seems like your world view is really ripe for some nasty repression. You must understand your high-IQ as a symptom, not a cause.
Now a little bit cheekier advice. Let me help you make an argument for your position against someone like me, because I really didn't see how I was to be convinced that this thing we are talking about is "substantial" enough to reduce any which way. The only way you can really argue with my position here is to take the point of view that "intelligence" is something like a metaphysical category. It doesn't have to be exactly that, but the important thing is that intelligence is more something "justice" than it is like "phenotype" or "language" or "neuron". You simply cannot be a materialist and also hold the fundamental nature of something like "intelligence," I wont insult you by filling in the dots there, but just know you are committed to a bit of woo-woo when you want to go all the way like you are (which is fine, I like woo-woo, just not in this case). You want, at the end of the day, for the brain to be more than an organ, but a truly teleological entity, which by chance has now touched upon something "substantial". Its a rough argument to make these days, you would of done great in the Enlightenment though :).
Also, what I believe you were trying to get at with the Tesla/Edison thing is this idea that Edison manifests my point of view because he was more successful in like the capitalist sense than Tesla. But that is just surface level here, and not at all what I am saying. I would come back and say "no, those are simply too people we are now calling intelligent, for different reasons." Intelligence isnt about results, its about certain things that we value (at a given time). And I am not quite sure even how you want to make your point here, we all generally consider Tesla as a very intelligent man these days, people even did back then!
Meta: I have noticed many comments written in this style lately. Long-winded with out-of-place internet shorthand. It is as if someone is deliberately trying to sound youthful while not really knowing how. It is the "how do you do, fellow kids" of writing styles.
I am not sure what sort of LLM-powered bot is behind them, or whether it's one person with some sort of schizophrenia, but once you notice it you will see at least one of these per popular post.
Growing up, I never used words or even sentences for thinking.
The abstract visualizations I could build in my mind where comparable to semi-transparent buildings that I could freely spin, navigate and bend to connect relations.
In my mid-twenties, someone introduced me to the concept of people using words for mental processes, which was completely foreign to me up to this point.
For some reason, this made my brain move more and more towards this language-based model and at the same time, I felt like I was losing the capacity for complex abstract thoughts.
Still to this day I (unsuccessfully) try to revive this and unlearn the language in my head, which feels like it imposes a huge barrier and limits my mental capacity to the capabilities of what the language my brain uses at the given time (mostly EN, partially DE) allows to express.
This reminds me of my experiences working with a software developer transplanted from the humanities who was highly articulate and capable of producing language about programming, yet seemed to not be able to write many actual computer programs themselves.
I think that I ultimately developed an obsessive need to cite all my ideas against the literature and formulate natural language arguments for my claims to avoid being bludgeoned over the head with wordcelry and being seen as inferior for my lesser verbal fluency despite having written software for years at that point, since early childhood, and even studied computer science.
I think people who can manipulate complex structures but struggle with language tend to see language in a more formal way, putting more effort into understanding its structure and inner working.
Basically what to most people is so obvious that it becomes transparent ("air") isn't to us, which apparently is an incredible gift for becoming a language researcher. Or a programmer.
Although I fit the profile of a verbal thinker (English degree, education in the humanities) I don't exactly find language the primary aspect of my thought.
It seems more like a complement to it: the idea arises, and then I have this compulsion to verbalise it, which gets quite frustrating as it takes several iterations. Clearly words do matter to me as a way to structure and record my ideas but there is something that pre-empts verbalisation and to some extent resists it.
I cannot provide insight on how I arrive at ideas. Even when I did literary criticism, the best I can say is that I absorbed lots of text and then suddenly a pattern would spring out. But the same things would happen for me studying maths or the hard sciences.
Software engineering is actually a bit different for me because I am not naturally a good algorithmic problem solver. Really I am somebody very passionate about computing who has a near-compulsion to see and collect more and more technology. So for me it is as simple as saying "this resembles a reader monad" or "this puns on the active record pattern". Less impressive than my humanities intelligence but worth maybe 10x the amount in the labour market :-)
> During my life, I have met several people, especially among software engineers, who seem to be similar to me
This begs a question though: Since programming is mostly done with language - admittedly primitive/pidgin ones - why isn't that a struggle? Not sure if you're a programmer yourself, but if so do you prefer certain programming languages for some sense of "less-verbalness" or does it even matter?
The idea that programming languages and natural languages are processed with the same wetware should be testable with something like the tests described in this submission.
I don't expect it to be true, but only expecting something is not science
Some progress has been made in this area, see [0], [1], [2] and [3], observing both similarities and dissimilarities in terms of language processing:
Siegmund, J., Kästner, C., Apel, S., Parnin, C., Bethmann, A., Leich, T. & Brechmann, A. (2014). Understanding understanding source code with functional magnetic resonance imaging. In Proceedings of the 36th International Conference on Software Engineering (pp. 378-389).
Peitek, N., Siegmund, J., Apel, S., Kästner, C., Parnin, C., Bethmann, A. & Brechmann, A. (2018). A look into programmers’ heads. IEEE Transactions on Software Engineering, 46(4), 442-462.
Krueger, R., Huang, Y., Liu, X., Santander, T., Weimer, W., & Leach, K. (2020). Neurological divide: An fMRI study of prose and code writing. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering (pp. 678-690).
Peitek, N., Apel, S., Parnin, C., Brechmann, A. & Siegmund, J. (2021). Program comprehension and code complexity metrics: An fmri study. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) (pp. 524-536). IEEE.
Other than the word “language”, programming languages and natural languages really have very little in common.
Anecdotally, when I write code, I don’t “talk in my head”. The structures that I have in my brain are in fact difficult to put into words, and I can only vaguely describe them as interconnected 3D shapes evolving over time, or even just “feelings” and “instincts” in some cases.
The code that comes out of that process does not, in fact, describe the process fully, even though it describes exactly what the computer should do. That’s why reading someone else’s code can be so difficult - you are accessing just the end product of their thinking process, without seeing the process itself.
I do subjectively agree with this. I, too, don't "code by words". However, it's the first time someone has described their personal experience to me as interconnected 3d shapes. Really fascinating and really distant from my own experience.
For the second part of your message, code comments are a possible place where you can store the process, via the medium of words, this time.
I see your general point on needing language proficiency to program, but I think it's just a very low requirement.
Parent isn't saying they can't handle language (and we wouldn't have this discussion in the first place), just that they better handle complexity and structure in non verbal ways.
To get back to programming, I think this do apply to most of us. Most of us probably don't think in ruby or JS, we have a higher vision of what we want to build and "flatten" it into words that can be parsed and executed. It's of course more obvious for people writing in say basic or assembly, some conversion has to happen at some point.
Programming is moreso based on recursive problem solving. (Most) language does have some recursive structures, but these become quite difficult to think about after just a few levels, and really aren't what you'd normally consider to be "good language", e.g.
> The dog's owner's house's roof's angle's similarity to an equilateral triangle is remarkable.
You’d still be reasoning using symbols, language is inherently an extension of symbols and memes. Think of a person representing a complex concept in their mind with a symbol and using it for further reasoning
>> They’re basically the first model organism for researchers studying the neuroscience of language. They are not a biological organism, but until these models came about, we just didn’t have anything other than the human brain that does language.
I think this is completely wrong-headed. It's like saying that until cars came about we just didn't have anything other than animals that could move around under its own power, therefore in order to understand how animals move around we should go and study cars. There is a great gulf of unsubstantiated assumptions between observing the behaviour of a technological artifact, like a car or a statistical language model, and thinking we can learn something useful from it about human or more generally animal faculties.
I am really taken aback that this is a serious suggestion: study large language models as in-silico models of human linguistic ability. Just putting it down in writing like that rings alarm bells all over the place.
I've been trying to figure out to respond to this for a while. I appreciate the fact that you are pretty much the lone voice on this thread voicing this opinion, which I also share but tend to keep my mouth shut since it seems to be unpopular.
It's hard for me to understand where my peers are coming from on the other side of this argument
and respond without being dismissive, so I'll
do my best to steelman the argument later.
Machine learning models are function approximators and by definition do not have an internal experience distinct from the training data any more
than the plus operator does. I agree with the sentiment that even putting it in writing gives more weight to the position than it should, bordering on absurdity.
I suppose this is like the ELIZA phenomena on steroids, is the only thing I can think of for why such notions are being entertained.
However, to be generous, lets do some vigorous hand waving and say we could find a way to have an embodied learning agent gather sublinguistic perceptual data in an online reinforcement learning process, and furthermore that the (by definition) non-quantifiable subjective experience data could somehow be extracted, made into a training set, and fit to a nicely parametric loss function.
The idea then is that could find some architecture that would allow you to fit a model to the data.
And voila, machine consciousness, right? A perfect model for sentience.
Except for the fact that you would need to ignore that in the RL model gathering the data and the NN distilled from it, even with all of our vigorous hand waving, you are once again developing function approximators that have no subjective internal experience distinct from the training data.
Let's take it one step further. The absolute simplest form of learning comes in the form of habituation and sensitization to stimuli. Even microbes have the ability to do this.
LLMs and other static networks do not. You can attempt to attack this point by fiatting online reinforcement learning or dismissing it as unnecessary, but I should again point out that you would be attacking or dismissing the bare minimum requirement for learning, let alone a higher order subjective internal experience.
So then the argument, proceeding from false premises, would claim that the compressed experience in the NN could contain mechanical equivalents of higher order internal subjective experiences.
So even with all the might vigorous hand waving we have allowed, you have at best found a way to convert internal subjective processes to external mechanical processes fit to a dataset.
