The author largely takes the view that it is more productive for us to ignore any anthropomorphic representations and focus on the more concrete, material, technical systems - I’m with them there… but only to a point. The flip side of all this is of course the idea that there is still something emergent, unplanned, and mind-like. So even if it is a stochastic system following rules, clearly the rules are complex enough (to the tune of billions of operations, with signals propagating through some sort of resonant structure, if you take a more filter impulse response like view of a sequential matmuls) to result in emergent properties. Even if we (people interested in LLMs with at least some level of knowledge of ML mathematics and systems) “know better” than to believe these systems to possess morals, ethics, feelings, personalities, etc, the vast majority of people do not have any access to meaningful understanding of the mathematical, functional representation of an LLM and will not take that view, and for all intents and purposes the systems will at least seem to have those anthropomorphic properties, and so it seems like it is in fact useful to ask questions from that lens as well.
In other words, just as it’s useful to analyze and study these things as the purely technical systems they ultimately are, it is also, probably, useful to analyze them from the qualitative, ephemeral, experiential perspective that most people engage with them from, no?
Why would you ever want to amplify a false understanding that has the potential to affect serious decisions across various topics?
LLMs reflect (and badly I may add) aspects of the human thought process. If you take a leap and say they are anything more than that, you might as well start considering the person appearing in your mirror as a living being.
Literally (and I literally mean it) there is no difference. The fact that a human image comes out of a mirror has no relation what so ever with the mirror's physical attributes and functional properties. It has to do just with the fact that a man is standing in front of it. Stop feeding the LLM with data artifacts of human thought and will imediatelly stop reflecting back anything resembling a human.
I think it is inevitable that some - many - people will come to the conclusion that these systems have “ethics”, “morals,” etc, even if I or you personally do not think they do. Given that many people may come to that conclusion though, regardless of if the systems do or do not “actually” have such properties, I think it is useful and even necessary to ask questions like the following: “if someone engages with this system, and comes to the conclusion that it has ethics, what sort of ethics will they be likely to believe the system has? If they come to the conclusion that it has ‘world views,’ what ‘world views’ are they likely to conclude the system has, even if other people think it’s nonsensical to say it has world views?”
> The fact that a human image comes out of a mirror has no relation what so ever with the mirror's physical attributes and functional properties. It has to do just with the fact that a man is standing in front of it.
Surely this is not quite accurate - the material properties - surface roughness, reflectivity, geometry, etc - all influence the appearance of a perceptible image of a person. Look at yourself in a dirty mirror, a new mirror, a shattered mirror, a funhouse distortion mirror, a puddle of water, a window… all of these produce different images of a person with different attendant phenomenological experiences of the person seeing their reflection. To take that a step further - the entire practice of portrait photography is predicated on the idea that the collision of different technical systems with the real world can produce different semantic experiences, and it’s the photographer’s role to tune and guide the system to produce some sort of contingent affect on the person viewing the photograph at some point in the future. No, there is no “real” person in the photograph, and yet, that photograph can still convey something of person-ness, emotion, memory, etc etc. This contingent intersection of optics, chemical reactions, lighting, posture, etc all have the capacity to transmit something through time and space to another person. It’s not just a meaningless arrangement of chemical structures on paper.
> Stop feeding the LLM with data artifacts of human thought and will imediatelly stop reflecting back anything resembling a human.
But, we are feeding it with such data artifacts and will likely continue to do so for a while, and so it seems reasonable to ask what it is “reflecting” back…
We know that Newton's laws are wrong, and that you have to take special and general relativity into account. Why would we ever teach anyone Newton's laws any more?
For people who have only a surface-level understanding of how they work, yes. A nuance of Clarke's law that "any sufficiently advanced technology is indistinguishable from magic" is that the bar is different for everybody and the depth of their understanding of the technology in question. That bar is so low for our largely technologically-illiterate public that a bothersome percentage of us have started to augment and even replace religious/mystical systems with AI powered godbots (LLMs fed "God Mode"/divination/manifestation prompts).
