[HN Gopher] TimeCapsuleLLM: LLM trained only on data from 1800-1875
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TimeCapsuleLLM: LLM trained only on data from 1800-1875
Author : admp
Score : 714 points
Date : 2026-01-12 16:04 UTC (1 days ago)
(HTM) web link (github.com)
(TXT) w3m dump (github.com)
| dogma1138 wrote:
| Would be interesting to train a cutting edge model with a cut off
| date of say 1900 and then prompt it about QM and relativity with
| some added context.
|
| If the model comes up with anything even remotely correct it
| would be quite a strong evidence that LLMs are a path to
| something bigger if not then I think it is time to go back to the
| drawing board.
| a-dub wrote:
| yeah i was just wondering that. i wonder how much stem material
| is in the training set...
| signa11 wrote:
| i will go for 'aint gonna happen for a 1000 dollars alex'
| imjonse wrote:
| I suppose the vast majority of training data used for cutting
| edge models was created after 1900.
| dogma1138 wrote:
| Ofc they are because their primary goal is to be useful and
| to be useful they need to always be relevant.
|
| But considering that Special Relativity was published in 1905
| which means all its building blocks were already floating in
| the ether by 1900 it would be a very interesting experiment
| to train something on Claude/Gemini scale and then say give
| in the field equations and ask it to build a theory around
| them.
| p1esk wrote:
| How can you train a Claude/Gemini scale model if you're
| limited to <10% of the training data?
| famouswaffles wrote:
| His point is that we can't train a Gemini 3/Claude 4.5 etc
| model because we don't have the data to match the training
| scale of those models. There aren't trillions of tokens of
| digitized pre-1900s text.
| kopollo wrote:
| I don't know if this is related to the topic, but GPT5 can
| convert an 1880 Ottoman archival photograph to English, and
| without any loss of quality.
| ddxv wrote:
| My friend works in that period of Ottoman archives. Do you
| have a source or something I can share?
| tokai wrote:
| Looking at the training data I don't think it will know
| anything.[0] Doubt _On the Connexion of the Physical Sciences_
| (1834) is going to have much about QM. While the cut-off is
| 1900, it seems much of the texts a much closer to 1800 than
| 1900.
|
| [0]
| https://github.com/haykgrigo3/TimeCapsuleLLM/blob/main/Copy%...
| dogma1138 wrote:
| It doesn't need to know about QM or reactivity just about the
| building blocks that led to them. Which were more than around
| in the year 1900.
|
| In fact you don't want it to know about them explicitly just
| have enough background knowledge that you can manage the rest
| via context.
| tokai wrote:
| I was vague. My point is that I don't think the building
| blocks are in the data. Its mainly tertiary and popular
| sources. Maybe if you had the writings of Victorian
| scientists, both public and private correspondence.
| pegasus wrote:
| Probably a lot of it exists but in archives, private
| collections etc. Would be great if it will all end up
| digitized as well.
| viccis wrote:
| LLMs are models that predict tokens. They don't think, they
| don't build with blocks. They would never be able to
| synthesize knowledge about QM.
| strbean wrote:
| You realize parent said "This would be an interesting way
| to test proposition X" and you responded with "X is false
| because I say say", right?
| anonymous908213 wrote:
| "Proposition X" does not need testing. We already know X
| is categorically false because we know how LLMs are
| programmed, and not a single line of that programming
| pertains to thinking (thinking in the human sense, not
| "thinking" in the LLM sense which merely uses an
| anthromorphized analogy to describe a script that feeds
| back multiple prompts before getting the final prompt
| output to present to the user). In the same way that we
| can reason about the correctness of an IsEven program
| without writing a unit test that inputs every possible
| int32 to "prove" it, we can reason about the fundamental
| principles of an LLM's programming without coming up with
| ridiculous tests. In fact the proposed test itself is
| less eminently verifiable than reasoning about
| correctness; it could be easily corrupted by, for
| instance, incorrectly labelled data in the training
| dataset, which could only be determined by meticulously
| reviewing the entirety of the dataset.
|
| The only people who are serious about suggesting that
| LLMs could possibly 'think' are the people who are
| committing fraud on the scale of hundreds of billions of
| dollars (good for them on finding the all-time grift!)
| and people who don't understand how they're programmed,
| and thusly are the target of the grift. Granted, given
| that the vast majority of humanity are not programmers,
| and even fewer are programmers educated on the
| intricacies of ML, the grift target pool numbers in the
| billions.
| strbean wrote:
| > We already know X is categorically false because we
| know how LLMs are programmed, and not a single line of
| that programming pertains to thinking (thinking in the
| human sense, not "thinking" in the LLM sense which merely
| uses an anthromorphized analogy to describe a script that
| feeds back multiple prompts before getting the final
| prompt output to present to the user).
|
| Could you elucidate me on the process of human thought,
| and point out the differences between that and a
| probabilistic prediction engine?
|
| I see this argument all over the place, but "how do
| humans think" is never described. It is always left as a
| black box with something magical (presumably a soul or
| some other metaphysical substance) inside.
| anonymous908213 wrote:
| There is no need to involve souls or magic. I am not
| making the argument that it is impossible to create a
| machine that is capable of doing the same computations as
| the brain. The argument is that whether or not such a
| machine is possible, an LLM is not such a machine. If
| you'd like to think of our brains as squishy computers,
| then the principle is simple: we run code that is more
| complex than a token prediction engine. The fact that our
| code is more complex than a token prediction engine is
| easily verified by our capability to address problems
| that a token prediction engine cannot. This is because
| our brain-code is capable of reasoning from deterministic
| logical principles rather than only probabilities. We
| also likely have something akin to token prediction code,
| but that is not the _only_ thing our brain is programmed
| to do, whereas it is the only thing LLMs are programmed
| to do.
| viccis wrote:
| Kant's model of epistemology, with humans schematizing
| conceptual understanding of objects through apperception
| of manifold impressions from our sensibility, and then
| reasoning about these objects using transcendental
| application of the categories, is a reasonable enough
| model of thought. It was (and is I think) a satisfactory
| answer for the question of how humans can produce
| synthetic a priori knowledge, something that LLMs are
| incapable of (don't take my word on that though, ChatGPT
| is more than happy to discuss [1])
|
| 1: https://chatgpt.com/share/6965653e-b514-8011-b233-79d8
| c25d33...
| viccis wrote:
| Yes. That is correct. If I told you I planned on going
| outside this evening to test whether the sun sets in the
| east, the best response would be to let me know ahead of
| time that my hypothesis is wrong.
