Posted by ntnbr 7 days ago
What does it mean to say that we humans act with intent? It means that we have some expectation or prediction about how our actions will effect the next thing, and choose our actions based on how much we like that effect. The ability to predict is fundamental to our ability to act intentionally.
So in my mind: even if you grant all the AI-naysayer's complaints about how LLMs aren't "actually" thinking, you can still believe that they will end up being a component in a system which actually "does" think.
My personal assessment is that LLMs can do neither.
An LLM has: words in its input plane, words in its output plane, and A LOT of cross-linked internals between the two.
Those internals aren't "words" at all - and it's where most of the "action" happens. It's how LLMs can do things like translate from language to language, or recall knowledge they only encountered in English in the training data while speaking German.
The heavy lifting here is done by embeddings. This does not require a world model or “thought”.
This is a case where it's going to be next to impossible to provide proof that no counterexamples exist. Conversely, if what I've written there is wrong then a single counterexample will likely suffice to blow the entire thing out of the water.
If you're interested in why compression is like understanding in many ways, I'd suggest reading through the wikipedia article on Kolmogorov complexity.
My "abstract thoughts" are a stream of words too, they just don't get sounded out.
Tbf I'd rather they weren't there in the first place.
But bodies which refuse to harbor an "interiority" are fast-tracked to destruction because they can't suf^W^W^W be productive.
Funny movie scene from somewhere. The sergeant is drilling the troops: "You, private! What do you live for!", and expects an answer along the lines of dying for one's nation or some shit. Instead, the soldier replies: "Well, to see what happens next!"
To me, solving problems happens in a logico/aesthetical space which may be the same as when you are intellectually affected by a work of art. I don't remember myself being able to translate directly into words what I feel for a great movie or piece of music, even if in the late I can translate this "complex mental entity" into words, exactly like I can tell to someone how we need to change the architecture of a program in order to solve something after having looked up and right for a few seconds.
It seems to me that we have an inner system that is much faster than language, that creates entities that can then beslowly and sometimes painfully translated to language.
I do note that I'm not sure about any of the previous statements though'
The twist about words in particular is they are distinctly articulable symbols, i.e. you can sound 'em out - and thus, presumably, have a reasonable expectation for bearers of the same language to comprehend if not what you meant then at least some vaguely predictable meaning-cloud associated with the given speech act.
That's unlike e.g. the numbers (which are more compressed, and thus easier to get wrong), or the syntagms of a programming language (which don't even have a canonical sonic representation).
Therefore, it's usually words that are taught to a mind during the formative stages of its emergence. That is, the words that you are taught, your means of inner reflection, are still sort of an imposition from the outside.
Just consider what you life trajectory would've been if in your childhood you had refused to learn any words, or learned them and then refused to mistake them for the things they represent!
Infants and even some animals recognize their reflection in a mirror; however, practically speaking, introspection is something that one needs to be taught: after recognizing your reflection you still need to be instructed what is to be done about it.
Unfortunately, introspection needing to be taught means that introspection can be taught wrongly.
As you can see with the archetypical case of "old and wise person does something completely stupid in response to communication via digital device", a common failure mode of how people are taught introspection (and, I figure, an intentional one!) is not being able to tell apart yourself from your self, i.e. not having an intuitive sense of where the boundary lies between perception and cognition, i.e. going through life without ever learning the difference between the "you" and the "words about you".
It's extremely common, and IMO an extremely factory-farming kind of tragic.
I say it must be extremely intentional as well, because the well-known practice of using "introspection modulators" to establish some sort of perceptual point of reference (such as where the interior logicoaeshtetical space ends and exterior causalityspace begins) very often ends up with the user in, well, a cage of some sort.
> It's extremely common
I cannot conceive this ? I am lacking the empirical knowledge you seem to have. (I don't understand your "archetypical case", I can't relate to it). I'd love a reexplanation of your point here, as your intent is unclear to me.
I didn't understand also the "introspection modulators" part :(, (a well known practice ?? I must be living on another planet haha...).
edit: or maybe that's a metaphor for "language" ??
Hmm, seems unlikely. They are not sounded out part is true, sure, but I question whether 'abstract thoughts' can be so easily dismissed as mere words.
edit: come to think of it and I am asking this for a reason: do you hear your abstract thoughts?
Play a little game of "what word will I think of next?" ... just let it happen. Those word choices are fed to the monologue, they aren't a product of it.
move.panic.fear.run
that effectively becomes one thought and not a word exactly. I am stating it like this, because I worry that my initial point may have been lost.
edit: I can only really speak for myself, but I am curious how people might respond to the distinction.
Most of the fucking time, and I would prefer that I didn't. I even wrote that, lol.
I don't think they're really "mine", either. It's just all the stuff I heard somewhere, coalescing into potential verbalizations in response to perceiving my surroundings or introspecting my memory.
If you are a materialist positivist, well sure, the process underlying all that is some bunch of neural activation patterns or whatever; the words remain the qualia in which that process is available to my perception.
It's all cuz I grew up in a cargo cult - where not presenting the correct passwords would result in denial of sustenance, shelter, and eventually bodily integrity. While presenting the correct passwords had sufficient intimidation value to advance one's movement towards the "mock airbase" (i.e. the feeder and/or pleasure center activation button as provided during the given timeframe).
Furthermore - regardless whether I've been historically afforded any sort of choice in how to conceptualize my own thought processes, or indeed whether to have those in the first place - any entity which has actual power to determine my state of existence (think institutions, businesses, gangs, particularly capable individuals - all sorts of autonomous corpora) has no choice but to interpret me as either a sequence of words, a sequence of numbers, or some other symbol sequence (e.g. the ones printed on my identity documents, the ones recorded in my bank's database, or the metadata gathered from my online represence).
My first-person perspective, being constitutionally inaccessible to such entities, does not have practical significance to them, and is thus elided from the process of "self-determination". As far as anyone's concerned, "I" am a particular sequence of that anyone's preferred representational symbols. For example if you relate to me on the personal level, I will probably be a sequence of your emotions. Either way, what I may hypothetically be to myself is practically immaterial and therefore not a valid object of communication.
Though I do think in human brains it's also an interplay where what we write/say also loops back into the thinking as well. Which is something which is efficient for LLMs.