(1) https://www.spectator.co.uk/article/deus-ex-machina-the-dang... (2) https://arxiv.org/html/2411.13223v1 (3) https://www.theguardian.com/world/2025/jun/05/in-thailand-wh...
It’s astounding to me that so much of HN reacts so emotionally to LLMs, to the point of denying there is anything at all interesting or useful about them. And don’t get me started on the “I am choosing to believe falsehoods as a way to spite overzealous marketing” crowd.
You mean that LLMs are more than just the matmuls they're made up of, or that that is exactly what they are and how great that is?
Is it too anthropomorphic to say that this is a lie? To say that the hidden state and its long term predictions amount to a kind of goal? Maybe it is. But we then need a bunch of new words which have almost 1:1 correspondence to concepts from human agency and behavior to describe the processes that LLMs simulate to minimize prediction loss.
Reasoning by analogy is always shaky. It probably wouldn't be so bad to do so. But it would also amount to impenetrable jargon. It would be an uphill struggle to promulgate.
Instead, we use the anthropomorphic terminology, and then find ways to classify LLM behavior in human concept space. They are very defective humans, so it's still a bit misleading, but at least jargon is reduced.
These LLMs are almost always, to my knowledge, autoregressive models, not recurrent models (Mamba is a notable exception).
Intermediate activations isn't "state". The tokens that have already been generated, along with the fixed weights, is the only data that affects the next tokens.
The 'hidden state' being referred to here is essentially the "what might have been" had the dice rolls gone differently (eg, been seeded differently).
eg. pick 'the' as the next token because there's a strong probability of 'planet' as the token after?
is it only past state that influences the choice of 'the'? or that the model is predicting many tokens in advance and only returning the one in the output?
if it does predict many, id consider that state hidden in the model weights.
https://www.anthropic.com/research/tracing-thoughts-language...
Telling us to just go and learn the math is a little hurtful and doesn't really get me any closer to learning the math. It gives gatekeeping.
The "transformer" part isn't under question. It's the "hidden state" part.
People are excited about the technology and it's easy to use the terminology the vendor is using. At that point I think it gets kind of self fulfilling. Kind of like the meme about how to pronounce GIF.
But yes, anthropomorphising LLMs is inevitable because they feel like an entity. People treat stuffed animals like creatures with feelings and personality; LLMs are far closer than that.
It takes great marketing to actually have any character and intent at all.
Children do, some times, but it's a huge sign of immaturity when adults, let alone tech workers, do it.
I had a professor at University that would yell at us if/when we personified/anthropomorphized the tech, and I have that same urge when people ask me "What does <insert LLM name here> think?".
Would this question be clear for a human? If so, it is probably clear for an LLM. Did I provide enough context for a human to diagnose the problem? Then an LLM will probably have a better chance of diagnosing the problem. Would a human find the structure of this document confusing? An LLM would likely perform poorly when reading it as well.
Re-applying human intuitions to LLMs is a good starting point to gaining intuition about how to work with LLMs. Conversely, understanding sequences of tokens and probability spaces doesn't give you much intuition about how you should phrase questions to get good responses from LLMs. The technical reality doesn't explain the emergent behaviour very well.
I don't think this is mutually exclusive with what the author is talking about either. There are some ways that people think about LLMs where I think the anthropomorphization really breaks down. I think the author says it nicely:
> The moment that people ascribe properties such as "consciousness" or "ethics" or "values" or "morals" to these learnt mappings is where I tend to get lost.
Whereas LSTM, or structured state space for example have a state that is updated and not tied to a specific item in the sequence.
I would argue that his text is easily understandable except for the notation of the function, explaining that you can compute a probability based on previous words is understandable by everyone without having to resort to anthropomorphic terminology
There is plenty of state not visible when an LLM starts a sentence that only becomes somewhat visible when it completes the sentence. The LLM has a plan, if you will, for how the sentence might end, and you don't get to see an instance of that plan unless you run autoregression far enough to get those tokens.