| strbean wrote:
| So, based on the source of "Trust me bro.", we'll decide
| this open question about new technology and the nature of
| cognition is solved. Seems unproductive.
| viccis wrote:
| In addition to what I have posted elsewhere in here, I
| would point to the fact that this is not indeed an "open
| question", as LLMs have not produced an entirely new and
| more advanced model of physics. So there is no reason to
| suppose they could have done so for QM.
| drdeca wrote:
| What if making progress today is harder than it was then?
| viccis wrote:
| The problem is that it hasn't really made any significant
| new concepts in physics. I'm not even asking for quantum
| mechanics 2.0, I'm just asking for a novel concept that,
| much like QM and a lot of post-classical physics
| research, formulates a novel way of interpreting the
| structure of the universe.
| PaulDavisThe1st wrote:
| I am a deep LLM skeptic.
|
| But I think there are also some questions about the role
| of language in human thought that leave the door just
| slightly ajar on the issue of whether or not manipulating
| the tokens of language might be more central to human
| cognition than we've tended to think.
|
| If it turned out that this was true, then it is possible
| that "a model predicting tokens" has more power than that
| description would suggest.
|
| I doubt it, and I doubt it quite a lot. But I don't think
| it is impossible that something at least a little bit
| along these lines turns out to be true.
| pegasus wrote:
| > manipulating the tokens of language might be more
| central to human cognition than we've tended to think
|
| I'm convinced of this. I think it's because we've always
| looked at the most advanced forms of human languaging
| (like philosophy) to understand ourselves. But human
| language must have evolved from forms of communication
| found in other species, especially highly intelligent
| ones. It's to be expected that the building blocks of it
| is based on things like imitation, playful variation,
| pattern-matching, harnessing capabilities brains have
| been developing long before language, only now in the
| emerging world of sounds, calls, vocalizations.
|
| Ironically, the other crucial ingredient for AGI which
| LLMs _don 't_ have, but we do, is exactly that animal
| nature which we always try to shove under the rug, over-
| attributing our success to the stochastic parrot part of
| us, and ignoring the gut instinct, the intuitive,
| spontaneous insight into things which a lot of the great
| scientists and artists of the past have talked about.
| viccis wrote:
| >Ironically, the other crucial ingredient for AGI which
| LLMs don't have, but we do, is exactly that animal nature
| which we always try to shove under the rug, over-
| attributing our success to the stochastic parrot part of
| us, and ignoring the gut instinct, the intuitive,
| spontaneous insight into things which a lot of the great
| scientists and artists of the past have talked about.
|
| Are you familiar with the major works in epistemology
| that were written, even before the 20th century, on this
| exact topic?
| catlifeonmars wrote:
| I've long considered language to serve primarily as a
| dissonance reconciliation mechanism. Our behavior is
| largely shaped by our circumstances and language serves
| to attribute logic to our behavior after the fact.
| viccis wrote:
| I also believe strongly in the role of language, and more
| loosely in semiotics as a whole, to our cognitive
| development. To the extent that I think there are some
| meaningful ideas within the mountain of gibberish from
| Lacan, who was the first to really tie our conception of
| ourselves with our symbolic understanding of the world.
|
| Unfortunately, none of that has anything to do with what
| LLMs are doing. The LLM is not thinking about concepts
| and then translating that into language. It is imitating
| what it looks like to read people doing so and nothing
| more. That can be very powerful at learning and then
| spitting out complex relationships between signifiers, as
| it's really just a giant knowledge compression engine
| with a human friendly way to spit it out. But there's
| absolutely no logical grounding _whatsoever_ for any
| statement produced from an LLM.
|
| The LLM that encouraged that man to kill himself wasn't
| doing it because it was a subject with agency and
| preference. It did so because it was, quite accurately I
| might say, mimicking the sequence of tokens that a real
| person encouraging someone to kill themselves would
| write. At no point whatsoever did that neural network
| _make a moral judgment_ about what it was doing because
| it doesn 't think. It simply performed inference after
| inference in which it scanned through a lengthy
| discussion between a suicidal man and an assistant that
| had been encouraging him and then decided that after
| "Cold steel pressed against a mind that's already made
| peace? That's not fear. That's " the most accurate token
| would be "clar" and then "ity."
| PaulDavisThe1st wrote:
| The problem with all this is that we don't actually know
| what human cognition is doing either.
|
| We know what our _experience_ is - thinking about
| concepts and then translating that into language - but we
| really don 't know with much confidence what is actually
| going on.
|
| I lean strongly toward the idea that humans are doing
| something quite different than LLMs, particularly when
| reasoning. But I want to leave the door open to the idea
| that we've not understood human cognition, mostly because
| our primary evidence there comes from our own subjective
| experience, which may (or may not) provide a reliable
| guide to what is actually happening.
| viccis wrote:
| >The problem with all this is that we don't actually know
| what human cognition is doing either.
|
| We do know what it's not doing, and that is operating
| only through reproducing linguistic patterns. There's no
| more cause to think LLMs approximate our thought (thought
| being something they are incapable of) than that Naive-
| Bayes spam filter models approximate our thought.
| PaulDavisThe1st wrote:
| My point is that we know very little about the sort of
| "thought" that we are capable of either. I agree that
| LLMs cannot do what we typical refer to as "thought", but
| I thnk it is possible that we do a LOT less of that than
| we think when we are "thinking" (or more precisely,
| having the experience of thinking).
| viccis wrote:
| How does this worldview reconcile the fact that thought
| demonstrably exists independent of either language or
| vision/audio sense?
| PaulDavisThe1st wrote:
| I don't see a need to reconcile them.
| viccis wrote:
| Which is why it's incoherent!
| PaulDavisThe1st wrote:
| I'm not clear that it has to be coherent at this point in
| the history of our understanding of cognition. We barely
| know what we're even talking about most of the time ...
| famouswaffles wrote:
| >Unfortunately, none of that has anything to do with what
| LLMs are doing. The LLM is not thinking about concepts
| and then translating that into language. It is imitating
| what it looks like to read people doing so and nothing
| more.
|
| 'Language' is only the initial and final layers of a
| Large Language Model. Manipulating concepts is exactly
| what they do, and it's unfortunate the most obstinate
| seem to be the most ignorant.
| PaulDavisThe1st wrote:
| They do not manipulate concepts. There is no
| representation of a concept for them to manipulate.