But raising kids, I can clearly see that intelligence isn't just solved by LLMs
Funny, I have the opposite experience. Like early LLMs kids tend to give specific answers to the questions they don't understand or don't really know or remember the answer to. Kids also loop (give the same reply repeatedly to different prompts), enter highly emotional states where their output is garbled (everyone loves that one), etc. And it seems impossible to correct these until they just get smarter as their brain grows.
What's even more funny is that adults tend to do all these things as well, just less often.
As the person you initially responded to said, observing children growing up should make it obvious.
Or if we shift to stating the obvious, there's the minor detail that the vast majority of architectures lack the ability to learn during inference. That's one of the basic things that biological systems are capable of.
If it turns out that LLMs don't model human brains well enough to qualify as "learning abstract thought" the way humans do, some future technology will do so. Human brains aren't magic, special or different.
They’re certainly special both within the individual but also as a species on this planet. There are many similar to human brains but none we know of with similar capabilities.
They’re also most obviously certainly different to LLMs both in how they work foundationally and in capability.
I definitely agree with the materialist view that we will ultimately be able to emulate the brain using computation but we’re nowhere near that yet nor should we undersell the complexity involved.
If I throw some braincells into a cup alongside the dice, will they think about the outcome anymore than the dice alone?
If so, yes, they're thinking
"A.I. and humans are as different as chalk and cheese."
As aphorisms are a good way to think about this topic?
From this point on its all about efficiencies:
modeling efficiency: how do we best fit the elephant, with bezier curves, rational polynomials, ...?
memory bandwidth training efficiency: when building coincidence statistics, say bigrams, is it really necessary to update the weights for all concepts? a co-occurence of 2 concepts should just increase the predicted probability for the just observed bigram and then decrease a global coefficient used to scale the predicted probabilities. I.e. observing a baobab tree + an elephant in the same image/sentence/... should not change the relative probabilities of observing french fries + milkshake versus bicycle + windmill. This indicates different architectures should be possible with much lower training costs, by only updating weights of the concepts observed in the last bigram.
and so on with all other kinds of efficiencies.
> Human brains aren't magic, special or different.
[1] https://www.nature.com/articles/s41598-024-62539-5
As the result, all living cells with DNA emit coherent (as in lasers) light [2]. There is a theory that this light also facilitates intercellular communication.
[2] https://www.sciencealert.com/we-emit-a-visible-light-that-va...
Chemical structures in dendrites, not even neurons, are capable to compute XOR [3] which require multilevel artificial neural network with at least 9 parameters. Some neurons in brain have hundredths of thousands of dendrites, we are now talking of millions of parameters only in single neuron's dendrites functionality.
[3] https://www.science.org/doi/10.1126/science.aax6239
So, while human brains aren't magic, special or different, they are just extremely complex.
Imagine building a computer with 85 billions of superconducting quantum computers, optically and electrically connected, each capable of performing computations of a non-negligibly complex artificial neural network.
> We know this because we can test what aspects of neurons actually lead to practical real world effects.
What is wonderful here is that contemporary electronics wizardry that allowed us to have machines that mimic some of thinking, also is very concerned of the quantum-level electromagnetic effects at the transistor level.
How complex our everything computing-related should be to mimic thinking (of humans) little more closely?
Planes and boats disrupt the environments they move through and air and sea freight are massive contributors to pollution.
While I agree to some extent with the materialistic conception, the brain is not an isolated mechanism, but rather the element of a system which itself isn't isolated from the experience of being a body in a world interacting with different systems to form super systems.
The brain must be a very efficient mechanism, because it doesn't need to ingest the whole textual production of the human world in order to know how to write masterpieces (music, litterature, films, software, theorems etc...). Instead the brain learns to be this very efficient mechanism with (as a starting process) feeling its own body sh*t on itself during a long part of its childhood.
I can teach someone to become really good at producing fine and efficient software, but on the contrary I can only observe everyday that my LLM of choice keeps being stupid even when I explain it how it fails. ("You're perfectly right !").
It is true that there's nothing magical about the brain, but I am pretty sure it must be stronger tech than a probabilistic/statistical next word guesser (otherwise there would be much more consensus about the usability of LLMs I think).
Animals and computers come close in some ways but aren't quite there.
“Internal combustion engines and human brains are both just mechanisms. Why would one mechanism a priori be capable of "learning abstract thought", but no others?”
The question isn't about what an hypothetical mechanism can do or not, it's about whether the concrete mechanism we built does or not. And this one doesn't.
I will absolutely say that all ML methods known are literally too stupid to live, as in no living thing can get away with making so many mistakes before it's learned anything, but that's the rate of change of performance with respect to examples rather than what it learns by the time training is finished.
What is "abstract thought"? Is that even the same between any two humans who use that word to describe their own inner processes? Because "imagination"/"visualise" certainly isn't.
If you consider that LLMs have already "learned" more than any one human in this world is able to learn, and still make those mistakes, that suggests there may be something wrong with this approach...
To a limited degree, they can compensate for being such slow learners (by example) due to the transistors doing this learning being faster (by the wall clock) than biological synapses to the same degree to which you walk faster than continental drift. (Not a metaphor, it really is that scale difference).
However, this doesn't work on all domains. When there's not enough training data, when self-play isn't enough… well, this is why we don't have level-5 self-driving cars, just a whole bunch of anecdotes about various different self-driving cars that work for some people and don't work for other people: it didn't generalise, the edge cases are too many and it's too slow to learn from them.
So, are LLMs bad at… I dunno, making sure that all the references they use genuinely support the conclusions they make before declaring their task is complete, I think that's still a current failure mode… specifically because they're fundamentally different to us*, or because they are really slow learners?
* They *definitely are* fundamentally different to us, but is this causally why they make this kind of error?
Some machines, maybe. But attention-based LLMs aren't these machines.
The same way a todler creeping is the start of the general concept of space exploration.
But to your point, I do see a lot of people very emotionally and psychologically committed to pointing out how deeply magical humans are, and how impossible we are to replicate in silicon. We have a religion about ourselves; we truly do have main character syndrome. It's why we mistakenly thought the earth was at the center of the universe for eons. But even with that disproved, our self-importance remains boundless.