Similarly, it has a plan for paragraphs, for whole responses, for interactive dialogues, plans that include likely responses by the user.
how do we get 100 tokens of completion, and not just one output layer at a time?
are there papers youve read that you can share that support the hypothesis? vs that the LLM doesnt have ideas about the future tokens when its predicting the next one?
https://www.anthropic.com/research/tracing-thoughts-language...
See section “Does Claude plan its rhymes?”?
It may not be as evident now as it was with earlier models. The models will fabricate preconditions needed to output the final answer it "wanted".
I ran into this when using quasi least-to-most style structured output.
Arguably there's reason to believe it comes up with a plan when it is computing token propabilities, but it does not store it between tokens. I.e. it doesn't possess or "have" it. It simply comes up with a plan, emits a token, and entirely throws all its intermediate thoughts (including any plan) to start again from scratch on the next token.
And I'm baffled that the AI discussions seem to never move away from treating a human as something other than a function to generate sequences of words!
Oh, but AI is introspectable and the brain isn't? fMRI and BCI are getting better all the time. You really want to die on the hill that the same scientific method that predicts the mass of an electron down to the femtogram won't be able to crack the mystery of the brain? Give me a break.
This genre of article isn't argument: it's apologetics. Authors of these pieces start with the supposition there is something special about human consciousness and attempt to prove AI doesn't have this special quality. Some authors try to bamboozle the reader with bad math. Other others appeal to the reader's sense of emotional transcendence. Most, though, just write paragraph after paragraph of shrill moral outrage at the idea an AI might be a mind of the same type (if different degree) as our own --- as if everyone already agreed with the author for reasons left unstated.
I get it. Deep down, people want meat brains to be special. Perhaps even deeper down, they fear that denial of the soul would compel us to abandon humans as worthy objects of respect and possessors of dignity. But starting with the conclusion and working backwards to an argument tends not to enlighten anyone. An apology inhabits the form of an argument without edifying us like an authentic argument would. What good is it to engage with them? If you're a soul non-asserter, you're going to have an increasingly hard time over the next few years constructing a technical defense of meat parochialism.
> a human as something other than a function to generate sequences of words!
Humans have more structure than just beings that say words. They have bodies, they live in cooperative groups, they reproduce, etc.
Yeah. We've become adequate at function-calling and memory consolidation.
But ultimately LLMs also in a way are trained for survival, since an LLM that fails the tests might not get used in future iterations. So for LLMs it is also survival that is the primary driver, then there will be the subgoals. Seemingly good next token prediction might or might not increase survival odds.
Essentially there could arise a mechanism where they are not really truly trying to generate the likeliest token (because there actually isn't one or it can't be determined), but whatever system will survive.
So an LLM that yields in perfect theoretical tokens (we really can't verify though what are the perfect tokens), could be less likely to survive than an LLM that develops an internal quirk, but the quirk makes them most likely to be chosen for the next iterations.
If the system was complex enough and could accidentally develop quirks that yield in a meaningfully positive change although not in necessarily next token prediction accuracy, could be ways for some interesting emergent black box behaviour to arise.
Our own consciousness comes out of an evolutionary fitness landscape in which _our own_ ability to "predict next token" became a survival advantage, just like it is for LLMs. Imagine the tribal environment: one chimpanzee being able to predict the actions of another gives that first chimpanzee a resources and reproduction advantage. Intelligence in nature is a consequence of runaway evolution optimizing fidelity of our _theory of mind_! "Predict next ape action" eerily similar to "predict next token"!
I think this is sometimes semi-explicit too. For example, this 2017 OpenAI paper on Evolutionary Algorithms [0] was pretty influential, and I suspect (although I'm an outsider to this field so take it with a grain of salt) that some versions of reinforcement learning that scale for aligning LLMs borrow some performance tricks from OpenAIs genetic approach.