The argument would then follow, well, what's the difference? And I could point back to the microbe, but if the argument hasn't connected by this point, we will be chasing our tails forever.
A good book on the topic that examines this in much greater depth is "The Self Assembling Brain".
It has everything to do with it. The OP of the comment that you are replying to has correctly identified why LLMs are lacking - bc they have not actually learned, they have actual experience, they have no actual frame of reference to reality and how reality actually functions - what we expect of them is not possible considering how reality works.
I'm not saying we cannot create a self conscious entity - I'm saying that none of the stuff we've made so far can become self aware or conscious as we are bc we haven't made it correctly. Nobody worried about AGI has anything to worry about rn - at best the models we have now may someday be able to trick us into perceiving them as aware but fundamentally they cannot attain that state out of what they are now, so it will be bullshit if one "wakes up" soon.
I could enter what we all here call the "Zone" quite often when i was young (once while doing math :D). I still can, but rarely on purpose, and rarely while coding. I have a lot of experience in this state, and i can clearly say that a marker of entering the zone is that your thoughts are not "limited" by language anymore and the impression of clarity and really fast thinking. This is why i never thought that language was required for thinking.
Now the question: would it be possible to scan the brain of people while they enter the zone? I know it isn't a state you can reach on command, but isn't it worth to try? understand the mechanism of this state? And maybe understand where our thought start?
I also wonder - is the flow state for work the same as the flow state in other domains? IE team sports flow state is very similar - actions flow smoothly and feel automatic. Flow state in cycling feels really similar, but doesn’t create the same outputs.
I’m not a neuroscience expert, but I do have a degree in philosophy. The Russell quote immediately struck me as misleading (especially without a citation). The author could show more integrity by including Russell’s full quote:
> Language serves not only to express thoughts, but to make possible thoughts which could not exist without it. It is sometimes maintained that there can be no thought without language, but to this view I cannot assent: I hold that there can be thought, and even true and false belief, without language. But however that may be, it cannot be denied that all fairly elaborate thoughts require words.
> Human Knowledge: Its Scope and Limits by Bertrand Russell, Section: Part II: Language, Chapter I: The Uses of Language Quote Page 60, Simon and Schuster, New York.
Of course, that would contravene the popular narrative that philosophers are pompous idiots incapable of subtlety.
Is Russell aligned with Ludwig Wittgenstein’s statement, "The limits of my language mean the limits of my world."? Is he talking about how to communicate his world to others, or is he saying that without language internal reasoning is impossible?
Practically, I think the origins of fire-making abilities in humans tend to undermine that viewpoint. No other species is capable of reliably starting a fire with a few simple tools, yet the earliest archaeological evidence for fire (1 mya) could mean the ability predated complex linguistic capabilities. Observation and imitation could be enough for transmitting the skill from the first proto-human who successfully accomplished the task to others.
P.S. This is also why Homo sapiens should be renamed Homo ignis IMO.
I think it’s a nicely summarised challenge to boot.
It’s doubtless to me that thinking happens without intermediary symbols; but it’s also obvious that I can’t think deeply without the waypoints and context symbols provide. I think it is a common sense opinion.
"Language" is a subset of "symbols". I agree with what you said, but it's not representative of the quote in GP.
Just a few days ago was "What do you visualize while programming?", and there's a few of us in the comments that, when programming, think symbolically without language: https://news.ycombinator.com/item?id=41869237
The important question is: what is considered a language?
> You can ask whether people who have these severe language impairments can perform tasks that require thinking. You can ask them to solve some math problems or to perform a social reasoning test, and all of the instructions, of course, have to be nonverbal because they can’t understand linguistic information anymore.
Surely these "non-verbal instructions" are some kind of language. Maybe all human action can be considered language.
A contrarian example to this research might be feral children, i.e people who have been raised away from humans.[0] In most cases they are mentally impaired; as in not having human-like intelligence. I don't think there is a good explanation why this happens to humans. And why it doesn't happen to other animals, which develop normally in species-typical way whether they are in the wild or in human captivity. It seems that most human behavior (even high-level intelligence) is learned / copied from other humans, and maybe this copied behavior can be considered language.
If humans are "copy machines", there's also a risk of completely losing the "what's it like to be a human" behavior if children of the future are raised by AI and algorithmic feeds.
It's worth noting the precise and narrow sense in which the term "language" is used throughout these studies: it is those particular "word sequences" that activate particular regions in the brain's left hemisphere, to the exclusion of other forms of symbolic representation such as mathematical notation. Indeed, in two of the studies cited, [0] [1] subjects with language deficits or brain lesions in areas associated with the "language network" are asked to perform on various mathematical tasks involving algebraic expressions [0] or Arabic numerals [1]:
> DA was impaired in solving simple addition, subtraction, division or multiplication problems, but could correctly simplify abstract expressions such as (b×a)÷(a×b) or (a+b)+(b+a) and make correct judgements whether abstract algebraic equations like b − a = a − b or (d÷c)+a=(d+a)÷(c+a) were true or false.
> Sensitivity to the structural properties of numerical expressions was also evaluated with bracket problems, some requiring the computation of a set of expressions with embedded brackets: for example, 90 [(3 17) 3].
Discussions of whether or not these sorts of algebraic or numerical expressions constitute a "language of mathematics" aside (despite them not engaging the same brain regions and structures associated with the word "language"); it may be the case that these sorts of word sequences and symbols processed by structures in the brain's left hemisphere are not essential for thought, but can still serve as a useful psychotechnology or "bicycle of the mind" to accelerate and leverage its innate capabilities. In a similar fashion to how this sort of mathematical notation allows for more concise and precise expression of mathematical objects (contrast "the number that is thrice of three and seventeen less of ninety") and serves to amplify our mathematical capacities, language can perhaps be seen as a force multiplier; I have doubts whether those suffering from aphasia or an agrammatic condition would be able to rise to the heights of cognitive performance.
A concept in every human culture - i.e., created in every culture, not passed from one to some others - is mentalese [0]: "A universal non-verbal system of concepts, etc., conceived of as an innate representational system resembling language, which is the medium of thought and underlies the ability to learn and use a language." [1]
If you look up 'mentalese' you can find a bunch written about it. There's an in-depth article by Daniel Gregory and Peter Langland-Hassan, in the incredible Stanford Encyclopedia of Philosophy, on Inner Speech (admittedly, I'm taking a leap to think they mean precisely the same thing). [2]
[0] Steven Pinker, The Blank Slate: The Modern Denial of Human Nature (2002)
A little late to the thread, but this is obvious if you've done any reasonably serious mindfulness practice. When you are meditating, you can get to the point where the internal monolog (the yabbering of the "crazy monkey mind") is completely silenced. "You" are still present, and can direct your attention, and can observe all of the perceptions with full comprehension, without the verbal layer interpreting for you.
This "feeling" of full comprehension can be an illusion. Similar to how we think we are taking in 140 degree of full visual information through our eyes. In truth, we can only take in accurate information about the size of our thumb at arms length. The so called saccades phenomenon.
Came here to say something similar. You also notice that before any verbal thoughts arise, there are "primordial" thoughts, which are "felt" (sometimes as emotions.) These can instigate huge chains of verbal, visual, auditory thought, in turn generating more emotions, causing a (occasionally vicious) feedback loop.
This sounds similar to a fairly early realization in the practice of meditation. Daniel M Ingram refers to it as "Cause and Effect" in Mastering the Core Teachings of the Buddha: [0]
> In the stage of Cause and Effect, the relationships between mental and physical phenomena become very clear and sometimes ratchet-like. There is a cause, such as intention, and then an effect, such as movement. There is a cause, such as a sensation, and there is an effect, namely a mental impression.
Trying to increase the frequency at which you oscillate between physical sensations and mental sensations is a fascinating exercise.
>You can ask whether people who have these severe language impairments can perform tasks that require thinking. You can ask them to solve some math problems or to perform a social reasoning test, and all of the instructions, of course, have to be nonverbal because they can’t understand linguistic information anymore. Scientists have a lot of experience working with populations that don’t have language—studying preverbal infants or studying nonhuman animal species. So it’s definitely possible to convey instructions in a way that’s nonverbal. And the key finding from this line of work is that there are people with severe language impairments who nonetheless seem totally fine on all cognitive tasks that we’ve tested them on so far.
They should start with what is their definition of language. To me it's any mean you can use to communicate some information to someone else and they generally get a correct inference of what kind of representations and responses are expected is the definition of a language. Whether it's uttered words, a series of gestures, subtle pheromones or a slap in your face, that's all languages.
For the same reason I find extremely odd that the hypothesis that animals don't have any form of language is even considered as a serious claim in introduction.
Anyone can prove anything and its contrary about language if the term is given whatever meaning is needed for premises to match with the conclusion.
Just as a data point, my guess is that a very small minority of English-language speakers would define the term as broadly as you do, at least in a context relating the concept to analytical thought processes. At the very least, I think most people expect that language is used actively, such that pheromones wouldn’t fall within the definition. (And actually, that’s reflected when you say language is a means “you can use”.) Likewise, a slap in the face certainly can be interpreted, but slapping doesn’t seem like a means of communicating in general—because a slap only communicates one thing.
It's also doubtful that thinking about the concept of analytical thought processes is something most humans do either, at least not in these terms and this perspective.
Should we expect experts in cognitive science exposing their view in a scientific publication to stick to the narrowest median view of language though? All the more when in the same article you quote people like Russell who certainly didn't have a naïve definition of language when expressing a point of view on the matter.