This a straw man, the question isn't if this is possible or not (this is an open question), it's about whether or not we are already here, and the answer is pretty straightforward: no we aren't. (And the current technology isn't going to bring us anywhere near that)
It's not just that. The problem of “deep learning” is that we use the word “learning” for something that really has no similarity with actual learning: it's not just that it converges way too slowly, it's also that it just seeks to minimize the predicted loss for every samples during training, but that's no how humans learn. If you feed it enough flat-earther content, as well a physics books, an LLM will happily tells you that the earth is flat, and explain you with lots of physics why it cannot be flat. It simply learned both “facts” during training and then spit it out during inference.
A human will learn one or the other first, and once the initial learning is made, it will disregards all the evidence of the contrary, until maybe at some point it doesn't and switches side entirely.
LLMs don't have an inner representation of the world and as such they don't have an opinion about the world.
The humans can't see the reality for itself, but they at least know it exists and they are constantly struggling to understand it. The LLM, by nature, is indifferent to the world.
This is a terrible example, because it's what humans do as well. See religious, or indeed military, indoctrination. All propaganda is as effective as it is, because the same message keeps getting hammered in.
And not just that, common misconceptions abound everywhere and not just conspiracy theories, religion, and politics. My dad absolutely insisted that the water draining in toilets or sinks are meaningfully influenced by the Coriolis effect, used an example of one time he went to the equator and saw a demonstration of this on both sides of the equator. University education and lifetime career in STEM, should have been able to figure out from first principles why the Coriolis effect is exactly zero on the equator itself, didn't.
> A human will learn one or the other first, and once the initial learning is made, it will disregards all the evidence of the contrary, until maybe at some point it doesn't and switches side entirely.
We don't have any way to know what a human would do if they could read the entire internet, because we don't live long enough to try.
The only bet I'd make is that we'd be more competent than any AI doing the same, because we learn faster from fewer examples, but that's about it.
> LLMs don't have an inner representation of the world and as such they don't have an opinion about the world.
There is evidence that they do have some inner representation of the world, e.g.:
You completely misread my point.
The key thing with humans isn't that they cannot believe in bullshit. They can definitely do. But we don't usually believe in both the bullshit and in the fact the BS is actually BS. We have opinions on the BS. And we, as a species, routinely die or kill for these opinions, by the way. LLM don't care about anything.
I can't parse what you mean by this.
> LLM don't care about anything.
"Care" is ill-defined. LLMs are functions that have local optima (the outputs); those functions are trained to approximate other functions (e.g. RLHF) that optimise other things that can be described with functions (what humans care about). It's a game of telephone, like how Leonard Nimoy was approximating what the script writers were imagining Spock to be like when given the goal of "logical and unemotional alien" (ditto Brent Spiner, Data, "logical and unemotional android"), and yet humans are bad at writing such characters: https://tvtropes.org/pmwiki/pmwiki.php/Main/StrawVulcan
But rather more importantly in this discussion, I don't know what you care about when you're criticising AI for not caring, especially in this context. How, *mechanistically*, does "caring" matter to "learning abstract thought", and the question of how closely LLMs do or don't manage it relative to humans?
I mean, in a sense, I could see why someone might argue the exact opposite, that LLMs (as opposed to VLMs or anything embodied in a robot, or even pure-text agents trained on how tools act in response to the tokens emitted) *only* have abstract "thought", in so far as it's all book-learned knowledge.
> I can't parse what you mean by this.
The point is that humans care about the state of a distributed shared world model and use language to perform partial updates to it according to their preferences about that state.
Humans who prefer one state (the earth is flat) do not -- as a rule -- use language to undermine it. Flat earthers don't tell you all the reasons the earth cannot be flat.
But even further than this, humans also have complex meta-preferences of the state, and their use of language reflects those too. Your example is relevant here:
> My dad absolutely insisted that the water draining in toilets or sinks are meaningfully influenced by the Coriolis effect [...]
> [...] should have been able to figure out from first principles why the Coriolis effect is exactly zero on the equator itself, didn't.
This is an exemplar of human behavior. Humans act like this. LLMs don't. If your dad did figure out from first principles and expressed it and continued insisting the position, I would suspect them of being an LLM, because that's how LLMs 'communicate'.
Now that the what is clear -- why? Humans experience social missteps like that as part of the loss surface. Being caught in a lie sucks, so people learn to not lie or be better at it. That and a million other tiny aspects of how humans use language in an overarching social context.
The loss surface that LLMs see doesn't have that feedback except in the long tail of doing Regularized General Document Corpora prediction perfectly. But it's so far away compared to just training on the social signal, where honesty is immediately available as a solution and is established very early in training instead of at the limit of low loss.
How humans learn (embedded in a social context from day one) is very effective at teaching foundational abilities fast. Natural selection cooked hard. LLM training recipes do not compare, they're just worse in so many different ways.
https://www.anthropic.com/research/introspection
Its hard to tell sometimes because we specifically train them to believe they don't.
I don't think the version of self awareness they demonstrated is synonymous with subjective experience. But same thing can be said about any human other then me.
Damn, just let me believe all brains are magical or I'll fall into solipsism.
> If it turns out that LLMs don't model human brains well enough to qualify as "learning abstract thought" the way humans do, some future technology will do so. Human brains aren't magic, special or different.
Google "strawman".
Nobody is. What people are doing is claiming that "predicting the next thing" does not define the entirety of human thinking, and something that is ONLY predicting the next thing is not, fundamentally, thinking.
LLMs have higher dimensions (they map token to grammatical and semantical space) .. it might not be thinking but it seems on its way we're just thinking with more abstractions before producing speech ?... dunno
It is not unreasonable to suspect differences between humans and LLMs are differences in degree, rather than category.
My claim is that the two concepts are indistinguishable, thus equivalent. The unfalsifiability is what makes it a natural equivalence, the same as in the other examples I gave.
I could also say a motorcycle "moves forward" just like a person "moves forward". Whether we use the same or different words for same or different concepts doesn't say anything about the actual underlying similarity.
And please don't call stuff "dumb shit" here. Not appropriate for HN.
I am not having some existential crisis, but if we get to a point where X% of humans cannot outperform “AI” on any task that humans deem “useful”, for some nontrivial value of X, then many assumptions that culture has inculcated into me about humanity are no longer valid.
What is the role of humans then?
Can it be said that humans “think” if they can’t think a thought that a non thinking AI cannot also think?
If all AI was suddenly wiped off the face of the earth, humans would rebuild it, and would carry on fine in the meantime.