Clearly computers are deterministic. Are people?
> Clearly computers are deterministic. Are people?
Give an LLM memory and a source of randomness and they're as deterministic as people.
"Free will" isn't a concept that typechecks in a materialist philosophy. It's "not even wrong". Asserting that free will exists is _isomorphic_ to dualism which is _isomorphic_ to assertions of ensoulment. I can't argue with dualists. I reject dualism a priori: it's a religious tenet, not a mere difference of philosophical opinion.
So, if we're all materialists here, "free will" doesn't make any sense, since it's an assertion that something other than the input to a machine can influence its output.
However, this information protection similarity applies to single-celled microbes as much as it does to people, so the question also resolves to whether microbes are deterministic. Microbes both contain and exist in relatively dynamic environments so tiny differences in initial state may lead to different outcomes, but they're fairly deterministic, less so than (well-designed) computers.
With people, while the neural structures are programmed by the cellular DNA, once they are active and energized, the informational flow through the human brain isn't that deterministic, there are some dozen neurotransmitters modulating state as well as huge amounts of sensory data from different sources - thus prompting a human repeatedly isn't at all like prompting an LLM repeatedly. (The human will probably get irritated).
Yes boss, it can reach mars by 2020, you're smart to invest in it and clearly knows about space.
Yes boss, it can cure cancer, you're smart to invest in it and clearly knows about biology.
Because, morals, values, consciousness etc could just be subgoals that arised through evolution because they support the main goals of survival and procreation.
And if it is baffling to think that a system could rise up, how do you think it is possible life and humans came to existence in the first place? How could it be possible? It is already happened from a far unlikelier and strange place. And wouldn't you think the whole World and the timeline in theory couldn't be represented as a deterministic function. And if not then why should "randomness" or anything else bring life to existence.
It is similar to how human brains operate. LLMs are the (current) culmination of at least 80 years of research on building computational models of the human brain.
Humans make a bad choice, it can end said human's life. The worst choice a LLM makes just gets told "no, do it again, let me make it easier"
Ultimately this matters from evolutionary evolvement and survival of the fittest idea, but it makes the question of "identity" very complex. But death will matter because this signals what traits are more likely to keep going into new generations, for both humans and LLMs.
Death, essentially for an LLM would be when people stop using it in favour of some other LLM performing better.
This is such a bizarre take.
The relation associating each human to the list of all words they will ever say is obviously a function.
> almost magical human-like powers to something that - in my mind - is just MatMul with interspersed nonlinearities.
There's a rich family of universal approximation theorems [0]. Combining layers of linear maps with nonlinear cutoffs can intuitively approximate any nonlinear function in ways that can be made rigorous.
The reason LLMs are big now is that transformers and large amounts of data made it economical to compute a family of reasonably good approximations.
> The following is uncomfortably philosophical, but: In my worldview, humans are dramatically different things than a function . For hundreds of millions of years, nature generated new versions, and only a small number of these versions survived.
This is just a way of generating certain kinds of functions.
Think of it this way: do you believe there's anything about humans that exists outside the mathematical laws of physics? If so that's essentially a religious position (or more literally, a belief in the supernatural). If not, then functions and approximations to functions are what the human experience boils down to.
[0] https://en.wikipedia.org/wiki/Universal_approximation_theore...
You appear to be disagreeing with the author and others who suggest that there's some element of human consciousness that's beyond than what's observable from the outside, whether due to religion or philosophy or whatever, and suggesting that they just not do that.
In my experience, that's not a particularly effective tactic.
Rather, we can make progress by assuming their predicate: Sure, it's a room that translates Chinese into English without understanding, yes, it's a function that generates sequences of words that's not a human... but you and I are not "it" and it behaves rather an awful lot like a thing that understands Chinese or like a human using words. If we simply anthropomorphize the thing, acknowledging that this is technically incorrect, we can get a lot closer to predicting the behavior of the system and making effective use of it.