And slapping in general can definitely communicate far more than a single thing depending on many parameters. See https://www.33rdsquare.com/is-a-slap-disrespectful-a-nuanced... for a text exploring some of nuances of the meaning it can encompasse. But even a kid can get that slap could perfectly have all the potential to create a fully doubly articulated language, as The Croods 2 creators funnily have put in scene. :D
I'm not sure it's that fringe. Popular addages such as 'language is a vehicle for thought' and 'the pen is mightier than the sword' reveal that language is sometimes implied to be tool-like, with many of our unspoken acts carrying linguistic meaning (e.g. ghosting, not answering a call, sign language, gesturing, nodding, etc.).
Even tools present us a certain 'language', talking to us via beeps, blinks and buzzes, and are having increasingly interesting discussions amongst themselves (e.g. subreddit simulator, agent based modeling). Recent philosophers of technology as Mark Coeckelbergh present a comprehensive argument for why we need to move away from the tool/language barrier [0], and has been part in informing the EC Expert Group on AI [1].
I think what you’re saying supports the view that language is structured and actively used—which excludes pheromones. But I don’t see how you get to the next step, of characterizing unspoken acts as carrying linguistic meaning. That is, sign language and not answering a call aren’t obviously in the same category, precisely because not answering a call fails to communicate any particular concept, and because people don’t use various modes of non-answering to communicate various things.
They do, in the first section of the journal article itself:
> Do any forms of thought—our knowledge of the world and ability to reason over these knowledge representations—require language (that is, representations and computations that sup-port our ability to generate and interpret meaningfully structured word sequences)?
Emphasis on "word sequences," to the exclusion of, e.g. body language or sign language. They go on to discuss some of the brain structures involved in the production and interpretation of these word sequences:
> Language production and language understanding are sup-ported by an interconnected set of brain areas in the left hemisphere, often referred to as the ‘language network'.
It is these brain areas that form the basis of their testable claims regarding language.
> Anyone can prove anything and its contrary about language if the term is given whatever meaning is needed for premises to match with the conclusion.
This is why "coming to terms" on the definitions of words and what you mean by them should be the first step in any serious discussion if you aim to have any hope in hell of communicating precisely; it is also why you should be skeptical of political actors that insist on redefining the meanings of (especially well-known) terms in order to push an agenda. Confusing a term with its actual referent is exceedingly commonplace in modern day.
Language is infinitely productive. Using a finite number of sounds or symbols, humans can produce unlimited utterance chains to communicate novel and complex ideas.
Think about it: almost every nontrivial conversation you’ve had or comment/blog/article/book you’ve read constituted an entirely new (to you) utterance which you understood and which enabled you to acquire new ideas and information you had previously lacked. No non-human animals have demonstrated this ability. At best they are able to perform single-symbol utterances to communicate previously-understood concepts (hungry, sad, scared, tired) but are unable to combine them to produce a novel utterance, the way a child could tell you about her day:
“Today the teacher asked me to multiply 3 times 7 and I got the answer right away! Then Bobby farted and the whole class was laughing. At lunch I bit my apple and my tooth felt funny. I think it’s starting to wiggle! Sally asked me if I could go to her house for a sleepover but I said I had to ask mom and dad first.”
> Language is infinitely productive. Using a finite number of sounds or symbols, humans can produce unlimited utterance chains to communicate novel and complex ideas.
We maybe disagree, in the sense that it seems to be mixing indefinitely bounded expressiveness with actual unlimited expression production that could potentially be in a bijective relationship with the an infinite set of expression.
We human are mortals and even at the whole humankind scale, we will produce a finite set of utterances.
The main thing bringing so much flexibility to languages, is our ability to reuse, fit and evolve them as we go through indefinitely many inedit experiences of the world. So something like context change tolerance. But if we want to be fair with crediting admirable unknowingly extensive creativeness, we should first consider the universe as a whole, with its permanent flow of novel context, which also include all interpretations of itself through mere mortals as ourself.
> For the same reason I find extremely odd that the hypothesis that animals don't have any form of language is even considered as a serious claim in introduction.
I guess I've always just assumed it refers to some feature that's uniquely human—notably, recursive grammars.
Also, let's recall that in its broader meaning, the scientific consensus is that humans are animals and they evolved through the same basic mechanism as all other life forms that is evolution. So even assuming that evolution made some unique language hability emerge in humans, it's most likely that they share most language traits with other species and that there is more things to learn from them that what would be possible if it's assumed they can't have a language and thoughts.
Does any other living entity have recursive grammars? It seems uniquely human.
It seems that the second link may indicate otherwise but I'm still pretty skeptical. This requires extraordinary evidence. Furthermore there may be a more practical limit of "stack size" or "context size" that effectively exceptionalizes humans (especially considering the size and proportional energy consumption of our brains).
Does it matter in the frame of investigating relations between cognitive processes and languages?
Other animals have cognitive processes, and languages, or at least it seems to be something scientifically consensual. Thus the surprise reading the kind of statement given in introduction.
Whether humans have exceptional language habilites or even "just" a biggest board to play on with the same basic facilities seems to be a completely different matter.
I'm inclined to believe that of the animals that exhibit varying degrees of self awareness, they have mental structures isomorphic to a recursive grammar. As such, perhaps using a recursive grammar is not distinctly a human trait.
I don't think that recursive grammar is linked to self awareness. Certainly not strongly. Many animals that don't appear to have ability to interpret recursive grammar seem to have self awareness.
There is a non-verbal me. E.g. who moves my limbs, feels the feelings (hunger, happiness, ..), and sometimes helps my verbal me to think (in math or in chess the answer just appears for the verbal me), or in sudden situations it takes over, and it makes decisions very fast.
Since it controls my limbs, I consider it to be the real me. My inner monologue is a bit frustrated that it can't control my limbs, and it can't really communicate with whoever controls my limbs.
Then there is my inner monologue, which does my thinking almost always, in an auditory way: imagine the sound of spoken words in an ~5 sec long duration, and let the answer appear. I consider it as an auditory deducing thingy, and also an intelligence on its own.
I am mostly fine with this, tho I am curious about my non-verbal me, and I wish I'd know more about it.
Julian Jaynes has written on this verbal/non-verbal dichotomy in The Origin of Consciousness in the Breakdown of the Bicameral Mind, in which he literally defines god to mean those phenomena related to right hemispheric structures and activities in the brain that are communicated over the anterior commissures and interpreted by left hemispheric language centers as speech; hence the many mystical reports of "hearing the voice of god" as passed down through the aeons. Such phenomena have gone by many other names: gods, the genius, the higher self, the HGA... though this metaphysical and spiritual terminology is best understood as referring to non-verbal, non-rational, non-linear forms of cognition that are closer to free association and intuitive pattern matching (similar to Kahneman's "System 1" thinking). There even exist certain mystical traditions which purport to facilitate deeper connections with this subsystem of the mind; see for instance Eshelman's accounting of the western esoteric tradition in The Mystical and Magical System of the A.'.A.'. at [0] (currently defunct pending the restoration of the Internet Archive).
As one of those people most of the time (communicating with other people is the main exception), I still find it astounding that it's hard for some people to understand.
Take riding a bike: I presume even people with an overactive inner monologue aren't constantly planning their actions (brakes, steering, turns) in words. Then just extend that out to most other stuff.
That's how I start reading, but once I'm really into it, no - I'm seeing a (low-quality) version of the scene in my head and not even registering that I'm still reading words and turning pages.
Thought and language are intertwined in ways we don’t fully grasp. The fact that certain cognitive tasks, like comprehension, can proceed without engaging traditional language-related brain regions doesn't mean thought doesn't use language—it just means we might not yet understand how it does. Thought could employ other forms of linguistic-like processes that Fedorenko's experiments, or even current brain-imaging techniques, fail to capture.
There could be functional redundancies or alternative systems at play that we haven't identified, systems that allow thought to access linguistic capabilities even when the specialized language areas are offline or unnecessary. The question of what "language in thought" looks like remains open, particularly in tasks requiring comprehension. This underscores the need for further exploration into how thought operates and what role, if any, latent or alternative linguistic functionalities play when conventional language regions aren't active.
In short, we may have a good understanding of language in isolation, but not necessarily in its broader role within the cognitive architecture that governs thought, comprehension, and meaning-making.
Moreover, I believe that one should distinguish between "language" and "words".
The parent article is mostly about thinking without "words", not necessary without a "language".
Some thoughts might be completely different from sentences in a language, probably when they have a non-sequential nature, but other thoughts are exactly equivalent to a sentence in a language, except that they do not use the words.
I can look and see to things that I recognize, e.g. A and B, and I can see that one is bigger than the other and I can think "A is bigger than B" without thinking at the words used in the spoken language, but nonetheless associating some internal concepts of "A", "B" and "is greater than", exactly like when formulating a spoken sentence.
I do not believe that such a thought can be considered as an example of thinking without language, but just as an example that for a subset of the words used in a spoken language there is an internal representation that is independent of the sequence of sounds or letters that compose a spoken or written language.
I would like to propose that reasoning needs an intermediate representation for it to be effective. Consider the scene graph representation in computer graphics. This scene graph is the intermediate representation. The algorithm is not reasoning about individual pixels of two objects interacting in the scene graph. It uses IR. Now for some that IR takes the form of language/words. For some it takes the form of visuals. For some, these are just abstract feelings.
> The fact that certain cognitive tasks, like comprehension, can proceed without engaging traditional language-related brain regions doesn't mean thought doesn't use language
All other things being equal, its is a reason to provisionally reject the hypothesis that those kinds of thought use language as introducing entities (the ties between those kinds of thought and language) into the model of reality being generated that are not needed to explain any observed phenomenon.