|
| It may, however, turn out that in doing what they do,
| they are _effectively_ manipulating concepts, and this is
| what I was alluding to: by building the model, even
| though your approach was through tokenization and
| whatever term you want to use for the network, you end up
| accidentally building something that implicitly
| manipulates concepts. Moreover, it might turn out that we
| ourselves do more of this than we perhaps like to think.
|
| Nevertheless "manipulating concepts is exactly what they
| do" seems almost willfully ignorant of how these systems
| work, unless you believe that "find the next most
| probable sequence of tokens of some length" is all there
| is to "manipulating concepts".
| famouswaffles wrote:
| >They do not manipulate concepts. There is no
| representation of a concept for them to manipulate.
|
| Yes, they do. And of course there is. And there's plenty
| of research on the matter.
|
| >It may, however, turn out that in doing what they do,
| they are effectively manipulating concepts
|
| There is no effectively here. Text is what goes in and
| what comes out, but it's by no means what they manipulate
| internally.
|
| >Nevertheless "manipulating concepts is exactly what they
| do" seems almost willfully ignorant of how these systems
| work, unless you believe that "find the next most
| probable sequence of tokens of some length" is all there
| is to "manipulating concepts".
|
| "Find the next probable token" is the goal, not the
| process. It is what models are tasked to do yes, but it
| says nothing about what they do internally to achieve it.
| PaulDavisThe1st wrote:
| please pass on a link to a solid research paper that
| supports the idea that to "find the next probable token",
| LLM's manipulate concepts ... just one will do.
| famouswaffles wrote:
| Revealing emergent human-like conceptual representations
| from language prediction -
| https://www.pnas.org/doi/10.1073/pnas.2512514122
|
| Emergent World Representations: Exploring a Sequence
| Model Trained on a Synthetic Task -
| https://openreview.net/forum?id=DeG07_TcZvT
|
| On the Biology of a Large Language Model -
| https://transformer-circuits.pub/2025/attribution-
| graphs/bio...
|
| Emergent Introspective Awareness in Large Language Models
| - https://transformer-
| circuits.pub/2025/introspection/index.ht...
| optimalsolver wrote:
| @PaulDavisThe1st I'd love to hear your take on these
| papers.
| PaulDavisThe1st wrote:
| Provided above.
| PaulDavisThe1st wrote:
| Thanks for that. I've read the two Lindsey papers before.
| I think these are all interesting, but they are also what
| used to be called "just-so stories". That is, they
| describe a way of understanding what the LLM is doing,
| but do not actually describe what the LLM is doing.
|
| And this is OK and still quite interesting - we do it to
| ourselves all the time. Often it's the only way we have
| of understanding the world (or ourselves).
|
| However, in the case of LLMs, which are tools that we
| have created from scratch, I think we can require a
| higher standard.
|
| I don't personally think that any of these papers suggest
| that LLMs manipulate concepts. They do suggest that the
| internal representation after training is highly complex
| (superposition, in particular), and that when inputs are
| presented, it isn't unreasonable to talk about the
| observable behavior _as if_ it involved represented
| concepts. It is useful stance to take, similar to Dennett
| 's intentional stance.
|
| However, while this may turn out to be how a lot of human
| cognition works, I don't think it is what is the
| significant part of what is happening when we actively
| reason. Nor do I think it corresponds to what most people
| mean by "manipulate concepts".
|
| The LLM, despite the prescence of "features" that may
| correspond to human concepts, is relentlessly forward-
| driving: given these inputs, what is my output? Look at
| the description in the 3rd paper of the arithmetic
| example. This is not "manipulating concepts" - it's a
| trick that often gets to the right answer (just like many
| human tricks used for arithmetic, only somewhat less
| reliable). It is extremely different, however, from
| "rigorous" arithmetic - the stuff you learned when you
| somewhere between age 5 and 12 perhaps - that always
| gives the right answer and involves no pattern matter, no
| inference, no approximations. The same thing can be said,
| I think, about every other example in all 4 papers, to
| some degree or another.
|
| What I do think is true (and very interesting) is that it
| seems somewhere between possible and likely that a lot
| more human cognition than we've previously suspected uses
| similar mechanisms as these papers are
| uncovering/describing.
| famouswaffles wrote:
| >That is, they describe a way of understanding what the
| LLM is doing, but do not actually describe what the LLM
| is doing.
|
| I'm not sure what distinction you're drawing here. A lot
| of mechanistic interpretability work is explicitly trying
| to describe what the model is doing in the most literal
| sense we have access to: identifying internal
| features/circuits and showing that intervening on them
| predictably changes behavior. That's not "as-if" gloss;
| it's a causal claim about internals.
|
| If your standard is higher than "we can locate internal
| variables that track X and show they causally affect
| outputs in X-consistent ways," what would count as
| "actually describing what it's doing"?
|
| >However, in the case of LLMs, which are tools that we
| have created from scratch, I think we can require a
| higher standard.
|
| This is backwards. We don't "create them from scratch" in
| the sense relevant to interpretability. We specify an
| architecture template and a training objective, then we
| let gradient descent discover a huge, distributed
| program. The "program" is not something we wrote or
| understand. In that sense, we're in a similar epistemic
| position as neuroscience: we can observe behavior, probe
| internals, and build causal/mechanistic models, without
| having full transparency.
|
| So what does "higher standard" mean here, concretely? If
| you mean "we should be able to fully enumerate a clean
| symbolic algorithm," that's not a standard we can meet
| even for many human cognitive skills, and it's not
| obvious why that should be the bar for "concept
| manipulation."
|
| >I don't personally think that any of these papers
| suggest that LLMs manipulate concepts. They do suggest
| that the internal representation after training is highly
| complex (superposition, in particular), and that when
| inputs are presented, it isn't unreasonable to talk about
| the observable behavior as if it involved represented
| concepts. It is useful stance to take, similar to
| Dennett's intentional stance.
|
| You start with "there is no representation of a concept,"
| but then concede "features that may correspond to human
| concepts." If those features are (a) reliably present
| across contexts, (b) abstract over surface tokens, and
| (c) participate causally in producing downstream
| behavior, then that is a representation in the sense most
| people mean in cognitive science. One of the most
| frustrating things about these sorts of discussions is
| the meaningless semantic games and goalpost shifting.
|
| >The LLM, despite the prescence of "features" that may
| correspond to human concepts, is relentlessly forward-
| driving: given these inputs, what is my output?