One AI researcher decades ago said something to the effect of: researchers in biology look at living organisms and wonder how they live; researchers in physics look at the cosmos and wonder what all is out there; researchers in artificial intelligence look at computer systems and wonder how they can be made to wonder such things.
Language and society constrains the way we use words, but when you speak, are you "predicting"? Science allows human beings to predict various outcomes with varying degrees of success, but much of our experience of the world does not entail predicting things.
How confident are you that the abstractions "search" and "thinking" as applied to the neurological biological machine called the human brain, nervous system, and sensorium and the machine called an LLM are really equatable? On what do you base your confidence in their equivalence?
Does an equivalence of observable behavior imply an ontological equivalence? How does Heisenberg's famous principle complicate this when we consider the role observer's play in founding their own observations? How much of your confidence is based on biased notions rather than direct evidence?
The critics are right to raise these arguments. Companies with a tremendous amount of power are claiming these tools do more than they are actually capable of and they actively mislead consumers in this manner.
Yes. This is the core claim of the Free Energy Principle[0], from the most-cited neuroscientist alive. Predictive processing isn't AI hype - it's the dominant theoretical framework in computational neuroscience for ~15 years now.
> much of our experience of the world does not entail predicting things
Introspection isn't evidence about computational architecture. You don't experience your V1 doing edge detection either.
> How confident are you that the abstractions "search" and "thinking"... are really equatable?
This isn't about confidence, it's about whether you're engaging with the actual literature. Active inference[1] argues cognition IS prediction and action in service of minimizing surprise. Disagree if you want, but you're disagreeing with Friston, not OpenAI marketing.
> How does Heisenberg's famous principle complicate this
It doesn't. Quantum uncertainty at subatomic scales has no demonstrated relevance to cognitive architecture. This is vibes.
> Companies... are claiming these tools do more than they are actually capable of
Possibly true! But "is cognition fundamentally predictive" is a question about brains, not LLMs. You've accidentally dismissed mainstream neuroscience while trying to critique AI hype.
[0] https://www.nature.com/articles/nrn2787
[1] https://mitpress.mit.edu/9780262045353/active-inference/
The article argues that the brain "predicts" acts of perception in order to minimize surprise. First of all, very few people mean to talk about these unconscious operations of the brain when they claim they are "thinking". Most people have not read enough neuroscience literature to have such a definition. Instead, they tend to mean "self-conscious activity" when they say "thinking". Thinking, the way the term is used in the vernacular, usually implies some amount of self-reflexivity. This is why we have the term "intuition" as opposed to thinking after all. From a neuronal perspective, intuition is still thinking, but most people don't think (ha) of the word thinking to encompass this, and companies know that.
It is clear to me, as it is to everyone one the planet, that when OpenAI for example claims that ChatGPT "thinks" they want consumers to make the leap to cognitive equivalence at the level of self-conscious thought, abstract logical reasoning, long-term learning, and autonomy. These machines are designed such that they do not even learn and retain/embed new information past their training date. That already disqualifies them from strong equivalence to human beings, who are able to rework their own tendencies toward prediction in a meta cognitive fashion by incorporating new information.
The thing you're doing here has a name: using "emergence" as a semantic stopsign. "The system is complex, therefore emergence, therefore we can't really say" feels like it's adding something, but try removing the word and see if the sentence loses information.
"Neurons are complex and might exhibit chaotic behavior" - okay, and? What next? That's the phenomenon to be explained, not an explanation.
This was articulated pretty well 18 years ago [0].
[0]: https://www.lesswrong.com/posts/8QzZKw9WHRxjR4948/the-futili...
It doesn't even meaningfully engage with the historical literature that established the term, etc. If you want to actually understand why the term gained prominence, check out the work of Edgar Morin.
To my understanding, bloaf's claim was only that the ability to predict seems a requirement of acting intentionally and thus that LLMs may "end up being a component in a system which actually does think" - not necessarily that all thought is prediction or that an LLM would be the entire system.
I'd personally go further and claim that correctly generating the next token is already a sufficiently general task to embed pretty much any intellectual capability. To complete `2360 + 8352 * 4 = ` for unseen problems is to be capable of arithmetic, for instance.
So notice that my original claim was "prediction is fundamental to our ability to act with intent" and now your demand is to prove that "prediction is fundamental to all mental activity."
That's a subtle but dishonest rhetorical shift to make me have to defend a much broader claim, which I have no desire to do.
> Language and society constrains the way we use words, but when you speak, are you "predicting"?
Yes, and necessarily so. One of the main objections that dualists use to argue that our mental processes must be immaterial is this [0]:
* If our mental processes are physical, then there cannot be an ultimate metaphysical truth-of-the-matter about the meaning of those processes.
* If there is no ultimate metaphysical truth-of-the-matter about what those processes mean, then everything they do and produce are similarly devoid of meaning.
* Asserting a non-dualist mind therefore implies your words are meaningless, a self-defeating assertion.
The simple answer to this dualist argument is precisely captured by this concept of prediction. There is no need to assert some kind of underlying magical meaning to be able to communicate. Instead, we need only say that in the relevant circumstances, our minds are capable of predicting what impact words will have on the receiver and choosing them accordingly. Since we humans don't have access to each other's minds, we must not learn these impacts from some kind of psychic mind-to-mind sense, but simply from observing the impacts of the words we choose on other parties; something that LLMs are currently (at least somewhat) capable of observing.
[0] https://www.newdualism.org/papers/E.Feser/Feser-acpq_2013.pd...
If you read the above link you will see that they spell out 3 problems with our understanding of thought:
Consciousness, intentionality, and rationality.
Of these, I believe prediction is only necessary for intentionality, but it does have some roles to play in consciousness and rationality.
They prove to have some useful utility to me regardless.
Especially when modeling acting with intent. The ability to measure against past results and think of new innovative approaches seems like it may come from a system that may model first and then use LLM output. Basically something that has a foundation of tools rather than an LLM using MCP. Perhaps using LLMs to generate a response that humans like to read, but not in them coming up with the answer.
Either way, yes, its possible for a thinking system to use LLMs (and potentially humans piece together sentences in a similar way), but its also possible LLMs will be cast aside and a new approach will be used to create an AGI.
So for me: even if you are an AI-yeasayer, you can still believe that they won't be a component in an AGI.