When we eventually nail agi, I think we will look at llm’s as nothing more than the interface to ai, how we interact with it, but we won’t consider it to be ai.
I know a little about this area and there is certainly a movement (glacial) away from thinking that thinking uses symbols, distributed or not. The argument cannot be made in a popular science article and so such articles inevitably fall back on popular ideas of what thinking is. The alternatives: the embodied nature of reasoning is one direction and many talk of an "enacivist" approach. There are certainly some kinds of thinking that require symbols, but a surprisingly large and diverse range of intelligent behaviour can be done by just wiring stuff up. Interestingly, a significant amount seems amenable to a mechanism based on "glorified auto-complete" (cf Hinton) and I have written something on the sociological variant - something readable I hope - arxiv.org/abs/2402.08403
When I was 13 or so, a friend asked me, "So, you speak three languages. Which one do you think in?" and the question left me speechless, because until that moment I hadn't considered that people think in words. It seemed a very inefficient way to go about things!
Much later, I did begin to think mostly in words, and (perhaps for unrelated reasons?) my thinking became much less efficient.
Also related, I experienced temporarily enhanced cognition while under the influence of entheogens. My thoughts, which normally fade within seconds, became stretched out, so that I could stack up to 7 layers of thought on top of each other and examine them simultaneously.
I remember feeling greatly diminished, mentally, once that ability went away.
It might be that my working memory was temporarily expanded. Research has found its possible to massively increase it by disabling parts of the brain with electronmagnets.
What it seemed like subjectively though is that my thoughts themselves became "longer", imagine planks of wood. You can stack them (slightly offset, like a video timeline with layers), and the wider they are, the more ideas you can stack before it topples over.
I have unfortunately been unable to replicate the experience. There were after-effects for a few weeks where my senses and cognition were markedly enhanced, but this faded after a few weeks.
My main take-away here is "why are we trying to make machines smarter than humans, we should try to make humans smarter"! (I guess Neuralink kinda does that, but it doesn't actually make the human part smarter...)
I can think without language about all the things that I have experienced directly through some of my senses, but there is a huge number of things that I have never experienced directly and about which I can think only using language.
I doubt that this is different for other people. I believe that those people who claim that they never think using language are never thinking about the abstract or remote things about which I think using language.
For instance, I can think about a model of CPU without naming it, if it has been included in some of the many computers that I have used during the years, by recalling an image of the computer, or of its motherboard, or of the CPU package, or recalling some experiences when running programs on that computer, how slow or how responsive that felt, and so on.
I cannot think about a CPU that I have never used, e.g. Intel 11900K, without naming it.
Similarly, I can think without language about the planet Jupiter, which I have seen directly many times, or even about the planet Neptune, which I have never seen with my eyes, but I have seen in photographs, but I cannot think otherwise than with words about some celestial bodies that I have never seen.
The same for verbs, some verbs name actions about which I can think by recalling images or sounds or smells or tactile feelings that correspond with typical results of those actions. Other verbs are too abstract, so I can think about the corresponding action only using the word that names it.
For some abstract concepts, one could imagine a sequence of images, sounds etc. that would suggest them, but that would be like a pantomime puzzle and it would be a too slow way of thinking.
I can look at a wood plank thrown over a precipice and I can conclude that it may be safe to walk on it without language, but if I were to design a bridge guaranteed to resist to the weight of some trucks passing on it, I could not do that design without thinking with language.
Therefore I believe that language is absolutely essential for complex abstract thinking, even if there are alternative ways of thinking that may be sufficient even most of the time for some people.
Knowing someone with a brain injury, something that is hugely apparent is how much we take for granted "sequencing" - that is, the ability for the brain to hold a sequence of events, ideas or actions in a coherent order over a period of time. It's much more fragile than you would think. People with specific brain injuries suddenly can't work out whether to put their shoes on before their socks etc.
Why I mention this is that I see both language and reasoning as rooted in this more fundamental cognitive ability of "coherent sequencing". This sits behind all kinds of planning and puzzling tasks where you have to project forward a sequence of theoretical actions and abstractly evaluate the outcome.
Which is all to say, I don't think language and reasoning are the same, but I do think it is likely they stem from the same underlying fundamental mechanisms in our brain. And as a consequence, it's actually quite plausible that LLMs can reconstruct mechanisms of reasoning from language, in a regressive model kind of fashion. ie: just because their are other ways to reason doesn't exclude language as a path to it.
Man, brain is so weird. The weirdest brain injury symptom I can’t wrap my head around is when people lose the ability to understand the number 0. Like everything else works but this is beyond their understanding. Like what’s so special about this number?
One thing that always seemed important to these discussions is that the serial structure of language is probably not an optimization but just due to the reality that we can only handle uttering or hearing one sound at a time.
In my mind there should be some kind of parallel/hierarchical model that comes after language layers and then optionally can be converted back to a series of tokens. The middle layers are trained on world models such as from videos, intermediary layers on mapping, and other layers on text, including quite a lot of transcripts etc. to make sure the middle layers fully ground the outer layers.
I don't really understand transformers and diffusion transformers etc., but I am optimistic that as we increase the compute and memory capacity over the next few years it will allow more video data to be integrated with language data. That will result in fully grounded multimodal models that are even more robust and more general purpose.
I keep waiting to hear about some kind of manufacturing/design breakthroughs with memristors or some kind of memory-centric computing that gives another 100 X boost in model sizes and/or efficiency. Because it does seem that the major functionality gains have been unlocked through scaling hardware which allowed the development of models that took advantage of the new scale. For me large multimodal video datasets with transcripts and more efficient hardware to compress and host them are going to make AI more robust.
I do wish I understood transformers better though because it seems like somehow they are more general-purpose. Is there something about them that is not dependant on the serialization or tokenization that can be extracted to make other types of models more general? Maybe they are tokens that have scalars attached which are still fully contextualized but are computed as many parallel groups for each step.
When I was in junior high, I remember a friend saying to me “you can’t think in images, you think in words.” She insisted, and couldn’t believe that I actually thought in images a lot of the time. she was pretty smart and creative.
But I thought in images and I still do in part. so I don’t think you need words to think.
I thought the people who did were overly computerized, maybe thinking in an over defined way.
I think the argument is in whether "thought" only applies to conscious articulation or whether non-linguistic, non-symbolic processes also qualify.
We only consciously "know" something when we represent it with symbols. There are also unconscious processes that some would consider "thought", like driving a car safely without thinking about what you're doing, but I wouldn't consider those thoughts.
I find an interesting parallel to Chain of Thought techniques with LLMs. I personally don't (consciously) know what I think until I articulate it.
To me this is similar to giving an LLM space to print out intermediary thoughts, like a JSON array of strings. Language is our programming language, in a sense. Without representing something in a word/concept, it doesn't exist.
"Ich vermute, dass wir nur sehen, was wir kennen." - Nietzsche, where "know" refers to labeling something by projecting a concept/word onto it.
A very long time ago I took a programming aptitude test, supposedly from IBM. The test was essentially detecting pattern anomalies. Two straight lines, one crooked. Pick the crooked. The patterns became increasingly more complex. I remember a little voice in my head verbalizing "two straight, one crooked". But at some point the voice stopped but I was sure which item broke the pattern.
My take away is that language is secondary to thinking - aka intuitive pattern detection. Language is the Watson to Sherlock.
The corollary is that treating language as primary in decision making is (sometimes) not as effective as treating it as secondary. At this point in my life (I'm old) I seem to have spent much of my life attempting to understand why my pattern matching/intuition made a choice that turned out to be so superior to my verbal language process.
Wasn’t this known at least empirically for centuries? I mean obviously persons and animals without language capabilities (uneducated, deaf, mute) manage some cognitive processes that underlie thought. They might not be the brightest, but it’s there.
I guess this was the experiment the proved the point.
One interesting corollary of this is the need to rethink the underpinnings of therapy. Eg, CBT is based around verbal thoughts and replacing bad ones with good ones. I've had CBT practitioners insist to me that thoughts always include words. But once you recognise that there are kinds of thinking, both processing and "mental actions" , not linked to words, it's not so easy. How do you identify and replace a maladaptive mental process, if it's not linked to a verbalisation? If it is, does replacing the verbalisation really do anything?
This I think is why so much popular psychology is so vacuous - the slogans are merely things that triggered some people to figure out how to improve their mental actions, but there's no strong linkage between the two.
I'm wondering about the "non-verbal language" that scientists use to communicate with people affected by aphasia. What makes a brain with aphasia understand it? Do brains have dedicated circuitry to process words? (as opposed to, say, sounds which are a more general concept)
I think we need to distinguish between the language e.g. the native language the person uses like English and the concept of language. Your information exchanging binary messages over PCI bus is also part of a language.
While getting confirmation of this relationship (or lack of it) is exciting, none of this is surprising: language is a tool we "developed" further through our cognitive processes, but ultimately other primates use language as well.
The one thing I wonder is if it's mostly "code duplication": iow, would we be able to develop language by using a different region of the brain, or do we actually do cognitive processes in the language part too?
In other words, is this simply deciding to send language processing to the GPU even if we could do it with the CPU (to illustrate my point)?
How would one even devise an experiment to prove or disprove this?
To me it seems obvious that our language generation and processing regions really involve cognition as well, as languages are very much rule based (even of they came up in reverse: first language then rules): could we get both regions to light up in brain imaging when we get to tricky words that we aren't sure how to spell or adapt to context like declensions of foreign words
> But you can build these models that are trained on only particular kinds of linguistic input or are trained on speech inputs as opposed to textual inputs.