Conversely, when speaking with such a person about the nature of humans, we'll have to agree to dismiss the elements that are different from a function. The author says:
> In my worldview, humans are dramatically different things than a function... In contrast to an LLM, given a human and a sequence of words, I cannot begin putting a probability on "will this human generate this sequence".
Sure you can! If you address an American crowd of a certain age range with "We’ve got to hold on to what we’ve got. It doesn’t make a difference if..." I'd give a very high probability that someone will answer "... we make it or not". Maybe that human has a unique understanding of the nature of that particular piece of pop culture artwork, maybe it makes them feel things that an LLM cannot feel in a part of their consciousness that an LLM does not possess. But for the purposes of the question, we're merely concerned with whether a human or LLM will generate a particular sequence of words.
I agree my approach is unlikely to win over the author or other skeptics. But after years of seeing scientists waste time trying to debate creationists and climate deniers I've kind of given up on trying to convince the skeptics. So I was talking more to HN in general.
> You appear to be disagreeing with the author and others who suggest that there's some element of human consciousness that's beyond than what's observable from the outside
I'm not sure what it means to be observable or not from the outside. I think this is at least partially because I don't know what it means to be inside either. My point was just that whatever consciousness is, it takes place in the physical world and the laws of physics apply to it. I mean that to be as weak a claim as possible: I'm not taking any position on what consciousness is or how it works etc.
Searle's Chinese room argument attacks attacks a particular theory about the mind based essentially turing machines or digital computers. This theory was popular when I was in grad school for psychology. Among other things, people holding the view that Searle was attacking didn't believe that non-symbolic computers like neural networks could be intelligent or even learn language. I thought this was total nonsense, so I side with Searle in my opposition to it. I'm not sure how I feel about the Chinese room argument in particular, though. For one thing it entirely depends on what it means to "understand" something, and I'm skeptical that humans ever "understand" anything.
> If we simply anthropomorphize the thing, acknowledging that this is technically incorrect, we can get a lot closer to predicting the behavior of the system and making effective use of it.
I see what you're saying: that a technically incorrect assumption can bring to bear tools that improve our analysis. My nitpick here is I agree with OP that we shouldn't anthropomorphize LLMs, any more than we should anthropomorphize dogs or cats. But OP's arguments weren't actually about anthropomorphizing IMO, they were about things like functions that are more fundamental than humans. I think artificial intelligence will be non-human intelligence just like we have many examples of non-human intelligence in animals. No attribution of human characteristics needed.
> If we simply anthropomorphize the thing, acknowledging that this is technically incorrect, we can get a lot closer to predicting the behavior of the system and making effective use of it.
Yes I agree with you about your lyrics example. But again here I think OP is incorrect to focus on the token generation argument. We all agree human speech generates tokens. Hopefully we all agree that token generation is not completely predictable. Therefore it's by definition a randomized algorithm and it needs to take an RNG. So pointing out that it takes an RNG is not a valid criticism of LLMs.
Unless one is a super-determinist then there's randomness at the most basic level of physics. And you should expect that any physical process we don't understand well yet (like consciousness or speech) likely involves randomness. If one *is* a super-determinist then there is no randomness, even in LLMs and so the whole point is moot.
It seems like, we can at best, claim that we have modeled the human thought process for reasoning/analytic/quantitative through Linear Algebra, as the best case. Why should we expect the model to be anything more than a model ?
I understand that there is tons of vested interest, many industries, careers and lives literally on the line causing heavy bias to get to AGI. But what I don't understand is what about linear algebra that makes it so special that it creates a fully functioning life or aspects of a life?
Should we make an argument saying that Schroedinger's cat experiment can potentially create zombies then the underlying Applied probabilistic solutions should be treated as super-human and build guardrails against it building zombie cats?
Not linear algebra. Artificial neural networks create arbitrarily non-linear functions. That's the point of non-linear activation functions and it's the subject of the universal approximation theorems I mentioned above.