The near-religious fervor which people insist that "its just prediction" makes me want to respond with some religious allusions of my own:
> Who is this that wrappeth up sentences in unskillful words? Gird up thy loins like a man: I will ask thee, and answer thou me. Where wast thou when I laid up the foundations of the earth? tell me if thou hast understanding. Who hath laid the measures thereof, if thou knowest? or who hath stretched the line upon it?
The point is that (as far as I know) we simply don't know the necessary or sufficient conditions for "thinking" in the first place, let alone "human thinking." Eventually we will most likely arrive at a scientific consensus, but as of right now we don't have the terms nailed down well enough to claim the kind of certainty I see from AI-detractors.
I’m downplaying because I have honestly been burned by these tools when I’ve put trust in their ability to understand anything, provide a novel suggestion or even solve some basic bugs without causing other issues.?
I use all of the things you talk about extremely frequently and again, there is no “thinking” or consideration on display that suggests these things work like us, else why would we be having this conversation if they were ?
I've had that experience plenty of times with actual people... LLMs don't "think" like people do, that much is pretty obvious. But I'm not at all sure whether what they do can be called "thinking" or not.
The harms engendered by underestimating LLM capabilities are largely that people won't use the LLMs.
The harms engendered by overestimating their capabilities can be as severe as psychological delusion, of which we have an increasing number of cases.
Given we don't actually have a good definition of "thinking" what tack do you consider more responsible?
Speculative fiction about superintelligences aside, an obvious harm to underestimating the LLM's capabilities is that we could effectively be enslaving moral agents if we fail to correctly classify them as such.
Much worse, when insufficiently skeptical humans link the LLM to real-world decisions to make their own lives easier.
Consider the Brazil-movie-esque bureaucratic violence of someone using it to recommend fines or sentencing.
Do you have a proof for this?
Surely such a profound claim about human thought process must have a solid proof somewhere? Otherwise who's to say all of human thought process is not just a derivative of "predicting the next thing"?
What would change your mind? It's an exercise in feasibility.
For example, I don't believe in time travel. If someone made me time travel, and made it undeniable that I was transported back to 1508, then I would not be able to argue against it. In fact, no one in such position would.
What is that equivalent for your conviction? There must be something, otherwise, it's just an opinion that can't be changed.
You don't need to present some actual proof or something. Just lay out some ideas that demonstrate that you are being rational about this and not just sucking up to LLM marketing.
That's not a proof. Think harder about the questions people are asking you here.
In the case of LLMs you run into similarities, but they're much more monolithic networks, so the aggregate activations are going to scan across billions of neurons each pass. The sub-networks you can select each pass by looking at a threshold of activations resemble the diverse set of semantic clusters in bio brains - there's a convergent mechanism in how LLMs structure their model of the world and how brains model the world.
This shouldn't be surprising - transformer networks are designed to learn the complex representations of the underlying causes that bring about things like human generated text, audio, and video.
If you modeled a star with a large transformer model, you would end up with semantic structures and representations that correlate to complex dynamic systems within the star. If you model slug cellular growth, you'll get structure and semantics corresponding to slug DNA. Transformers aren't the end-all solution - the paradigm is missing a level of abstraction that fully generalizes across all domains, but it's a really good way to elicit complex functions from sophisticated systems, and by contrasting the way in which those models fail against the way natural systems operate, we'll find better, more general methods and architectures, until we cross the threshold of fully general algorithms.
Biological brains are a computational substrate - we exist as brains in bone vats, connected to a wonderfully complex and sophisticated sensor suite and mobility platform that feeds electrically activated sensory streams into our brains, which get processed into a synthetic construct we experience as reality.
Part of the underlying basic functioning of our brains is each individual column performing the task of predicting which of any of the columns it's connected to will fire next. The better a column is at predicting, the better the brain gets at understanding the world, and biological brains are recursively granular across arbitrary degrees of abstraction.
LLMs aren't inherently incapable of fully emulating human cognition, but the differences they exhibit are expensive. It's going to be far more efficient to modify the architecture, and this may diverge enough that whatever the solution ends up being, it won't reasonably be called an LLM. Or it might not, and there's some clever tweak to things that will push LLMs over the threshold.
the issue with AI and AI-naysayers is, by analogy, this: cars were build to drive from A to Z. people picked up tastes and some people started building really cool looking cars. the same happens on the engineering side. then portfolio communists came with their fake capitalism and now cars are build to drive over people but don't really work because people, thankfully, are overwhelming still fighting to attempt to act towards their own intents.
Predict the right words, predict the answer, predict when the ball bounces, etc. Then reversing predictions that we have learned. I.e. choosing the action with the highest prediction of the outcome we want. Whether that is one step, or a series of predicted best steps.
Also, people confuse different levels of algorithm.
There are at least 4 levels of algorithm:
• 1 - The architecture.
This input-output calculation for pre-trained models are very well understood. We put together a model consisting of matrix/tensor operations and few other simple functions, and that is the model. Just a normal but high parameter calculation.
• 2 - The training algorithm.
These are completely understood.
There are certainly lots of questions about what is most efficient, alternatives, etc. But training algorithms harnessing gradients and similar feedback are very clearly defined.
• 3 - The type of problem a model is trained on.
Many basic problem forms are well understood. For instance, for prediction we have an ordered series of information, with later information to be predicted from earlier information. It could simply be an input and response that is learned. Or a long series of information.
• 4 - The solution learned to solve (3) the outer problem, using (2) the training algorithm on (1) the model architecture.
People keep confusing (4) with (1), (2) or (3). But it is very different.
For starters, in the general case, and for most any challenging problem, we never understand their solution. Someday it might be routine, but today we don't even know how to approach that for any significant problem.
Secondly, even with (1), (2), and (3) exactly the same, (4) is going to be wildly different based on the data characterizing the specific problem to solve. For complex problems, like language, layers and layers of sub-solutions to sub-problems have to be solved, and since models are not infinite in size, ways to repurpose sub-solutions, and weave together sub-solutions to address all the ways different sub-problems do and don't share commonalities.
Yes, prediction is the outer form of their solution. But to do that they have to learn all the relationships in the data. And there is no limit to how complex relationships in data can be. So there is no limit on the depths or complexity of the solutions found by successfully trained models.