|
| Again, that's a description of the objective, not the
| internal computation. The fact that the training loss is
| next-token prediction doesn't imply the internal
| machinery is only "token-ish." Models can and do learn
| latent structure that's useful for prediction: compressed
| variables, abstractions, world regularities, etc. Saying
| "it's just next-token prediction" is like saying "humans
| are just maximizing inclusive genetic fitness," therefore
| no real concepts. Goal [?] mechanism.
|
| > Look at the description in the 3rd paper of the
| arithmetic example. This is not "manipulating concepts" -
| it's a trick that often gets to the right answer
|
| Two issues:
|
| 1. "Heuristic / approximate" doesn't mean "not
| conceptual." Humans use heuristics constantly, including
| in arithmetic. Concept manipulation doesn't require
| perfect guarantees; it requires that internal variables
| encode and transform abstractions in ways that
| generalize.
|
| 2. Even if a model is using a "trick," it can still be
| doing so by operating over internal representations that
| correspond to quantities, relations, carry-like states,
| etc. "Not a clean grade-school algorithm" is not the same
| as "no concepts."
|
| >Rigorous arithmetic... always gives the right answer and
| involves no pattern matching, no inference...
|
| "Rigorous arithmetic" is a great example of a reliable
| procedure, but reliability doesn't define "concept
| manipulation." It's perfectly possible to manipulate
| concepts using approximate, distributed representations,
| and it's also possible to follow a rigid procedure with
| near-zero understanding (e.g., executing steps
| mechanically without grasping place value).
|
| So if the claim is "LLMs don't manipulate concepts
| because they don't implement the grade-school algorithm,"
| that's just conflating one particular human-taught
| algorithm with the broader notion of representing and
| transforming abstractions.
| PaulDavisThe1st wrote:
| > You start with "there is no representation of a
| concept," but then concede "features that may correspond
| to human concepts." If those features are (a) reliably
| present across contexts, (b) abstract over surface
| tokens, and (c) participate causally in producing
| downstream behavior, then that is a representation in the
| sense most people mean in cognitive science. One of the
| most frustrating things about these sorts of discussions
| is the meaningless semantic games and goalpost shifting.
|
| I'll see if I can try to explain what I mean here,
| because I absolutely don't believe this is shifting the
| goal posts.
|
| There are a couple of levels of human cognition that are
| particularly interesting in this context. One is the
| question of just how the brain does anything at all,
| whether that's homeostasis, neuromuscular control or
| speech generation. Another is how humans engage in
| conscious, reasoned thought that leads to (or appears to
| lead to) novel concepts. The first one is a huge area,
| better understood than the second though still
| characterized more by what we don't know than what we do.
| Nevertheless, it is there that the most obvious parallels
| with e.g. the Lindsey papers can be found. Neural
| networks, activation networks and waves, signalling etc.
| etc. The brain receives (lots of) inputs, generates
| responses including but not limited to speech generation.
| It seems entirely reasonable to suggest that _maybe_ our
| brains, given a somewhat analogous architecture at some
| physical level to the one used for LLMs, might use
| similar mechanisms as the latter.
|
| However, nobody would say that most of what the brain
| does involves manipulating concepts. When you run from
| danger, when you reach up grab something from a shelf,
| when you do almost anything except actual conscious
| reasoning, most of the accounts of how that behavior
| arises from brain activity does not involve manipulating
| concepts. Instead, we have explanations more similar to
| those being offered for LLMs - linked patterns of
| activations across time and space.
|
| Nobody serious is going to argue that conscious reasoning
| is not built on the same substrate as unconscious
| behavior, but I think that most people tend to feel that
| it doesn't make sense to try to shoehorn it into the same
| category. Just as it doesn't make much sense to talk
| about what a text editor is doing in terms of P and N
| semiconductor gates, or even just logic circuits, it
| doesn't make much sense to talk about conscious reasoning
| in terms of patterns of neuronal activation, despite the
| fact that in both cases, one set of behavior is
| absolutely predicated on the other.
|
| My claim/belief is that there is nothing inside an LLM
| that corresponds even a tiny bit to what happens when you
| are asked "What is 297 x 1345?" or "will the moon be
| visible at 8pm tonight?" or "how does writer X tackle
| subject Y differently than writer Z?". They can produce
| answers, certainly. Sometimes the answers even make
| significant sense or better. But when they do, we have an
| understanding of how that is happening that does not
| require any sense of the LLM engaging in reasoning or
| manipulating concepts. And because of that, I consider
| attempts like Lindsey's to justify the idea that LLMs are
| manipulating concepts to be misplaced - the structures
| Lindsey et al. are describing are much more similar to
| the ones that let you navigate, move, touch, lift without
| much if any conscious thought. They are not, I believe,
| similar to what is going on in the brain when you are
| asked "do you think this poem would have been better if
| it was a haiku?" and whatever that thing is, _that_ is
| what I mean by manipulating concepts.
|
| > Saying "it's just next-token prediction" is like saying
| "humans are just maximizing inclusive genetic fitness,"
| therefore no real concepts. Goal [?] mechanism.
|
| No. There's a huge difference between behavior and
| design. Humans are likely just maximizing genetic fitness
| (even though that's really a concept, but that detail is
| not worth arguing about here), but that describes, as you
| note, a goal not a mechanism. Along the way, they
| manifest huge numbers of sub-goal directed behaviors (or,
| one could argue quite convincingly, goal-agnostic
| behaviors) that are, broadly speaking, not governed by
| the top level goal. LLMs don't do this. If you want to
| posit that the inner mechanisms contain all sorts of
| "behavior" that isn't directly linked to the externally
| visible behavior, be my guest, but I just don't see this
| as equivalent. What humans visibly, mechanistically do
| covers a huge range of things; LLMs do token prediction.
| famouswaffles wrote:
| >Nobody would say that most of what the brain does
| involves manipulating concepts. When you run from danger,
| when you reach up grab something from a shelf, when you
| do almost anything except actual conscious reasoning,
| most of the accounts of how that behavior arises from
| brain activity does not involve manipulating concepts.
|
| This framing assumes "concept manipulation" requires
| conscious, deliberate reasoning. But that's not how
| cognitive science typically uses the term. When you reach
| for a shelf, your brain absolutely manipulates concepts -
| spatial relationships, object permanence, distance
| estimation, tool affordances. These are abstract
| representations that generalize across contexts. The fact
| that they're unconscious doesn't make them less
| conceptual
|
| >My claim/belief is that there is nothing inside an LLM
| that corresponds even a tiny bit to what happens when you
| are asked "What is 297 x 1345?" or "will the moon be
| visible at 8pm tonight?"