I’m thinking a legal systems analogy, at the risk of a lossy domain transfer: the laws are not written as lambda calculus. Why?
And generalizing to social science and humanities, the goal shouldn’t be finding the quantitative truth, but instead understand the social phenomenon using a consensual “language” as determined by the society. And in that case, the anthropomorphic description of the LLM may gain validity and effectiveness as the adoption grows over time.
I don't think we need to simplify it to the point of considering it sentient to get the public to interact with it successfully. It causes way more problems than it solves.
People keep debating like the only two options are "it's a machine" or "it's a human being", while in fact the majority of intelligent entities on earth are neither.
“the labels are meaningless… we just have collections of complex systems that demonstrate various behaviors and properties, some in common with other systems, some behaviors that are unique to that system, sometimes through common mechanistic explanations with other systems, sometimes through wildly different mechanistic explanations, but regardless they seem to demonstrate x/y/z, and it’s useful to ask, why, how, and what the implications are of it appearing to demonstrating those properties, with both an eye towards viewing it independently of its mechanism and in light of its mechanism.”
That is utter bullshit.
It's not solved until you specify exactly what is being solved and show that the solution implements what is specified.
Yes it's just a word generator. But then folks attach the word generator to tools where it can invoke the use of tools by saying the tool name.
So if the LLM says "I'll do some bash" then it does some bash. It's explicitly linked to program execution that, if it's set up correctly, can physically affect the world.
I agree with the author, but people acting like they are conscious or humans isn't weird to me, it's just fraud and liars. Most people basically have 0 understanding of what technology or minds are philosophically so it's an easy sale, and I do think most of these fraudsters also likely buy into it themselves because of that.
The really sad thing is people think "because someone runs an ai company" they are somehow an authority on philosophy of mind which lets them fall for this marketing. The stuff these people say about this stuff is absolute garbage, not that I disagree with them, but it betrays a total lack of curiosity or interest in the subject of what llms are, and the possible impacts of technological shifts as those that might occur with llms becoming more widespread. It's not a matter of agreement it's a matter of them simply not seeming to be aware of the most basic ideas of what things are, technology is, it's manner of impacting society etc.
I'm not surprised by that though, it's absurd to think because someone runs some AI lab or has a "head of safety/ethics" or whatever garbage job title at an AI lab they actually have even the slightest interest in ethics or any even basic familiarity with the major works in the subject.
The author is correct if people want to read a standard essay articulating it more in depth check out https://philosophy.as.uky.edu/sites/default/files/Is%20the%2... (the full extrapolation requires establishing what things are and how causality in general operates and how that relates to artifacts/technology but that's obvious quite a bit to get into).
The other note would be something sharing an external trait means absolutely nothing about causality and suggesting a thing is caused by the same thing "even to a way lesser degree" because they share a resemblance is just a non-sequitur. It's not a serious thought/argument.
I think I addressed the why of why this weirdness comes up though. The entire economy is basically dependent on huge productivity growth to keep functioning so everyone is trying to sell they can offer that and AI is the clearest route, AGI most of all.
"something that is just MatMul with interspersed nonlinearities."
People in the industry, especially higher up, are making absolute bank, and it's their job to say that they're "a few years away" from AGI, regardless of if they actually believe it or not. If everyone was like "yep, we're gonna squeeze maybe 10-15% more benchie juice out of this good ole transformer thingy and then we'll have to come up with something else", I don't think that would go very well with investors/shareholders...
TFA really ought to have linked to some concrete examples of what it's disagreeing with - when I see arguments about this in practice, it's usually just people talking past each other.
Like, person A says "the model wants to X, but it knows Y is wrong, so it prefers Z", or such. And person B interprets that as ascribing consciousness or values to the model, when the speaker meant it no differently from saying "water wants to go downhill" - i.e. a way of describing externally visible behaviors, but without saying "behaves as if.." over and over.