Any argument they don't reason, based on the fact that they are being trained to predict, confuses at least (3) and (4). That is a category error.
It is true, they reason a lot more like our "fast thinking", intuitive responses, than our careful deep and reflective reasoning. And they are missing important functions, like a sense of what they know or don't. They don't continuously learn while inferencing. Or experience meta-learning, where they improve on their own reasoning abilities with reflection, like we do. And notoriously, by design, they don't "see" the letters that spell words in any normal sense. They see tokens.
Those reasoning limitations can be irritating or humorous. Like when a model seems to clearly recognize a failure you point out, but then replicates the same error over and over. No ability to learn on the spot. But they do reason.
Today, despite many successful models, nobody understands how models are able to reason like they do. There is shallow analysis. The weights are there to experiment with. But nobody can walk away from the model and training process, and build a language model directly themselves. We have no idea how to independently replicate what they have learned, despite having their solution right in front of us. Other than going through the whole process of retraining another one.
The illusion wears off after about half an hour for even the most casual users. That's better than the old chatbots, but they're still chatbots.
Did anyone ever seriously buy the whole "it's thinking" BS when it was Markov chains? What makes you believe today's LLMs are meaningfully different?
The truth is that the evidence says we don't. See the Libet experiment and its many replications.
Your decisions can be predicted from brain scans up to 10 seconds before you make them, which means they are as deterministic as an LLM's. Sorry, I guess.
This conclusion does not follow from the result at all.
It makes sense if you're desperate for free will to be real, but you really have to work for it. Especially when you add in the countless other studies showing that a lot of the reasons we give for our actions, especially in quick or ambiguous choices, are confabulationalist post-hoc constructions. Our own introspection seems mostly to consist of just "making stuff up" to justify the decisions we've already made.
I mean, a reasonable person could argue their way past all the evidence without totally denying it, but "free will" just isn't the simplest explanation that fits the available data. It's possible that free will exists in the same way it's possible that Russels teapot exists.
But beyond that, what do you want to say here? What is lost, what is gained? Are you wanting to say this makes us more like an LLM? How so?
"Implications
The experiment raised significant questions about free will and determinism. While it suggested that unconscious brain activity precedes conscious decision-making, Libet argued that this does not negate free will, as individuals can still choose to suppress actions initiated by unconscious processes."
It's pretty hard to argue that you're really "free" to make a different decision if your body knew which you would choose 7 seconds before you became aware of it.
I mean, those long term predictions were only something like 60% accurate, but still, the preponderance of evidence says that those decisions are deterministic and we keep finding new ways to predict the outcome sooner and with higher accuracy.
Clearly, that conclusion would be patently absurd to draw from that experiment. There are so many expectation and observation effects that go into the very setup from the beginning. Humans generally follow directions, particularly when a guy in a labcoat is giving them.
> At some point, when they felt the urge to do so, they were to freely decide between one of two buttons, operated by the left and right index fingers, and press it immediately. [0]
Wow. TWO whole choices to choose from! Human minds tend to pre-think their choice between one of two fingers to wiggle, therefore free will doesn't exist.
> It's pretty hard to argue that you're really "free" to make a different decision if your body knew which you would choose 7 seconds before you became aware of it.
To really spell it out since the analogy/satire may be lost: You're free to refrain from pressing either button during the prompt. You're free to press both buttons at the same time. You're free to mash them rapidly and randomly throughout the whole experiment. You're free to walk into the fMRI room with a bag full of steel BB's and cause days of downtime and thousands of dollars in damage. Folks generally don't do those things because of conditioning.
[0] - http://behavioralhealth2000.com/wp-content/uploads/2017/10/U...
Certainly we can come up with some alternative theories (like "free will") to explain it all away, but the simplest (therefore most likely correct) answer is just that we're basically statistical state machines and are as deterministic as a similar computational system.
To be clear, I'm not saying that metacognition doesn't exist. Just that I've never seen any reason to believe it's very different from current thinking models that just feed an output back in as another input.
[0] - https://home.csulb.edu/~cwallis/382/readings/482/nisbett%20s...
That said, I think the author's use of "bag of words" here is a mistake. Not only does it have a real meaning in a similar area as LLMs, but I don't think the metaphor explains anything. Gen AI tricks laypeople into treating its token inferences as "thinking" because it is trained to replicate the semiotic appearance of doing so. A "bag of words" doesn't sufficiently explain this behavior.
Person-metaphor does nothing to explain its behavior, either.
"Bag of words" has a deep origin in English, the Anglo-Saxon kenning "word-hord", as when Beowulf addresses the Danish sea-scout (line 258)
"He unlocked his word-hoard and delivered this answer."
So, bag of words, word-treasury, was already a metaphor for what makes a person a clever speaker.
The contra-positive of "All LLMs are not thinking like humans" is "No humans are thinking like LLMs"
And I do not believe we actually understand human thinking well enough to make that assertion.
Indeed, it is my deep suspicion that we will eventually achieve AGI not by totally abandoning today's LLMs for some other paradigm, but rather embedding them in a loop with the right persistence mechanisms.
Its useful, it's amazing, but as the original text says, thinking of it as "some intelligence with reasoning " makes us use the wrong mental models for it.
If instead of a chat interface we simply had a "complete the phrase" interface, people would understand the tool better for what it is.
The fact that pretraining of ChatGPT is done with a "completing the phrase" task has no bearing on how people actually end up using it.
> Gen AI tricks laypeople into treating its token inferences as "thinking" because it is trained to replicate the semiotic appearance of doing so. A "bag of words" doesn't sufficiently explain this behavior.
Something about there being significant overlap between the smartest bears and the dumbest humans. Sorry you[0] were fooled by the magic bag.
[0] in the "not you, the layperson in question" sense
Whenever the comment section takes a long hit and goes "but what is thinking, really" I get slightly more cynical about it lol
By now, it's pretty clear that LLMs implement abstract thinking - as do humans.
They don't think exactly like humans do - but they sure copy a lot of human thinking, and end up closer to it than just about anything that's not a human.
It can kinda sorta look like thinking if you don't have a critical eye, but it really doesn't take much to break the illusion.
I really don't get this obsessive need to pretend your tools are alive. Y'all know when you watch YouTube that it's a trick and the tiny people on your screen don't live in your computer, right?
The answer to that is the siren song of "AI effect".