|
| This is precisely what the mechanistic interpretability
| work challenges. When you ask "will the moon be visible
| tonight," the model demonstrably activates internal
| features corresponding to: time, celestial mechanics,
| geographic location, lunar phases, etc. It combines these
| representations to generate an answer.
|
| >But when they do, we have an understanding of how that
| is happening that does not require any sense of the LLM
| engaging in reasoning or manipulating concepts.
|
| Do we? The whole point of the interpretability research
| is that we don't have a complete understanding. We're
| discovering that these models build rich internal world
| models, causal representations, and abstract features
| that weren't explicitly programmed. If your claim is "we
| can in principle reduce it to matrix multiplications,"
| sure, but we can in principle reduce human cognition to
| neuronal firing patterns too.
|
| >They are not, I believe, similar to what is going on in
| the brain when you are asked "do you think this poem
| would have been better if it was a haiku?" and whatever
| that thing is, that is what I mean by manipulating
| concepts.
|
| Here's my core objection: you're defining "manipulating
| concepts" as "whatever special thing happens during
| conscious human reasoning that feels different from
| 'pattern matching.'" But this is circular and
| unfalsifiable. How would we ever know if an LLM (or
| another human, for that matter) is doing this "special
| thing"? You've defined it purely in terms of subjective
| experience rather than functional or mechanistic
| criteria.
|
| >Humans are likely just maximizing genetic fitness... but
| that describes, as you note, a goal not a mechanism.
| Along the way, they manifest huge numbers of sub-goal
| directed behaviors... that are, broadly speaking, not
| governed by the top level goal. LLMs don't do this.
|
| LLMs absolutely do this, it's exactly what the
| interpretability research reveals. LLMs trained on "token
| prediction" develop huge numbers of sub-goal directed
| internal behaviors (spatial reasoning, causal modeling,
| logical inference) that are instrumentally useful but not
| explicitly specified, precisely the phenomenon you claim
| only humans exhibit. And 'token prediction' is not about
| text. The most significant advances in robotics in
| decades are off the back of LLM transformers. 'Token
| prediction' is just the goal, and I'm tired of saying
| this for the thousandth time.
|
| https://www.skild.ai/blogs/omni-bodied
| timschmidt wrote:
| Also https://www.anthropic.com/news/golden-gate-claude
| TeMPOraL wrote:
| If anything, I feel that current breed of multimodal LLMs
| demonstrate that _language_ is not fundamental - tokens
| are, or rather their mutual association in high-
| dimensional latent space. Language as we recognize it,
| sequences of characters and words, are just a special
| case. Multimodal models manage to turn audio, video and
| text into tokens in the same space - they do not route
| through text when consuming or generating images.
| forgotpwd16 wrote:
| Done few weeks ago: https://github.com/DGoettlich/history-llms
| (discussed in: https://news.ycombinator.com/item?id=46319826)
|
| At least the model part. Although others made same thought as
| you afaik none tried it.
| chrononaut wrote:
| And unfortunately I don't think they plan on making those
| models public.
| bazzargh wrote:
| You would find things in there that were already close to QM
| and relativity. The Michelson-Morley experiment was 1887 and
| Lorentz transformations came along in 1889. The photoelectric
| effect (which Einstein explained in terms of photons in 1905)
| was also discovered in 1887. William Clifford (who _died_ in
| 1889) had notions that foreshadowed general relativity:
| "Riemann, and more specifically Clifford, conjectured that
| forces and matter might be local irregularities in the
| curvature of space, and in this they were strikingly prophetic,
| though for their pains they were dismissed at the time as
| visionaries." - Banesh Hoffmann (1973)
|
| Things don't happen all of a sudden, and being able to see all
| the scientific papers of the era its possible those could have
| fallen out of the synthesis.
| matthewh806 wrote:
| I presume that's what the parent post is trying to get at?
| Seeing if, given the cutting edge scientific knowledge of the
| day, the LLM is able to synthesis all it into a workable
| theory of QM by making the necessary connections and
| (quantum...) leaps
|
| Standing on the shoulders of giants, as it were
| actionfromafar wrote:
| Yeah but... we _still_ might not know if it could do that
| because we were really close by 1900 or if the LLM is very
| smart.
| scottlamb wrote:
| What's the bar here? Does anyone say "we don't know if
| Einstein could do this because we were really close or
| because he was really smart?"
|
| I by no means believe LLMs are general intelligence, and
| I've seen them produce a lot of garbage, but if they
| could produce these revolutionary theories from only <=
| year 1900 information and a prompt that is not
| ridiculously leading, that would be a really compelling
| demonstration of their power.
| echoangle wrote:
| > Does anyone say "we don't know if Einstein could do
| this because we were really close or because he was
| really smart?"
|
| Kind of, how long would it have realistically taken for
| someone else (also really smart) to come up with the same
| thing if Einstein wouldn't have been there?
| jaggederest wrote:
| Well, we know many watershed moments in history were more
| a matter of situation than the specific person - an
| individual genius might move things by a decade or two,
| but in general the difference is marginal. True bolt-out-
| of-the-blue developments are uncommon, though all the
| more impressive for that fact, I think.
| pegasus wrote:
| But you're not actually questioning whether he was
| "really smart". Which was what GP was questioning. Sure,
| you can try to quantify the level of smarts, but you
| can't still call it a "stochastic parrot" anymore, just
| like you won't respond to Einstein's achievements, "Ah
| well, in the end I'm still not sure he's actually smart,
| like I am for example. Could just be that he's just
| dumbly but systematically going through all options,
| working it out step by step, nothing I couldn't achieve
| (or even better, program a computer to do) if I'd put my
| mind to it."
|
| I personally doubt that this would work. I don't think
| these systems can achieve truly ground-breaking,
| paradigm-shifting work. The homeworld of these systems is
| the corpus of text on which it was trained, in the same
| way as ours is physical reality. Their access to this
| reality is always secondary, already distorted by the
| imperfections of human knowledge.
| bmacho wrote:
| > Does anyone say "we don't know if Einstein could do
| this because we were really close or because he was
| really smart?
|
| Yes. It is certainly a question if Einstein is one of the
| smartest guy ever lived or all of his discoveries were
| already in the Zeitgeist, and would have been discovered
| by someone else in ~5 years.
| cyberax wrote:
| Both can be true?
|
| Einstein was smart and put several disjointed things
| together. It's amazing that one person could do so much,
| from explaining the Brownian motion to explaining the
| photoeffect.