As someone from this side of the "fence" (mathematics and CS, though currently obly a practicing software engineer), I don't think LLMs provide this opportunity that is in any way comparable to human minds.
Comparing performance of small kids developing their language skills (I've only had two, but one is enough to prove by contradiction) to LLMs (in particular for Serbian), LLMs like ChatGPT had a much broader vocabulary, but kids were much better at figuring out complex language rules with very limited number of inputs (noticed by them making mistakes on exceptions by following a "rule" at 2 years of age or younger).
The amount of training input GenAI needs is multiple orders of magnitude larger compared to young kids.
Though it's not a fair comparison: kids learn language by listening, immitation, watching, smelling, hearing and in context (you'll talk about bread at breakfast).
So let's be careful in considering LLMs a model of a human language process.
> British philosopher and mathematician Bertrand Russell answered the question with a flat yes, asserting that language’s very purpose is “to make possible thoughts which could not exist without it.” But even a cursory glance around the natural world suggests why Russell may be wrong.
I don't know why Russell is catching strays. Saying language exists to make possible thoughts which could not exist without it does not in any way imply that you can't think without language.
Language plays a role similar to that of paper and pen in solving certain math problems. As a tool, it aids deeper thinking. It serves two key functions: facilitating communication and enhancing thought processes. This is why "chain of thought" type of intermediate language prompts improve reasoning in OpenAI's o1 model.
Whether in the predator or in the prey, the reward system of getting food and surviving through evolution in geological time would strengthen effective thinking.
Then comes the need to transmit/transfer understanding.
From the fine article:
> various properties that human languages have—there are about 7,000 of them spoken and signed across the world—are optimized for efficiently transmitting information, making things easy to perceive, easy to understand, easy to produce and easy to learn for kids.
No. Just because words are not needed for cognitive processes, does not mean that people still can and do think in language. The properties of that language could then influence thought. This is known as the Weak Sapir-Whorf hypothesis (note "hypothesis", not "theory").
Yep, this pretty accurately describes the way of think. I have a pretty heavy inner monologue, but it's not the only way I think. I've found that words are the way I "organize" my thoughts from muddled general ideas mixed with feelings into concise ideas that I can understand and gain insights from. I often won't fully grasp the significance of an idea I have until I talk it out with someone and find a way to put it into words that distill whatever I'm thinking into a more minimal form.
Somewhat relatedly, I've started suspecting over the past few years that this is why I struggle to multitask or split my attention; while I can ruminate on several things at once, the "output" of my thinking is bottlenecked by a single stream that requires me to focus on exclusively to get a anything useful from it. Realizing this has actually helped me quite a bit in terms of being more productive because I can avoid setting myself up for failure by trying to get too much done at once and failing rather than tackling things one at a time.
This also doesn't say that the non-literal cognitive process is DNA-wired logic. Could very well be culturally constructed as well.
IMO this rather reinforce Sapir-Whorf positions than refute, it means more than literal language/grammar influence thoughts. That's directly against UG theory that predetermined rigid grammar is all you need.
Here's what Helen Keller had to say about this in _The World I Live In_:
"Before my teacher came to me, I did not know that I am. I lived in a world that was a no-world. I cannot hope to describe adequately that unconscious, yet conscious time of nothingness. I did not know that I knew aught, or that I lived or acted or desired. I had neither will nor intellect. I was carried along to objects and acts by a certain blind natural impetus. I had a mind which caused me to feel anger, satisfaction, desire. These two facts led those about me to suppose that I willed and thought. I can remember all this, not because I knew that it was so, but because I have tactual memory. It enables me to remember that I never contracted my forehead in the act of thinking. I never viewed anything beforehand or chose it. I also recall tactually the fact that never in a start of the body or a heart-beat did I feel that I loved or cared for anything. My inner life, then, was a blank without past, present, or future, without hope or anticipation, without wonder or joy or faith.
It was not night—it was not day.
. . . . .
But vacancy absorbing space,
And fixedness, without a place;
There were no stars—no earth—no time—
No check—no change—no good—no crime.
My dormant being had no idea of God or immortality, no fear of death.
I remember, also through touch, that I had a power of association. I felt tactual jars like the stamp of a foot, the opening of a window or its closing, the slam of a door. After repeatedly smelling rain and feeling the discomfort of wetness, I acted like those about me: I ran to shut the window. But that was not thought in any sense. It was the same kind of association that makes animals take shelter from the rain. From the same instinct of aping others, I folded the clothes that came from the laundry, and put mine away, fed the turkeys, sewed bead-eyes on my doll's face, and did many other things of which I have the tactual remembrance. When I wanted anything I liked,—ice-cream, for instance, of which I was very fond,—I had a delicious taste on my tongue (which, by the way, I never have now), and in my hand I felt the turning of the freezer. I made the sign, and my mother knew I wanted ice-cream. I "thought" and desired in my fingers. If I had made a man, I should certainly have put the brain and soul in his finger-tips. From reminiscences like these I conclude that it is the opening of the two faculties, freedom of will, or choice, and rationality, or the power of thinking from one thing to another, which makes it possible to come into being first as a child, afterwards as a man.
Since I had no power of thought, I did not compare one mental state with another. So I was not conscious of any change or process going on in my brain when my teacher began to instruct me. I merely felt keen delight in obtaining more easily what I wanted by means of the finger motions she taught me. I thought only of objects, and only objects I wanted. It was the turning of the freezer on a larger scale. When I learned the meaning of "I" and "me" and found that I was something, I began to think. Then consciousness first existed for me. Thus it was not the sense of touch that brought me knowledge. It was the awakening of my soul that first rendered my senses their value, their cognizance of objects, names, qualities, and properties. Thought made me conscious of love, joy, and all the emotions. I was eager to know, then to understand, afterward to reflect on what I knew and understood, and the blind impetus, which had before driven me hither and thither at the dictates of my sensations, vanished forever.
I cannot represent more clearly than any one else the gradual and subtle changes from first impressions to abstract ideas. But I know that my physical ideas, that is, ideas derived from material objects, appear to me first an idea similar to those of touch. Instantly they pass into intellectual meanings. Afterward the meaning finds expression in what is called "inner speech." When I was a child, my inner speech was inner spelling. Although I am even now frequently caught spelling to myself on my fingers, yet I talk to myself, too, with my lips, and it is true that when I first learned to speak, my mind discarded the finger-symbols and began to articulate. However, when I try to recall what some one has said to me, I am conscious of a hand spelling into mine.
It has often been asked what were my earliest impressions of the world in which I found myself. But one who thinks at all of his first impressions knows what a riddle this is. Our impressions grow and change unnoticed, so that what we suppose we thought as children may be quite different from what we actually experienced in our childhood. I only know that after my education began the world which came within my reach was all alive. I spelled to my blocks and my dogs. I sympathized with plants when the flowers were picked, because I thought it hurt them, and that they grieved for their lost blossoms. It was two years before I could be made to believe that my dogs did not understand what I said, and I always apologized to them when I ran into or stepped on them.
As my experiences broadened and deepened, the indeterminate, poetic feelings of childhood began to fix themselves in definite thoughts. Nature—the world I could touch—was folded and filled with myself. I am inclined to believe those philosophers who declare that we know nothing but our own feelings and ideas. With a little ingenious reasoning one may see in the material world simply a mirror, an image of permanent mental sensations. In either sphere self-knowledge is the condition and the limit of our consciousness. That is why, perhaps, many people know so little about what is beyond their short range of experience. They look within themselves—and find nothing! Therefore they conclude that there is nothing outside themselves, either.
However that may be, I came later to look for an image of my emotions and sensations in others. I had to learn the outward signs of inward feelings. The start of fear, the suppressed, controlled tensity of pain, the beat of happy muscles in others, had to be perceived and compared with my own experiences before I could trace them back to the intangible soul of another. Groping, uncertain, I at last found my identity, and after seeing my thoughts and feelings repeated in others, I gradually constructed my world of men and of God. As I read and study, I find that this is what the rest of the race has done. Man looks within himself and in time finds the measure and the meaning of the universe."
How specifically do you define 'meaning' and (the domain of) 'any thing'? Pls. consider if your definition of language would lead to an inference that most animals have language abilities.
Well this comment is about the article not LLMs so I doubt it will have much in the way of legs, but this work has already been covered extensively and to a fascinating depth by Jaak Panksepp [1].
His work explores the neuropsychology of emotions WAIT DON'T GO they are actually the substrate of consciousness,
NOT the other way around.
We have 7 primary affective processes (measurable hardware level emotions) and they are not what you think[2]. They are considered primary because they are sublinguistic. For instance, witnessing the color red is a primary experience, you cannot explain in words the color red to someone who has not ever seen it before.
His work is a really fascinating read if you ever want to take a break from puters for a minute and learn how people work.
PS the reason this sort of research isn't more widely known is because the behaviorist school was so incredibly dominant since the 1970s they made it completely taboo to discuss subjective experience in the realm of scientific discourse. In fact the emotions we are usually taught are not based on emotional states but on muscle contractions in the face! Not being allowed to talk about emotions in psychological studies or the inner process of the mind is kinda crazy when you think about it. So only recently with neuroimaging has it suddenly become ok to acknowledge that things happen in the brain independent of externally observable behavior.
[2]
- seeking
- fear
- anxiety and grief
- rage
- lust
- play!!!
- caring
[3] if this sounds familiar at all it's because Jordan Peterson cites Jaak Panksep all the time. Well 50% of the time, the other 50% is CG Jung and the final
50% is the book of Exodus for some reason.
While not essential for thought, language is a very important tool in shaping and sharing thoughts.