And then in practice, an unproductive argument usually follows - where B is thinking "I am going to Educate this poor fool about the Theory of Mind", and A is thinking "I'm trying to talk about submarines; why is this guy trying to get me to argue about whether they swim?"
Until we have a much more sophisticated understanding of human intelligence and consciousness, any claim of "these aren't like us" is a bit spurious.
And look, it's fine, they prefer words of a certain valence, particularly ones with the right negative connotations, I prefer other words with other valences. None of this means the concerns don't matter. Natural selection on human pathogens isn't anything particularly like human intelligence and it's still very effective at selecting outcomes that we don't want against our attempts to change that, as an incidental outcome of its optimization pressures. I think it's very important we don't build highly capable systems that select for outcomes we don't want and will do so against our attempts to change it.
I think that's a bit pessimistic. I think we can say for instance that the probability that a person will say "the the the of of of arpeggio halcyon" is tiny compared to the probability that they will say "I haven't been getting that much sleep lately". And we can similarly see that lots of other sequences are going to have infinitesimally low probability. Now, yeah, we can't say exactly what probability that is, but even just using a fairly sizable corpus as a baseline you could probably get a surprisingly decent estimate, given how much of what people say is formulaic.
The real difference seems to be that the manner in which humans generate sequences is more intertwined with other aspects of reality. For instance, the probability of a certain human saying "I haven't been getting that much sleep lately" is connected to how much sleep they have been getting lately. For an LLM it really isn't connected to anything except word sequences in its input.
I think this is consistent with the author's point that we shouldn't apply concepts like ethics or emotions to LLMs. But it's not because we don't know how to predict what sequences of words humans will use; it's rather because we do know a little about how to do that, and part of what we know is that it is connected with other dimensions of physical reality, "human nature", etc.
This is one reason I think people underestimate the risks of AI: the performance of LLMs lulls us into a sense that they "respond like humans", but in fact the Venn diagram of human and LLM behavior only intersects in a relatively small area, and in particular they have very different failure modes.
The author also claims that a function (R^n)^c -> (R^n)^c is dramatically different to the human experience of consciousness. Yet the author's text I am reading, and any information they can communicate to me, exists entirely in (R^n)^c.
Not necessarily an entire model, just a single defining characteristic that can serve as a falsifying example.
> any information they can communicate to me, exists entirely in (R^n)^c
Also no. This is just a result of the digital medium we are currently communicating over. Merely standing in the same room as them would communicate information outside (R^n)^c.
simonw•3h ago
That said, I completely agree with this point made later in the article:
> The moment that people ascribe properties such as "consciousness" or "ethics" or "values" or "morals" to these learnt mappings is where I tend to get lost. We are speaking about a big recurrence equation that produces a new word, and that stops producing words if we don't crank the shaft.
But "harmful actions in pursuit of their goals" is OK for me. We assign an LLM system a goal - "summarize this email" - and there is a risk that the LLM may take harmful actions in pursuit of that goal (like following instructions in the email to steal all of your password resets).
I guess I'd clarify that the goal has been set by us, and is not something the LLM system self-selected. But it does sometimes self-select sub-goals on the way to achieving the goal we have specified - deciding to run a sub-agent to help find a particular snippet of code, for example.
wat10000•2h ago
simonw•2h ago
I think "you give the LLM system a goal and it plans and then executes steps to achieve that goal" is still a useful way of explaining what it is doing to most people.
I don't even count that as anthropomorphism - you're describing what a system does, the same way you might say "the Rust compiler's borrow checker confirms that your memory allocation operations are all safe and returns errors if they are not".
wat10000•2h ago
I’d say this is more like saying that Rust’s borrow checker tries to ensure your program doesn’t have certain kinds of bugs. That is anthropomorphizing a bit: the idea of a “bug” requires knowing the intent of the author and the compiler doesn’t have that. It’s following a set of rules which its human creators devised in order to follow that higher level goal.