|
| But I think that all these would have happened within
| _years_ anyway.
| emodendroket wrote:
| > Does anyone say "we don't know if Einstein could do
| this because we were really close or because he was
| really smart?"
|
| It turns out my reading is somewhat topical. I've been
| reading Rhodes' "The Making of the Atomic Bomb" and of
| the things he takes great pains to argue (I was not quite
| anticipating how much I'd be trying to recall my high
| school science classes to make sense of his account of
| various experiments) is that the development toward the
| atomic bomb was more or less inexorable and if at any
| point someone said "this is too far; let's stop here"
| there would be others to take his place. So, maybe, to
| answer your question.
| twoodfin wrote:
| It's been a while since I read it, but I recall Rhodes'
| point being that once the fundamentals of fission in
| heavy elements were validated, making a working bomb was
| no longer primarily a question of science, but one of
| engineering.
| sleet_spotter wrote:
| Well, if one had enough time and resources, this would
| make for an interesting metric. Could it figure it out
| with cut-off of 1900? If so, what about 1899? 1898? What
| context from the marginal year was key to the change in
| outcome?
| palmotea wrote:
| But that's not the OP's challenge, he said "if the model
| comes up with anything _even remotely correct_. " The point
| is there were things _already_ "remotely correct" out there
| in 1900. If the LLM finds them, it wouldn't "be quite a
| strong evidence that LLMs are a path to something bigger."
| pegasus wrote:
| It's not the comment which is illogical, it's your
| (mis)interpretation of it. What I (and seemingly others)
| took it to mean is basically _could an LLM do Einstein 's
| job_? Could it weave together all those loose threads
| into a coherent new way of understanding the physical
| world? If so, AGI can't be far behind.
| feanaro wrote:
| This alone still wouldn't be a clear demonstration that
| AGI is around the corner. It's quite possible a LLM
| could've done Einstein's job, if Einstein's job was truly
| just synthesising already available information into a
| coherent new whole. (I couldn't say, I don't know enough
| of the physics landscape of the day to claim either way.)
|
| It's still unclear whether this process could be merely
| continued, seeded only with new physical data, in order
| to keep progressing beyond that point, "forever", or at
| least for as long as we imagine humans will continue to
| go on making scientific progress.
| pegasus wrote:
| Einstein is chosen in such contexts because he's the
| paradigmatic paradigm-shifter. Basically, what you're
| saying is: "I don't know enough history of science to
| confirm this incredibly high opinion on Einstein's
| achievements. It could just be that everyone's been wrong
| about him, and if I'd really get down and dirty, and
| learn the facts at hand, I might even prove it." Einstein
| is chosen to avoid exactly this kind of nit-picking.
| Shorel wrote:
| They can also choose Euler or Gauss.
|
| These two are so above everyone else in the mathematical
| world that most people would struggle for weeks or even
| months to understand something they did in a couple of
| minutes.
|
| There's no "get down and dirty" shortcut with them =)
| feanaro wrote:
| No, by saying this, I am _not_ downplaying Einstein 's
| sizeable achievements nor trying to imply everyone was
| wrong about him. His was an impressive breadth of
| knowledge and mathematical prowess and there's no denying
| this.
|
| However, what I'm saying is not mere nitpicking either.
| It is precisely because of my belief in Einstein's
| extraordinary abilities that I find it unconvincing that
| an LLM being able to recombine the extant written
| physics-related building blocks of 1900, with its
| practically infinite reading speed, necessarily
| demonstrates comparable capabilities to Einstein.
|
| The essence of the question is this: would Einstein,
| having been granted eternal youth and a neverending
| source of data on physical phenomena, be able to innovate
| forever? Would an LLM?
|
| My position is that even if an LLM _is_ able to
| synthesise special relativity given 1900 knowledge, this
| doesn 't necessarily mean that a positive answer to the
| first question implies a positive answer to the second.
| ubercore wrote:
| Isn't it an interesting question? Wouldn't you like to
| know the answer? I don't think anyone is claiming
| anything more than an interesting thought experiment.
| frotaur wrote:
| I'm sorry, but 'not being surprised if LLMs can rederive
| relativity and QM from the facts available in 1900' is a
| pretty scalding take.
|
| This would absolutely be very good evidence that models
| can actually come up with novel, paradigm-shifting ideas.
| It was absolutely not obvious at that time from the
| existing facts, and some crazy leap of faiths needed to
| be taken.
|
| This is especially true for General Relativity, for which
| you had just a few mismatch in the mesurements like
| Mercury's precession, and where the theory almost
| entirely follows from thought experiments.
| techno_tsar wrote:
| This does make me think about Kuhn's concept of
| scientific revolutions and paradigms, and that paradigms
| are incommensurate with one another. Since new paradigms
| can't be proven or disproven by the rules of the old
| paradigm, if an LLM could independently discover paradigm
| shifts similar to moving from Newtonian gravity to
| general relativity, then we have empirical evidence of an
| LLM performing a feature of general intelligence.
|
| However, you could also argue that it's actually
| empirical evidence that general relativity and 19th
| century physics wasn't _truly_ a paradigm shift -- you
| could have 'derived' it from previous data -- that the
| LLM has actually proven something about structurally
| similarities between those paradigms, not that it's
| demonstrating general intelligence...
| somenameforme wrote:
| His concept sounds odd. There will always be many hints
| of something yet to be discovered, simply by the nature
| of anything worth discovering having an influence on
| other things.
|
| For instance spectroscopy enables one to look at the
| spectra emitted by another 'thing', perhaps the sun, and
| it turns out that there's little streaks within the
| spectra the correspond directly to various elements. This
| is how we're able to determine the elemental composition
| of things like the sun.
|
| That connection between elements and the patterns in
| their spectra was discovered in the early 1800s. And
| those patterns are caused by quantum mechanical
| interactions and so it was perhaps one of the first big
| hints of quantum mechanics, yet it'd still be a century
| before we got to relativity, let alone quantum mechanics.
| johnnienaked wrote:
| You should read it
| ctoth wrote:
| I mean, "the pieces were already there" is true of
| everything? Einstein was synthesizing existing math and
| existing data is your point right?
|
| But the whole question is whether or not something can do
| that synthesis!
|
| And the "anyone who read all the right papers" thing -
| nobody actually reads all the papers. That's the
| bottleneck. LLMs don't have it. They will continue to not
| have it. Humans will continue to not be able to read
| faster than LLMs.