Even admitting "we don't know" requires letting go of the idea that "thinking" must be exclusive to humans. And many are far too weak to do that.
I feel that's more a description of a search engine. Doesn't really give an intuition of why LLMs can do the things they do (beyond retrieval), or where/why they'll fail.
"Self-awareness" used in a purely mechanical sense here: having actionable information about itself and its own capabilities.
If you ask an old LLM whether it's able to count the Rs in "strawberry" successfully, it'll say "yes". And then you ask it to do so, and it'll say "2 Rs". It doesn't have the self-awareness to know the practical limits of its knowledge and capabilities. If it did, it would be able to work around the tokenizer and count the Rs successfully.
That's a major pattern in LLM behavior. They have a lot of capabilities and knowledge, but not nearly enough knowledge of how reliable those capabilities are, or meta-knowledge that tells them where the limits of their knowledge lie. So, unreliable reasoning, hallucinations and more.
Anthropic has discovered that this is definitely the case for name recognition, and I suspect that names aren't the only things subject to a process like that.
My second thought is that it's not the metaphor that is misleading. People have been told thousands of times that LLMs don't "think", don't "know", don't "feel", but are "just a very impressive autocomplete". If they still really want to completely ignore that, why would they suddenly change their mind with a new metaphor?
Humans are lazy. If it looks true enough and it cost less effort, humans will love it. "Are you sure the LLM did your job correctly?" is completely irrelevant: people couldn't care less if it's correct or not. As long as the employer believes that the employee is "doing their job", that's good enough. So the question is really: "do you think you'll get fired if you use this?". If the answer is "no, actually I may even look more productive to my employer", then why would people not use it?
Yes, subconsciously I kept trying to map this article's ideas to word2vec and continuous-bag-of-words.
Woah, that hit hard
Sure, this is not the same as being a human. Does that really mean, as the author seems to believe without argument, that humans need not be afraid that it will usurp their role? In how many contexts is the utility of having a human, if you squint, not just that a human has so far been the best way to "produce the right words in any given situation", that is, to use the meat-bag only in its capacity as a word-bag? In how many more contexts would a really good magic bag of words be better than a human, if it existed, even if the current human is used somewhat differently? The author seems to rest assured that a human (long-distance?) lover will not be replaced by a "bag of words"; why, especially once the bag of words is also ducttaped to a bag of pictures and a bag of sounds?
I can just imagine someone - a horse breeder, or an anthropomorphised horse - dismissing all concerns on the eve of the automotive revolution, talking about how marketers and gullible marks are prone to hippomorphising anything that looks like it can be ridden and some more, and sprinkling some anecdotes about kids riding broomsticks, legends of pegasi and patterns of stars in the sky being interpreted as horses since ancient times.
Neither of these is entirely true in all cases, but they could be expected to remain true in at least some (many) cases, and so the role for humans remains.
There's a quote I love but have misplaced, from the 19th century I think. "Our bodies are just contraptions for carrying our heads around." Or in this instance... bag of words transport system ;)
I mean I use AI tools to help achieve the goal but I don’t see any signs of the things I’m building and doing being unreliable.
Either way, in what way is this relevant? If the human's labor is not useful at any price point to any entity with money, food or housing, then they presumably will not get paid/given food/housing for it.
I stumbled across a good-enough analogy based on something she loves: refrigerator magnet poetry, which if it's good consists of not just words but also word fragments like "s", "ed", and "ing" kinda like LLM tokens. I said that ChatGPT is like refrigerator magnet poetry in a magical bag of holding that somehow always gives the tile that's the most or nearly the most statistically plausible next token given the previous text. E.g., if the magnets already up read "easy come and easy ____", the bag would be likely to produce "go". That got into her head the idea that these things operate based on plausibility ratings from a statistical soup of words, not anything in the real world nor any internal cogitation about facts. Any knowledge or thought apparent in the LLM was conducted by the original human authors of the words in the soup.
Did she ask if a "statistical soup of words," if large enough, might somehow encode or represent something a little more profound than just a bunch of words?
That said, I was struck by a recent interview with Anthropic’s Amanda Askell [2]. When she talks, she anthropomorphizes LLMs constantly. A few examples:
“I don't have all the answers of how should models feel about past model deprecation, about their own identity, but I do want to try and help models figure that out and then to at least know that we care about it and are thinking about it.”
“If you go into the depths of the model and you find some deep-seated insecurity, then that's really valuable.”
“... that could lead to models almost feeling afraid that they're gonna do the wrong thing or are very self-critical or feeling like humans are going to behave negatively towards them.”
[1] https://www.anthropic.com/research/team/interpretability
Their vivid descriptions of what the Emperor could be wearing doesn't make said emperor any less nakey.
Can you give some concrete examples? The link you provided is kind of opaque
>Amanda Askell [2]. When she talks, she anthropomorphizes LLMs constantly.
She is a philosopher by trade and she describes her job (model alignment) as literally to ensure models "have good character traits." I imagine that explains a lot
https://www.anthropic.com/news/golden-gate-claude
Excerpt: “We found that there’s a specific combination of neurons in Claude’s neural network that activates when it encounters a mention (or a picture) of this most famous San Francisco landmark.”
https://www.anthropic.com/research/tracing-thoughts-language...
Excerpt: “Recent research on smaller models has shown hints of shared grammatical mechanisms across languages. We investigate this by asking Claude for the ‘opposite of small’ across different languages, and find that the same core features for the concepts of smallness and oppositeness activate, and trigger a concept of largeness, which gets translated out into the language of the question.”
https://www.anthropic.com/research/introspection
Excerpt: “Our new research provides evidence for some degree of introspective awareness in our current Claude models, as well as a degree of control over their own internal states.”
My fridge happily reads inputs without consciousness, has goals and takes decisions without "thinking", and consistently takes action to achieve those goals. (And it's not even a smart fridge! It's the one with a copper coil or whatever.)
I guess the cybernetic language might be less triggering here (talking about systems and measurements and control) but it's basically the same underlying principles. One is just "human flavored" and I therefore more prone to invite unhelpful lines of thinking?
Except that the "fridge" in this case is specifically and explicitly designed to emulate human behavior so... you would indeed expect to find structures corresponding to the patterns it's been designed to simulate.
Wondering if it's internalized any other human-like tendencies — having been explicitly trained to simulate the mechanisms that produced all human text — doesn't seem too unreasonable to me.