Another related tool is religion (for emotions instead of thoughts,) which funnily enough faces the same divergence language does.
Right now society that calls itself "secular" simply does not understand the role of religion, and its importance in society.
To be clear, I don't belong to any religion, I am saying one needs to be invented for people who are currently "secular."
In fact, you have the disorganized aspects of religion already. All one needs to spot these are to look at the aspects that attempt to systematize or control our feelings. Mass media, celebrities for example.
Instead of letting capitalistic forces create a pseudoreligion for society, it's better if people come together and organize something healthier, intentionally.
Holding materialism as an axiom, either directly or non-directly (through other axioms) could be called a "religion" (though at that point I'm not sure what couldn't be), and either way that could be considered bad.
Thinking some type of materialism is even mostly correct, with the sum over all mostly materialist theories being close to 1, isn't a religion at all.
Language may not be essential for thought, (most of us have the experience of an idea occurring to us that we struggle to put into words), but language acts as a regularization mechanism on thoughts.
Serializing much higher dimensional freeform thoughts into language is a very lossy process, and this kinda ensures that mostly only the core bits get translated. Think of times when someone gets an idea you're trying to convey, but you realize they're missing some critical context you forgot to share. It takes some activation energy to add that bit of context, so if it seems like they mostly get what you're saying, you skip it. Over time, transferring ideas from one person to the next, they tend towards a very compressed form because language is expensive.
This process also works on your own thoughts. Thinking out loud performs a similar role, it compresses the hell out of the thought or else it remains inexpressible. Now imagine repeated stages of compressing through language, allowing ideas to form from that compressed form, and then compressing those ideas in turn. It's a bit of a recursive process and language is in the middle of it.
Imo, that's the essense of reasoning. Limited memory and slow communication channels force us to create compact, but expressive models of reality. LLMs, on the other hand, have all the memory in the world and their model of reality is a piece-wise interpolation of the huge training dataset. Why invent grammar rules if you can keep the entire dictionary in mind?
Why do LLMs (or rather similar models that draw pictures) keep getting the number of fingers on the human hand wrong, or show two people's arms or legs merging? Or in computer-created videos, fail at object preservation? It seems to me they do not have a model of the world, only an imperfect model of pictures they've seen.
Communication of thought is a whole different question. Either way you're making a lot of strong claims without support?
> this kinda ensures that mostly only the core bits get translated
The kinda is doing a lot here. Many times the very act of trying to communicate a thought colors/corrupts the main point and gives only one perspective or a snapshot of the overall thought. There's a reason why they say a picture is worth a thousand words. Except the mind can conjure much more than a static picture. The mind can also hold the idea and the exceptions to the idea in one coherent model. For me this can be especially apparent when taking psychedelics and finding that trying to communicate some thoughts with words requires constant babbling to keep refining the last few sentences, ad libidum. There are exceptions of course, like for simple ideas.
> Many times the very act of trying to communicate a thought colors/corrupts the main point and gives only one perspective or a snapshot of the overall thought. There's a reason why they say a picture is worth a thousand words.
Yeah! Sometimes the thought isnt compressible and language doesnt help. But a lot of times it is, and it does
Does language actually 'help', or is it just the best we have? e.g. would running a thought through language have any benefit in a world where telepathy existed
Yeah, I think it would. I think if we coudl seamlessly transmit thought from one person to another in full fidelity, we would be less smart. Abstraction results from boiling out unnecessary parts and retaining the essence of something. If you have a very high bandwidth connection (unlike language) you dont need to abstract as much and I would guess humans wouldnt be as advanced as they are.
My cat asks me to go outside. No english words involved of course. She sits and faces the door, meows at it, and paws at the knob. Maybe you can argue they are speaking cat when they ask.
I remember back in school, a language teacher once was trying to convey the importance of language. One of his main arguments was that we needed words and languages in order to think. I still recall my disbelief.
I spent the next few days trying to understand how that process worked. I would force myself to think in words and sentences. It was incredibly limiting! So slow and lacking in images, in abstract relationships between ideas and sensations.
It took me another few years to realise that many people actually depend on those structures in order to produce any thought and idea.
I once realised that, for me, subvocalising thoughts was a way to keep something "in RAM", while some other thoughts went elsewhere, or developed something else. Perhaps slower speed helps in that respect?
I think people are just using the word "think" differently. They may have picked up a different meaning for that verb than you. For them, thinking == inner vocalization. It's just a different definition. They would not call imagining things or daydreaming or musing or planning action steps as "thinking".
Also, many people simply repeat facts they were told. "We need words to think" is simply a phrase this person learned, a fact to recite in school settings. It doesn't mean they deeply reflected on this statement or compared it with their experience.
Try it now: Tap your hand on the desk randomly. Can you recall how many times you did it without "saying" a sequence in your head like "1, 2, 3" or "A, B, C" etc?
If yes, how far can you count? With a language it's effectively infinite. You could theoretically go up to "1 million 5 hundred 43 thousand, 2 hundred and 10" and effortlessly know what comes next.
I can remember the sequence of sounds and like a delay line repeat that sequence in my head. This becomes easier the more distinguishable the taps are or the more of a cadence variability there is. But if it is a longer sequence I compress it by remembering an analogue like so: doo doo da doo da doo da da doo (reminiscent of morse code, or a kind of auditory binary). Would we consider this language? I think in the colloquial sense no, but it is essentially a machine language equivalent.
For context I have both abstract "multimedia" thought processes and hypervisor-like internal narrative depending on the nature of the experience or task.
Do you also have some noise for mathematical operations, such as raising a number to a power, and for equals? So doo doo da ugh doo doo feh doo doo da doo da doo da da doo?
...maybe I do this sometimes myself. Remembering the proper names of things is effort.
I can. But I do this by visualizing the taps as a group. I don't have to label them with a number. I can see them in my mind, thus recalling the taps. If I tap with any sort of rhythm I can see the rhythm in the way they are laid out in my mind and this helps with recollection.
If I want to translate this knowledge into a number, I need to count the taps I am seeing in my head. At that point I do need to think of the word for the number.
I could even do computations on these items in my mind, imagine dividing them into two groups for instance, without ever having to link them to words until I am ready to do something with the result, such as write down the number of items in each group.
But that's like how I memorize sheet music, visual groups and subgroups of notes, and yet sheet music is formally linguistic nevertheless. So in such debates I think a tricky pitfall to avoid is that all data structures are essentially linguistic as well.
This is highly anecdotal, but when I lift weights, I have an “intuition” about the number of reps I’ve performed without consciously counting them.
An example of this would be when I’m lifting weights with a friend and am lost in the set/focusing on mind-muscle connection, and as a result I forget to count my reps. I am usually quite accurate when I verify with my lifting partner the number of reps done/remaining.
As OP mentioned, many people have no internal speech, otherwise known as anendophasia, yet can still do everything anyone with an internal dialogue can do.
Similarly for me, I can do “mental object rotation” tasks even though I have aphantasia.
I don’t make a sound or word in my mind but I definitely keep track of the number. My thinking is definitely structured and there are things in my thoughts but there is no words or voice. I also can’t see images in my mind either. I’ve no idea what an inner monologue or the minds eye is like. I have however over the years found ways to produce these experiences in a way of my own. I found for instance some rough visualization was helpful in doing multi variate calculus but it’s very difficult and took a lot of practice. I’ve also been able to simulate language in my mind to help me practice difficult conversations but it’s really difficult and not distinct.
I would note though I have a really difficult time with arithmetic and mechanical tasks like counting. Mostly I just lose attention. Perhaps an inner voice would help if it became something that kept a continuity of thought.
Can you draft a sentence (with all the words precisely determined) in your mind before you say it or you write it down? Can you "rehearse" saying it without moving your tongue or mouth? If yes, that's pretty much an "inner voice".
Not really, I can speak it out loud though which is often what I do. I have over the years been able to do it in my mind but it’s not really a voice or words but some conceptual framing of the words. It’s difficult to explain.
I can imagine the numbers as figures (I mean that the shape of the characters 1, 2 etc), or the patterns on a dice in sequence.
This is a parallel stream, because if I count with imagined pictures, then I can speak and listen to someone talking without it disturbing the process. If I do it with subvocalization, then doing other speech/language related things would disturb the counting.
Practice a few polyrhythms, get used to things like:
X . X X X . X . X X X .
A . . A . . A . . A . .
B . B . B . B . B . B .
and:
X . . X . X X X . X X . X . X X . . X . X X . . X X . X X . X . . X . X X . . X X . X . . X . . X X X X . . X X X X . . X . . X . X X . . X X . X . . X . X X . X X . . X X . X . . X X . X . X X . X X X . X . .
A . . . . A . . . . A . . . . A . . . . A . . . . A . . . . A . . . . A . . . . A . . . . A . . . . A . . . . A . . . . A . . . . A . . . . A . . . . A . . . . A . . . . A . . . . A . . . . A . . . . A . . . .
B . . . . . . B . . . . . . B . . . . . . B . . . . . . B . . . . . . B . . . . . . B . . . . . . B . . . . . . B . . . . . . B . . . . . . B . . . . . . B . . . . . . B . . . . . . B . . . . . . B . . . . . .
C . . C . . C . . C . . C . . C . . C . . C . . C . . C . . C . . C . . C . . C . . C . . C . . C . . C . . C . . C . . C . . C . . C . . C . . C . . C . . C . . C . . C . . C . . C . . C . . C . . C . . C . .
Learn to do them with one limb (or finger) per line, and also with all the lines on the same limb (or finger). And then suddenly, they'll start to feel intuitive, and you'll be able to do them by feel. (It's a bit like scales.)