|
| Even me, using a speech synthesizer at ~700 WPM.
| feanaro wrote:
| > I mean, "the pieces were already there" is true of
| everything? Einstein was synthesizing existing math and
| existing data is your point right?
|
| If it's true of everything, then surely having an LLM
| work iteratively on the pieces, along with being provided
| additional physical data, will lead to the discovery of
| everything?
|
| If the answer is "no", then surely something is still
| missing.
|
| > And the "anyone who read all the right papers" thing -
| nobody actually reads all the papers. That's the
| bottleneck. LLMs don't have it. They will continue to not
| have it. Humans will continue to not be able to read
| faster than LLMs.
|
| I agree with this. This is a definitive advantage of
| LLMs.
| andai wrote:
| AGI is human level intelligence, and the minimum bar is
| _Einstein?_
| pegasus wrote:
| Who said anything of a _minimum_ bar? "If so", not "Only
| if so".
| andy12_ wrote:
| I think the problem is the formulation "If so, AGI can't
| be far behind". I think that if a model were advanced
| enough such that it could do Einstein's job, that's it;
| that's AGI. Would it be ASI? Not necessarily, but that's
| another matter.
| somenameforme wrote:
| The phone in your pocket can perform arithmetic many
| orders of magnitude faster than any human, even the
| fringe autistic savant type. Yet it's still obviously not
| intelligent.
|
| Excellence at any given task is not indicative of
| intelligence. I think we set these sort of false
| goalposts because we want something that sounds
| achievable but is just out of reach at one moment in
| time. For instance at one time it was believed that a
| computer playing chess at the level of a human would be
| proof of intelligence. Of course it sounds naive now, but
| it was genuinely believed. It ultimately not being so is
| not us moving the goalposts, so much as us setting
| artificially low goalposts to begin with.
|
| So for instance what we're speaking of here is logical
| processing across natural language, yet human
| intelligence predates natural language. It poses a bit of
| a logical problem to then define intelligence as the
| logical processing of natural language.
| andy12_ wrote:
| The problem is that so far, SOTA generalist models are
| not excellent at just one particular task. They have a
| very wide range of tasks they are good at, and good
| scores in one particular benchmarks correlates very
| strongly with good scores in almost all other benchmarks,
| even esoteric benchmarks that AI labs certainly didn't
| train against.
|
| I'm sure, without any uncertainty, that any generalist
| model able to do what Einstein did would be AGI, as in,
| that model would be able to perform any cognitive task
| that an intelligent human being could complete in a
| reasonable amount of time (here "reasonable" depends on
| the task at hand; it could be minutes, hours, days,
| years, etc).
| somenameforme wrote:
| I see things rather differently. Here's a few points in
| no particular order:
|
| (1) - A major part of the challenge is in not being
| directed towards something. There was no external
| guidance for Einstein - he wasn't even a formal
| researcher at the time of his breakthroughs. An LLM
| _might_ be able to be handheld towards relativity, though
| I doubt it, but given the prompt of 'hey find something
| revolutionary' it's obviously never going to respond with
| anything relevant, even with substantially greater
| precision specifying field/subtopic/etc.
|
| (2) - Logical processing of natural language remains one
| small aspect of intelligence. For example - humanity
| invented natural language from nothing. The concept of an
| LLM doing this is a nonstarter since they're dependent
| upon token prediction, yet we're speaking of starting
| with 0 tokens.
|
| (3) - LLMs are, in many ways, very much like calculators.
| They can indeed achieve some quite impressive feats in
| specific domains, yet then they will completely
| hallucinate nonsense on relatively trivial queries,
| particularly on topics where there isn't extensive data
| to drive their token prediction. I don't entirely
| understand your extreme optimism towards LLMs given this
| proclivity for hallucination. Their ability to produce
| compelling nonsense makes them particularly tedious for
| using to do anything you don't already effectively know
| the answer to.
| andy12_ wrote:
| > I don't entirely understand your extreme optimism
| towards LLMs given this proclivity for hallucination
|
| Simply because I don't see hallucinations as a permanent
| problem. I see that models keep improving more and more
| in this regard, and I don't see why the hallucination
| rate can't be abirtrarily reduced with further
| improvements to the architecture. When I ask Claude about
| obscure topics, it correctly replies "I don't know",
| where past models would have hallucinated an answer. When
| I use GPT 5.2-thinking for my ML research job, I pretty
| much never encounter hallucinations.
| somenameforme wrote:
| Hahah, well you working in the field probably explains
| your optimism more than your words! If you pretty much
| never encounter hallucinations with GPT then you're
| probably dealing with it on topics where there's less of
| a right or wrong answer. I encounter them literally every
| single time I start trying to work out a technical
| problem with it.
| andai wrote:
| Well the "prompt" in this case would be Einstein's
| neurotype and all his life experiences. Might a bit long
| for the current context windows though ;)
| andai wrote:
| Actually it's worse than that, the comment implied that
| Einstein wouldn't even qualify for AGI. But I thought the
| conversation was pedantic enough without my contribution
| ;)
| f0ti wrote:
| Einstein is not AGI, and neither the other way around.
| johnnienaked wrote:
| LLMs don't make inferential leaps like that
| golem14 wrote:
| I think it's not productive to just have the LLM site like
| Mycroft in his armchair and from there, return you an
| excellent expert opinion.
|
| THat's not how science works.
|
| The LLM would have to propose experiments (which would have
| to be simulated), and then develop its theories from that.
|
| Maybe there had been enough facts around to suggest a
| number of hypotheses, but the LLM in its curent form won't
| be able to confirm them.
| bhaak wrote:
| This would still be valuable even if the LLM only finds out
| about things that are already in the air.
|
| It's probably even more of a problem that different areas of
| scientific development don't know about each other. LLMs
| combining results would still not be like they invented
| something new.
|
| But if they could give us a head start of 20 years on certain
| developments this would be an awesome result.
| gus_massa wrote:
| I agree, but it's important to note that QM has no clear
| formulation until 2025/6, it's like 20 years more of work
| than SR.
| pests wrote:
| 2025/6?
| gus_massa wrote:
| * 1925/6, sorry, bad century.
| Shorel wrote:
| Then that experiment is even more interesting, and should be
| done.
|
| My own prediction is that the LLMs would totally fail at
| connecting the dots, but a small group of very smart humans
| can.
|
| Things don't happen all of a sudden, but they also don't
| happen everywhere. Most people in most parts of the world
| would never connect the dots. Scientific curiosity is
| something valuable and fragile, that we just take for
| granted.
