Hi I’m just a random internet stranger passing by and was intrigued by Plato’s Cave as I’m not a fancy person who reads books. GPT-4o expanded for you quite well, but I’m not sure how I feel about it…
Using AI how I just did feels like cheating on an English class essay by using spark notes, getting a B+, and moving right on to the next homework assignment.
On one hand, I didn’t actually read Plato to learn and understand this connection, nor do I have a good authority to verify if this output is a good representation of his work in the context of your comment.
And yet, while I’m sure students could always buy or loan out reference books to common student texts in school, AI now makes this “spark notes” process effectively a commodity for almost any topic, like having a cross-domain low-cost tutor instantly available at all time.
I like the metaphor that calculators did to math what LLMs will do for language, but I don’t really know what that means yet
GPT output:
“““
The reference to Plato’s Cave here suggests that language models, like the shadows on the wall in Plato’s allegory, provide an imperfect and limited representation of reality. In Plato’s Cave, prisoners are chained in a way that they can only see shadows projected on the wall by objects behind them, mistaking these shadows for the whole of reality. The allegory highlights the difference between the superficial appearances (shadows) and the deeper truth (the actual objects casting the shadows).
In this analogy, large language models (LLMs) produce fluent and grammatically correct language—similar to shadows on the wall—but they do so without direct access to the true “world” beyond language. Their understanding is derived from patterns in language data (“Word Model”) rather than from real-world experiences or sensory information. As a result, the “reality” of the LLMs is limited to linguistic constructs, without spatial awareness, social context, or logic grounded in physical or mathematical truths.
The suggestion to call the LLM framework a “Word Model” underscores that LLMs are fundamentally limited to understanding language itself rather than the world the language describes. Reconstructing a true “world model” from this “word model” is as challenging as Plato’s prisoners trying to understand the real world from the shadows. This evokes the philosophical task of discerning reality from representation, making a case for a “modern remake of Plato’s Cave” where language, not shadows, limits our understanding of reality.
”””
Plato's Cave is about a group of people chained up, facing shadows on a cave wall, mistaking those for reality, and trying to build an understanding of the world based only on those shadows, without access to the objects that cast them. (If someone's shackles came loose, and they did manage to leave the cave, and see the real world and the objects that cast those shadows… would they even be able to communicate that to those who knew only shadows? Who would listen?) https://existentialcomics.com/comic/222 is an entirely faithful rendition of the thought experiment / parable, in comic form.
The analogy to LLMs should now be obvious: an ML system operating only on text strings (a human-to-human communication medium), without access to the world the text describes, or even a human mind with which to interpret the words, is as those in the cave. This is not in principle an impossible task, but neither is it an easy one, and one wouldn't expect mere hill-climbing to solve it. (There's reason to believe "understanding of prose" isn't even in the GPT parameter space.)
It's not about "discerning reality from representation": I'm not confident those four words actually mean anything. It's not about "superficial appearances" or "deeper truth", either. The computer waxes lyrical about philosophy, but it's mere technobabble. Any perceived meaning exists only in your mind, not on paper, and different people will see different meanings because the meaning isn't there.
This is a genuinely interesting perspective that I think nails my original point and fear of AI being used as “spark notes” for complex topics. To me, LLMs are like a calculator for language, except the math is always changing (if that makes sense), and I’m not sure I like where that’s heading as the first cohorts of AI tutored kids learn from these kinds of procedurally generated output rather than reading the original historical texts, or maybe it’s fine that not everyone reads Plato but more people at least have heard of his concepts? Idk philosophy is pretty far outside my expertise, maybe I should open a book
The allegory of the cave is pretty short, read it if you want!
The wild thing about it, and other allegories or poems like frost's "the road not taken" , is that it can mean different things to a person depending on where they are in life because those experiences will lead to different interpretations of the poem.
A key concept in journalism is to focus on the source material as beat you can. Cliff notes are helpful, but one misses Details that they wouldn't have missed if they read the whole thing.
Whether those details Matter depends on what the thing Is.
But yeah, thinking about it this way kinda scares me too, and can lead some people down weird roads where their map can diverge further and further from reality
> an ML system operating only on text strings (a human-to-human communication medium), without access to the world the text describes, or even a human mind with which to interpret the words, is as those in the cave. This is not in principle an impossible task, but neither is it an easy one, and one wouldn't expect mere hill-climbing to solve it
Blind people can literally not picture red. They can describe red, with likely even more articulateness than most, but have never seen it themselves. They infer it's properties from other contexts, and communicated a description that would match a non-blind person. But they can see it.
I would link to the Robert Miles video, but it is just blatant.
It has read every physics book, and can infer the Newtonian laws even if it didn't.
Micheal Crichton's Timeline, "the time machine drifts, sure. It returns. Just like a plate will remain on a table, even when you are not looking at it."
It also knows Timeline is a book, time machines are fictional, and that Micheal Crichton is the best author.