An important note. If you're hearing your voice in your head doing this, that's subvocalisation and it's basically just saying it out loud, the instruction is still sent to your vocal chords
It's the equivalent of <thinking> tags for LLM output.
If that's your opinion, then define intelligence in a meaningful/reductive fashion (not just "i know it when i see it"), then defend this opinion based on that!
I have the standard internal monologue many people report, but I've never put much stock in the "words are necessary for thought" because while I think a lot in words, I also do a lot of thinking in not-words.
We recently put the project I've been working on for the last year out into the field for the first time. As was fully expected, some bugs emerged. I needed to solve one of them. I designed a system in my head for spawning off child processes based on the parent process to do certain distinct types of work in a way that gives us access to OS process-level controls over the work, and then got about halfway through implementing it. Little to none of this design involved "words". I can't even say it involved much "visualization" either, except maybe in a very loose sense. It's hard to describe in words how I didn't use words but I've been programming for long enough that I pretty much just directly work in system-architecture space for such designs, especially relatively small ones like that that are just a couple day's work.
Things like pattern language advocates aren't wrong that it can still be useful to put such things into words, especially for communication purposes, but I know through direct personal experience that words are not a necessary component of even quite complicated thought.
"Subjective experience reports are always tricky, jerf. How do you know that you aren't fooling yourself about not using words?" A good and reasonable question, to which my answer is, I don't even have words for the sort of design I was doing. Some, from the aforementioned pattern languages, yes, but not in general. So I don't think I was just fooling myself on the grounds that even if I tried to serialize what I did directly into English, a transliteration rather than a translation, I don't think I could. I don't have one.
I'm also not claiming to be special. I don't know the percentages but I'm sure many people do this too.
So if you want to look at your phone there's a voice going "I shall pick up my phone and swipe the lock away now."? Trying to understand if ALL thinking is in words or some subset.
I'm an idiot. I thought this meant, for some reason unknown to me... written words, something I couldn't imagine being able to think in. Spoken words, sure.
I think that using a LLM as the referred telepathy device to a wolfram-alpha/mathematica like general reasoning module is the way to AGI. The reasoning modules we have today are still much to narrow because of the very broad and deep search trees exploding in complexity. There is the need for a kind of pathfinder which could come from common knowledge already encoded in LLMs, like in o1. An system playing with real factual reasoning but exploring in directions coming from world knowledge.
What is still missing is the dialectic between possible and right, a physics engine, the motivation of analysed agents, the effects of emergent behavior and a lot of other -isms. But they may be encoded in the reasoning-explorer. And of course loops, more loops, refinement, working hypotheses and escaping cul-de-sacs.
There are people with great language skills and next to no reasoning skills. Some of them have general knowledge. If you ever talked to them, for a at least an hour freely meandering topics you will know. They seem intelligent for a couple of minutes but after a while you realise that they can refer fact, even interpret metaphors, but they will not find an elegant one, to navigate abstraction levels, even to differentiate root cause from effect or motivation and culture from cold logic. Some of them even ace IQ or can program but none did math so far. They hate, fear or despise rational results violation their learned rules. Sorry, chances are if you hate reading this, maybe you are one (or my English is annoyingly bad).
I love talking to people outside my bubble. They have an incredible broad diversity in abilities and experiences.
Stix's claim appears to be unfalsifiable. In scientific and philosophical discourse, a proposition must be falsifiable—there must be a conceivable empirical test that could potentially refute it. This criterion is fundamental for meaningful inquiry.
Several factors contribute to the unfalsifiability of this claim:
Subjectivity of Thought: Thought processes are inherently internal and subjective. There is no direct method to observe or measure another being's thoughts without imposing interpretative frameworks influenced by social and material contexts.
Defining Language and Thought: Language is not merely a collection of spoken or written symbols; it is a system of signs embedded within social relations and power structures. If we broaden the definition of language to include any form of symbolic representation or communication—such as gestures, images, or neural patterns—then the notion of thought occurring without language becomes conceptually incoherent. Thought is mediated through these symbols, which are products of historical and material developments.
Animal Cognition and Symbolic Systems: Observations of animals like chimpanzees engaging in strategic gameplay or crows crafting tools demonstrate complex behaviors. Interpreting these actions as evidence of thought devoid of language overlooks the possibility that animals utilize their own symbolic systems. These behaviors reflect interactions with their environment mediated by innate or socially learned symbols—a rudimentary form of language shaped by their material conditions.
Limitations of Empirical Testing: To empirically verify that thought can occur without any form of language would require accessing cognitive processes entirely free from symbolic mediation. Given the current state of scientific methodologies—and considering that all cognitive processes are influenced by material and social factors—this is unattainable.
Because of these factors, Stix's claim cannot be empirically tested in a way that could potentially falsify it. It resides outside the parameters of verifiable inquiry, highlighting the importance of recognizing the interplay between language, thought, and material conditions.
Cognitive processes and language are deeply intertwined. Language arises from collective practice; it both shapes and is shaped by the material conditions of the environment. Thought is mediated through language, carrying the cognitive imprints of the material base. Even in non-human animals, the cognitive abilities we observe may be underpinned by forms of symbolic interaction with their environment—a reflection of their material engagement with the world.
Asserting that language is not essential for thought overlooks the fundamental role that social and material conditions play in shaping both language and cognition. It fails to account for how symbolic systems—integral to language—are embedded in and arise from material realities.
Certain forms of thought might appear to occur without human language, but this perspective neglects the intrinsic connection between cognition, language, and environmental conditiond. Reasoning itself can be viewed as a form of internalized language—a symbolic system rooted in social and material contexts. Recognizing this interdependence is crucial for a comprehensive understanding of the nature of thought and the pivotal role language plays within it.
You're not getting it. The very proposition of discussing cognitive processes as comprehensible without language inherently relies on circular reasoning. The claim that thought occurs without language cannot be falsified. To analyze or describe thought, we must use language, which is the very tool that shapes and defines that thought. The discussion itself becomes impossible if you remove language from the equation, meaning language and thought are co-constituitve.
Just as Gödel showed that no formal system can be both complete and consistent, language as a system cannot fully encapsulate the entirety of cognitive processes without relying on foundational assumptions that it cannot internally validate. Attempting to describe thought without acknowledging this limitation is akin to seeking completeness in an inherently incomplete framework. Without language, the discussion becomes impossible, rendering the initial claim fundamentally flawed.
You are under the false assumption that thought can only be described by language. Why are you constructing this false hierarchy? Furthermore, symbolic constructs are not by definition language. The opposite, really. Language cannot be formed without symbols. Symbols, however, do not need language.
How else can thought be described if not through language? I don't know what you mean by "symbolic constructs." Symbols are the foundation of language—they're not the opposite. There is no sense in which symbols exist outside of at the very least a protolinguistic system. Once you begin to associate sensory data with meaning, you are doing the work of creating language. To analyze or describe cognition, we must use language, which organizes symbols into meaningful constructs. That thought occurs without language is not even wrong per se. It's unfalsifiable, which frankly is worse than being wrong in a scientific context. As Wittgenstein puts it, 'The limits of my language mean the limits of my world.' Without language, discussing thought is impossible, making the claim that thought occurs without language scientifically untenable. It is an attempt to position thought as the transcendental signified.
Yes, you need language to describe (discuss) something. But not everything that exists must have a description. Furthermore, meaningful does not require organization.
If you stand outside under the sun, do you have to be able to write the word "sun" in order to feel warm?
You're sidestepping the problem. Feeling warmth is a sensory issue. Connecting the fact that you're feeling warm with the fact that you're in the sun is cognition. In order to do that, you are doing the work of creating language. Sun equals warm.
Progress with LLMs would seem to support the title. The language abilities of LLMs does not seem to lead to higher thought, so there must be additional processes that are required for higher thought or process that don’t depend on language
You may be right, but there is another hypothesis that would need to be rejected: at question is whether LLMs "do" language the same way we do. For certain they learn language much differently, with orders of magnitude more input data. It could be that they just string sentence fragments together, whereas (by hypothesis) we construct sentences hierarchically. The internal representation of semantics might also be different, more compositional in humans.
If I had time and free use of an LLM, I'd like to investigate how well it understands constructional synonymy, like "the red car" and "the car that is red" and "John saw a car on the street yesterday. It was red." I guess models that can draw pictures can be used to test this sort of thing--surely someone has looked into this?
>And in fact, most of the things that you probably learned about the world, you learned through language and not through direct experience with the world.
Most things we know, we are probably not aware of. And for most of us, direct experience of everything that surrounds us in the world certainly exceeds by several order of magnitude the best bandwidth we can ever dream to achieve through any human language.
Ok, there are no actual data to back this, but authors of the article don't have anything solid either to back such a bold statement, from what is presented in the article.
If most of what we know of the world would mostly be things we were told, it would obviously be mostly a large amount of phatic noises, lies and clueless random assertions that we would have no mean to distinguish from the few stable credible elements inferable by comparing with a far more larger corpus of self experiments with realty.
As always, barely anyone reads the actual claims in the article and we're left with people opining on the title.
The claims here are exceptionally limited. You don't need spoken language to do well on cognitive tests, but that has never been a subject of debate. Obviously the deaf get on fine without spoken language. People suffering from aphasia, but still capable of communication via other mechanisms, still do well on cognitive tests. Brain scans show you can do sudoku without increasing bloodflow to language regions.
This kind of stuff has never really been in debate. You can teach plenty of animals to do fine on all sorts of cognitive tasks. There's never been a claim that language holds dominion over all forms of cognition in totality.