I did a simple experiment - took a photo of my kid in the park, showed it to Gemini and asked for a "detailed description". Then I took that description and put it into a generative model (Z-Image-Turbo, a new one). The output image was almost identical.
So one model converted image to text, the other reversed the processs. The photo was completely new, personal, never put online. So it was not in any training set. How did these 2 models do it if not actually using language like a thinking agent?
https://pbs.twimg.com/media/G7gTuf8WkAAGxRr?format=jpg&name=...
By having a gazillion of other, almost identical pictures of kids in parks in their training data.
I've completely given up on using LLMs for anything more than a typing assistant / translator and maybe an encyclopedia when I don't care about correctness.
All useful shorthands, all which lead to people displaying fundamental misunderstandings of what they're talking about - i.e. expressing surprise that a nation of millions doesn't display consistency of behavior of human lifetime scales, even though fairly obviously the mechanisms of government are churning their make up constantly, and depending on context maybe entirely different people.
For example, if you've worked at a large company, one of the little tragedies is when someone everyone likes gets laid off. There were probably no people who actively wanted Bob to lose his job. Even the CEO/Board who pulled the trigger probably had nothing against Bob. Heck, they might be the next ones out the door. The company is faceless, yet it wanted Bob to go, because that apparently contributed to the company's objective function. Had the company consisted entirely of different people, plus Bob, Bob might have been laid off anyway.
There is a strong will to do ... things the emerges from large structures of people and technology. It's funny like that.
I also know that we data and tech folks will probably never win the battle over anthropomorphization.
The average user of AI, nevermind folks who should know better, is so easily convinced that AI "knows," "thinks," "lies," "wants," "understands," etc. Add to this that all AI hosts push this perspective (and why not, it's the easiest white lie to get the user to act so that they get a lot of value), and there's really too much to fight against.
We're just gonna keep on running into this and it'll just be like when you take chemistry and physics and the teachers say, "it's not actually like this but we'll get to how some years down the line- just pretend this is true for the time being."
"We don't really know how human consciousness works, but the LLM resembles things we associate with thought, therefore it is thought."
I think most people would agree that the functioning of an LLM resembles human thought, but I think most people, even the ones who think that LLMs can think, would agree that LLMs don't think in the exact same way that a human brain does. At best, you can argue that whatever they are doing could be classified as "thought" because we barely have a good definition for the word in the first place.
I hear a lot of people saying "it's certainly not and cannot be thought" and then "it's not exactly clear how to delineate these things or how to detect any delineations we might want."
The average human is so easily convinced that humans "know", "think", "lie", "want", "understand", etc.
But really it's all just a probabilistic chain reaction of electrochemical and thermal interactions. There is literally nowhere in the brain's internals for anything like "knowing" or "thinking" or "lying" to happen!
Strange that we have to pretend otherwise
There you go again, auto-morphizing the meat-bags. Vroom vroom.
This is a fundamentally interesting point. Taking your comment as HN would advise, I totally agree.
I think genAI freaks a lot of people out because it makes them doubt what they thought made them special.
And to your comment, humans have always used words they reserve for humanity that indicates we're special: that we think, feel, etc... That we're human. Maybe we're not so special. Maybe that's scary to a lot of people.
(And I was about to react with
"In 2025 , ironically, a lot of anti-anthropomorphization is actually anthropocentrism with a moustache."
I'll have to save it for the next debate)
That was their point. Or rather, that the analogous argument about the underpinnings of LLMs is similarly unconvincing regarding the issue of thought or understanding.
Consciousness is not computation. You need something else.
On the flip side: If you do that, YOU are conscious and intelligent.
Would it mean that the machine that did the computation became conscious when it did it?
What is consciousness?
Consciousness is what it "feels like" when a part of the universe is engaged in local entropy reduction. You heard it here first, folks!
So the next definition of detecting "thinking" will have to be externally observable and inferrable like a Turing Test, but get into the other things that we consider part of consciousness/thinking.
Often this is some combination of introspection (understanding internal states), perception (understanding external objects), and synthesis of the two into testable hypotheses in some sort of feedback loop between the internal representation of the world and the external feedback from the world.
Right now, a chatbot can say all sorts of things about itself and about the world, but none of that is based on real-time, factual information. Whereas an animal can't speak, but they clearly process information and consider it when determining their future and current actions.
It is... such a retrospective narrative. It's so obvious that the author learned about this example first than came with the reasoning later, just to fit in his view of LLM.
Imaging if ChatGPT answered this question correctly. Would that change the author's view? Of course not! They'll just say:
> “Bag of words” is a also a useful heuristic for predicting where an AI will do well and where it will fail. Who reassigned the species Brachiosaurus brancai to its own genus, and when?” is an easy task for a bag of words, because the information has appeared in the words it memorizes.
I highly doubt this author has predicted that "bag of Words" can do image editing before OpenAI released that.
This is because there are many words about how to do web searches.
and got ths correct reply from the "Bag of Words"
The species Brachiosaurus brancai was reassigned to its own genus by Michael P. Taylor in 2009 — he transferred it to the new genus Giraffatitan. BioOne +2 Mike Taylor +2
How that happened:
Earlier, in 1988, Gregory S. Paul had proposed putting B. brancai into a subgenus as Brachiosaurus (Giraffatitan) brancai, based on anatomical differences. Fossil Wiki +1
Then in 1991, George Olshevsky used the name Giraffatitan brancai — but his usage was in a self-published list and not widely adopted. Wikipedia +1
Finally, in 2009 Taylor published a detailed re-evaluation showing at least 26 osteological differences between the African material (brancai) and the North American type species Brachiosaurus altithorax — justifying full generic separation. BioOne +1
If you like — I can show a short timeline of all taxonomic changes of B. brancai.
--
As an author, you should write things that are tested or at least true. But they did a pretty bad job of testing this and are making assumptions that are not true. Then they're basing their argument/reasoning (restrospectively) on assumptions not gounded in reality.
GIGO has an obvious Nothing-In-Nothing-Out trivial case.
The more human works I've read the more I feel meat intelligences are not that different from tensor intelligences.
This always contrasts with articles written by tech people and for tech people. They usually try to convey some information and maybe give some arguments for their position on some topic, but they are always concise and don't wallow in literary devices.