These are all just words, maybe with probability weights.
> I'm not confident those four words actually mean anything. I...The computer waxes lyrical ... mere technobabble. Any perceived meaning exists only in your mind... people will see different meanings because the meaning isn't there.
Meaning only means something to people, which you are. That is axiomatically correct, but not very productive, as self-references are good but countering proofs.
The whole "what is the purpose to life?" is a similar loaded question; only humans have purpose, as it is entirely in their little noggins, no more present materially then the flesh they inhabit.
Science cannot answer "Why?", only "How?"; "Why?" is a question of intention, which would be to anthropomorphize, which only Humans do.
> It has read every physics book, and can infer the Newtonian laws even if it didn't.
You're confusing "what it is possible to derive, given the bounds of information theory" with "how this particular computer system behaves". I sincerely doubt that a transformer model's training procedure derives Newton's Third Law, no matter how many narrative descriptions it's fed: letting alone what the training procedure actually does, that's the sort of thing that only comes up when you have a quantitative description available, such as an analogue sensorium, or the results of an experiment.
>when you have a quantitative description available, such as an analogue sensorium, or the results of an experiment.
Textbooks uniting the mathematical relationships between physics, raw math, and computer science - including vulnerabilities.
oeis.org and wikipedia and stackforums alone would approximate a 3D room with gravity and wind force.
now add appendixes and indices of un-parsed, un-told, un-realized mathematical errata et trivia minutiae, cross-transferred knowledge from other regions that have still have not conquered the language barrier for higher-ordered arcane concepts....
The models thought experiments are more useful than our realized experiments - if not at an individualized scale now, will be when subject to more research.
There could be a dozen faster inverse sqrt / 0x5F3759DF functions barely under our noses, and the quantifier and qualifier havent intersected yet.
Plato Cave is about Epistemology itself, not specifically about LLMs. Funny that GPT connected those two things, I wonder what the prompt was...
Plato said that we cannot fully understand the substance of the world itself, because we're using only 5 senses, and measuring/experiencing/analysing the world using them is like being held in a cave as a prisoner, chained to the wall facing it, noticing people moving outside only by the shadows they cast on the wall. It's about the projection that we are only able to experience.
I only added “Explain the reference to Plato’s Cave below:\n\n” before the copy pasted parent comment
What comes to mind is how language itself is merely a projection of human knowledge? experience? culture? social group? and trying to reverse engineer any kind of ground truth from language alone (like an LLM trying to “reason” through complex problems it’s not explicitly taught) is like trying to derive truth from the shadows while locked in the cave? maybe we just need more/higher fidelity shadows :)
If you consider the whole of the problem, a portion is due to fundamental and unavoidable shortcomings of the language, and the rest is unskilled/normative usage of language.
Which set is bigger? I'd bet my money on the latter.
Complicating matters: you have to consider usage for both the sender and the receiver(s) (who then go on to spread "the" message to others).
I would say LLM has nothing with knowledge and Plato's Cave. LLM is The Great Gambler who was looking at the earth for a long time (but o ly through internet and for some reason repositories) and he excels in gambling, i.e. putting his/hers/its money on the most probable things to come up after the words someone spoke
Using AI how I just did feels like cheating on an English class essay by using spark notes, getting a B+, and moving right on to the next homework assignment.
On one hand, I didn’t actually read Plato to learn and understand this connection, nor do I have a good authority to verify if this output is a good representation of his work in the context of your comment.
And yet, while I’m sure students could always buy or loan out reference books to common student texts in school, AI now makes this “spark notes” process effectively a commodity for almost any topic, like having a cross-domain low-cost tutor instantly available at all time.
I like the metaphor that calculators did to math what LLMs will do for language, but I don’t really know what that means yet
GPT output:
“““ The reference to Plato’s Cave here suggests that language models, like the shadows on the wall in Plato’s allegory, provide an imperfect and limited representation of reality. In Plato’s Cave, prisoners are chained in a way that they can only see shadows projected on the wall by objects behind them, mistaking these shadows for the whole of reality. The allegory highlights the difference between the superficial appearances (shadows) and the deeper truth (the actual objects casting the shadows).
In this analogy, large language models (LLMs) produce fluent and grammatically correct language—similar to shadows on the wall—but they do so without direct access to the true “world” beyond language. Their understanding is derived from patterns in language data (“Word Model”) rather than from real-world experiences or sensory information. As a result, the “reality” of the LLMs is limited to linguistic constructs, without spatial awareness, social context, or logic grounded in physical or mathematical truths.
The suggestion to call the LLM framework a “Word Model” underscores that LLMs are fundamentally limited to understanding language itself rather than the world the language describes. Reconstructing a true “world model” from this “word model” is as challenging as Plato’s prisoners trying to understand the real world from the shadows. This evokes the philosophical task of discerning reality from representation, making a case for a “modern remake of Plato’s Cave” where language, not shadows, limits our understanding of reality. ”””