But if you want to discuss the themes present in Proust, you're going to be hard pressed to do so without something resembling language. This is self-evident. You cannot ask questions or give answers for subjects you lack the facilities to describe.
tl;dr: Language's purpose is thought, not all thoughts require language
Language's purpose - why it arose - is more likely communication, primarily external communication. The benefit of using language to communicate with yourself via "inner voice" - think in terms of words - seems a secondary benefit, especially considering that less than 50% of people report doing this.
But certainly language, especially when using a large vocabulary of abstract and specialist concepts, does boost cognitive abilities - maybe essentially through "chunking", using words as "thought macros", and boosting what we're able to do with our limited 7+/- item working memory.
Whether language's purpose was communication or thought is not easily answered.
For one, how would you know? It left no fossils, nor do we have any other kind of record from that time.
For another, the very question implies a teleological view of evolution, which is arguably wrong.
As for what 50% of people report (where did that number come from?), we have virtually zero intuitive insight into the inner workings of our minds in general, or of the way we process language. All the knowledge that has been obtained about how language works--linguistics--has been obtained by external observation of a black box. (FMRIs and the like provide a little insight inside that black box, but only at the most general level--and again, that's not intuition.)
Surely it's obvious that language production and perception evolved out of more primitive animal vocalizations, used for communicative purposes. How could it not have ?!
Note that human speech ability required more than brain support - it also required changes to the vocal apparatus for pronunciation (which other apes don't have), indicating that communication (vocalization) was either driving the development of language, or remained a very important part of it.
hot take: language's original purpose must have been to lie.
It doesn't take words to understand implication of a club in your hand and a body of dead ape. From there it takes either violence or words to defend yourself(rightfully or not). Here, using language to explain the situation is more efficient.
> Obviously the deaf get on fine without spoken language.
Why the introduction of "spoken?" Sign languages are just as expressive as spoken language, and could easily be written. Writing is a sign.
> But if you want to discuss the themes present in Proust, you're going to be hard pressed to do so without something resembling language. This is self-evident.
And it's also a bad example. Of course you can't discuss the use of language without the use of language. You can't discuss the backstroke without any awareness of water or swimming, either. You can certainly do it without language though, just by waving your arms and jumping around.
> Language's purpose is thought
Is it, though? Did you make that case in the preceding paragraphs? I'm not going to go out on a limb here and alternatively suggest that language's purpose is communication, just like the purpose of laughing, crying, hugging, or smiling. This is why we normally do it loudly, or write it down where other people can see it.
Great point. They even did a bad job of reading the title. The title wasn't "Language is not essential for thought", the title was "Language is not essential for the cognitive processes *underlying* thought."
We'd better hope that is true, because if we didn't have non-linguistic mastery of the cognitive processes underlying thought it's hard to see how we could even acquire language in the first place.
This. Also the question is what the possible complexity of the question is that you want to convey. As long as it is rather simple it might seem realistic to argue that there is no language involved (i would argue this is wrong). But as soon as the problems get more complex, the system you need to use to communicate this question becomes more and more undeniably a form of language (i think about complexity here as things like self-referentiality which need sufficiently complex formal systems to be expressed – think what gödel is about). So this part seems more complicated than it is understood. The same goes for the brain-imaging argument. As a philosopher I have unfortunately seen even accomplished scientists in this field follow a surprisingly naive empiricist approach a lot of times – which seems to me to be the case here also.
> As always, barely anyone reads the actual claims in the article and we're left with people opining on the title
One must ask why this is such a common occurrence on this (and almost all other) social media, and conclude that it is because the structure of social media itself is rotten and imposes selective pressures that only allow certain kinds of content to thrive.
The actual paper itself is not readily accessible, and properly understanding its claims and conclusions would take substantial time and effort - by which point the article has already slid off the front page, and all the low-effort single-sentence karma grabbers who profit off of simplistic takes that appeal to majority groupthink have already occupied all the comment space "above the fold."
A much more interesting hypothesis is that abstract thought (thought about things not within the present sensorium) , or perhaps all thought, requires the use of symbols or tokens to represent the things that are to be considered.
I think this may have been partially substantiated through experiments in decoding thoughts with machine sensors.
If this turns out to-not- to be true it would have huge implications for AI research.
Language is a very poor substitute for freely flowing electrical information - it is evolved to compensate for the bottlenecks to external communication - bottlenecks that are lacking an internal analogue.
It's also a highly advanced feature - something as heavily optiimised as evolved life would not allow something as vital as cognition to be hampered by a lack of means for high fidelity external expression.
Perhaps. But one could argue that the development of language (as necessary for communication, its original purpose) was the seed that lead to evolutionary development of deeper thinking.
100% deeper thought - and to a large extent the capacity for linguistic categorisation of objects is incredibly helpful in developing a deeper cognitive understanding of the world around us...
But the most fundamental boon that it offered was in terms of planning and organisation. Before language we'd point and grunt and go there and do the thing that we were gesturing that we were going to do.
But that's a very crude form of planning - you're pretty much just going all in on Leeroy Jenkins.
But actually (and horrifically) I think it's the gift of Kane that speaking and planning permitted; well organised humans (the smartest things on the planet) have been figuring out increasingly better ways to both kill each other and not to die themselves in a brutal feedback loop for a very long time now.
It is not at all obvious that "freely flowing electrical information" isn't just language in a different medium, much the same as video on a cassette tape.
Language is designed to be expressible with low fidelity vibrating strings - it is very clear that the available bandwidth is in the order of bytes per second.
Verses a fucking neural network with ~100 billion neurons.
Come on man, seriously - the two communication modalities are completely incomparable.
Versus a fucking phone network with ~10 billion active numbers.
Come on man, seriously - the two communication modalities are completely incomparable.
Clearly the information traveling around on the phone network couldn't possibly be the same as the low bandwidth vibrating strings used in face to face communication. Obviously.
There's a major difference - the phone network takes in prerequisite constraints on the nature of the information that it's encoding; it is forced by its functionality to be a reflection of spoken language.
The internal communications of the mind have no need for such constraints (and evolved hundreds of millions of years beforehand).
Anyway, I don't know what you were actually trying to argue here: you just built a simulated brain out of people, and the massively multi-agent distributed nature of the language of that machine is (emergently) incomparable with vocalised language.
Also the crow. Yes the stupid movie. Stalking with idiots quoting the movie or acting out parts of it. Neat. You're assholes. I already knew that and now I can't enjoy this movie. Gee whiz you're so fucking sophisticated. What a waste of talent. You could have been doing literally anything else. Ugh.
This would also explain making me repeatedly sick and then seeing if you can field test a cure on me and use that as a pay off not to have me murdered. Like tin tin from the movie. Wonderful. Am I going to be made sick and hurt to every death of a character in every film ever until I die?
This is what they're doing rather than make buildings that don't fall over. slow clap.
And the water at 555 Beule street in San Francisco has been making me sick so I've been drinking large amounts of milk and now I wonder if it has morphine in it. I'm in physical pain all the time. These people are just so fucking evil and shitty. If I had a billion dollars I'd just put it in front of the ferry building and burn it out of spite.
They're probably just having sick homeless cough on my things when I'm not looking. They're a walking public health hazard. The best part is when idiots will cough on each other, throw a mask on so no one coughs on them, and then cough on someone else on the other side of the city. It's just so fucking sad.
Oh and the black guy that was in hackers was ton tin in the crow. So what he "learned" was essentially bird flu. And milk makes you sick because birds don't drink milk. Did I mention there are swarms of crowd flying all over the city. Also someone put a rusty spring in the bathroom and so now I'm wondering if the headaches are caused by iron filings in my food that respond to radiation that end up in my brain.
Anyway. I'll keep writing this all out because A) based on this stupid fucking movie there may or may not be bird flu in San Francisco B) I don't like being poisoned and stalked across the city by idiots re enacting every type of media they can think of (oh look this "crow"/phylactery is the way you kill people good fucking job) C) it encourages people to stay the fuck out of San Francisco which is a dangerous hell hole D) no one is helping me and everyone actively is attempting to harm me F) it's the right fucking thing to do and if I can encourage as many people as I possibly can not to come here I will E) yes they did in fact say alphabetical order when they killed tin tin. Did I mention there a stupid fucking wizard of Oz play and security company?
I just don't want to be sick. I'm going to write all this shit out so long as I'm ill and hurting.
So. Bird flu. Fucking wonderful. It's probably nothing, the hundreds of crows on the embarcadero are just there for no reason. Is someone going to give me rabies? Are my kidneys going to fail now? When I write shit people don't like do they hack the free phone I have to fuck with the iron filings in my head?
I'm sick and I hurt. 555 Beule street San Francisco.
Considering that, in 2024, if not a majority, then, still, a vast portion of our consciousness is words. Perhaps not for the illiterate, but for many, much of our knowledge is through the written or spoken word. [Edit: Even a hypothetical person, alone and isolated, never having spoken, would still devise internal language structures, at least for the external realm. ]
Base consciousness is surely not dependent on language, but I suspect base consciousness may be extremely different from what one might expect, so much that compared to what we perceive as consciousness, might seem something close to death.
Well, I'm not sure cognition entirely without language is even possible for non larval humans. Language is a natural tendency and it arises regardless of documentation, scribblings or utterings. It exists whether audible or not. Language itself is manifestation of the thinking process that permits it.
And I'll hold to the notion that the complete absence of language (and its underlying structure) would resemble death if death can be resembled. Perhaps death is only the excoriation of thought, cognition and language, with something more fundamental persisting.
Since we all have language and opinions about it, the risk of genericness is high with a title like this. It's like this with threads about other universal topics too, such as food or health.