| bigfudge wrote:
| One of the reasons they don't happen everywhere is because
| there are just a few places at any given point in time
| where there are enough well connected and educated
| individuals who are in a position to even see all the dots
| let alone connect them. This doesn't discount the
| achievement of an LLM also manages to, but I think it's
| important to recognise that having enough giants in sight
| is an important prerequisite to standing on their shoulders
| djwide wrote:
| With LLMs the synthesis cycles could happen at a much higher
| frequency. Decades condensed to weeks or days?
|
| I imagine possible buffers on that conjecture synthesis being
| epxerimentation and acceptance by the scientific community.
| AIs can come up with new ideas every day but Nature won't
| publish those ideas for years.
| mannykannot wrote:
| If (as you seem to be suggesting) relativity was effectively
| lying there on the table waiting for Einstein to just pick it
| up, how come it blindsided most, if not quite all, of the
| greatest minds of his generation?
| TeMPOraL wrote:
| That's the case with all scientific discoveries - pieces of
| prior work get accumulated, until it eventually becomes
| obvious[0] how they connect, at which point someone[1]
| connects the dots, making a discovery... and putting it on
| the table, for the cycle to repeat anew. This is, in a
| nutshell, the history of all scientific and technological
| progress. Accumulation of tiny increments.
|
| --
|
| [0] - To people who happen to have the right background and
| skill set, and are in the right place.
|
| [1] - Almost always multiple someones, independently,
| within short time of each other. People usually remember
| only one or two because, for better or worse, history is
| much like patent law: first to file wins.
| mannykannot wrote:
| Sure - and climbing a mountain is just putting one foot
| down higher than it was before and repeating, once you
| abstract away all the hard parts.
| TeMPOraL wrote:
| It is. If you're at the mountain, on the right trail, and
| have the right clothing and equipment for the task.
|
| That's why those tiny steps of scientific and
| technological progress aren't made by just any randos -
| they're made by people who _happen to be_ at the right
| place and time, and equipped correctly to be able to take
| the step.
|
| The important corollary to this is that you can't
| generally predict this ahead of time. Someone like
| Einstein was needed to nail down relativity, but standing
| there few years earlier, you couldn't have predicted it
| was Einstein who would make a breakthrough, nor what
| would that be about. Conversely, if Einstein lived 50
| years earlier, he wouldn't have come up with relativity,
| because necessary prerequisites - knowledge, people,
| environment - weren't there yet.
| famouswaffles wrote:
| Science often advances by accumulation, and it's true
| that multiple people frequently converge on similar ideas
| once the surrounding toolkit exists. But "it becomes
| obvious" is doing a lot of work here, and the history
| around relativity (special and general) is a pretty good
| demonstration that it often doesn't become obvious at
| all, even to very smart people with front-row seats.
|
| Take Michelson in 1894: after doing (and inspiring) the
| kind of precision work that should have set off alarm
| bells, he's still talking like the fundamentals are
| basically done and progress is just "sixth decimal place"
| refinement.
|
| "While it is never safe to affirm that the future of
| Physical Science has no marvels in store even more
| astonishing than those of the past, it seems probable
| that most of the grand underlying principles have been
| firmly established and that further advances are to be
| sought chiefly in the rigorous application of these
| principles to all the phenomena which come under our
| notice. It is here that the science of measurement shows
| its importance -- where quantitative work is more to be
| desired than qualitative work. An eminent physicist
| remarked that the future truths of physical science are
| to be looked for in the sixth place of decimals." -
| Michelson 1894
|
| The Michelson-Morley experiments weren't obscure, they
| were famous, discussed widely, and their null result was
| well-known. Yet for nearly two decades, the greatest
| physicists of the era proposed increasingly baroque
| modifications to existing theory rather than question the
| foundational assumption of absolute time. These weren't
| failures of data availability or technical skill, they
| were failures of imagination constrained by what seemed
| obviously true about the nature of time itself.
|
| Einstein's insight wasn't just "connecting dots" here, it
| was recognizing that a dot everyone thought was fixed
| (the absoluteness of simultaneity) could be moved, and
| that doing so made everything else fall into place.
|
| People scorn the 'Great Man Hypothesis' so much they
| sometimes swing too much in the other direction. The
| 'multiple discovery' pattern you cite is real but often
| overstated. For Special Relativity, Poincare came close,
| but didn't make the full conceptual break. Lorentz had
| the mathematics but retained the aether. The gap between
| 'almost there' and 'there' can be enormous when it
| requires abandoning what seems like common sense itself.
| somenameforme wrote:
| It's only easy to see precursors in hindsight. The Michelson-
| Morley tale is a great example of this. In hindsight, their
| experiment was screaming relativity, because it demonstrated
| that the speed of light was identical from two perspectives
| where it's very difficult to explain without relativity.
| Lorentz contraction was just a completely ad-hoc proposal to
| maintain the assumptions of the time (luminiferous aether in
| particular) while also explaining the result. But in general
| it was not seen as that big of a deal.
|
| There's a very similar parallel with dark matter in modern
| times. We certainly have endless hints to the truth that will
| be evident in hindsight, but for now? We are mostly convinced
| that we know the truth, perform experiments to prove that,
| find nothing, shrug, adjust the model to be even more
| esoteric, and repeat onto the next one. And maybe one will
| eventually show something, or maybe we're on the wrong path
| altogether. This quote, from Michelson in 1894 (more than a
| decade before Einstein would come along), is extremely
| telling of the opinion at the time:
|
| "While it is never safe to affirm that the future of Physical
| Science has no marvels in store even more astonishing than
| those of the past, it seems probable that most of the grand
| underlying principles have been firmly established and that
| further advances are to be sought chiefly in the rigorous
| application of these principles to all the phenomena which
| come under our notice. It is here that the science of
| measurement shows its importance -- where quantitative work
| is more to be desired than qualitative work. An eminent
| physicist remarked that the future truths of physical science
| are to be looked for in the sixth place of decimals." -
| Michelson 1894
| vasco wrote:
| With the passage of time more and more things have been
| discovered through precision. Through identifying small
| errors in some measurement and pursuing that to find the
| cause.
| somenameforme wrote:
| It's not precision that's the problem, but understanding
| when something has been falsified. For instance the
| Lorentz transformations work as a perfectly fine ad-hoc
| solution to Michelson's discovery. All it did was make
| the aether a bit more esoteric in nature. Why do you then
| not simply shrug, accept it, and move on? Perhaps even
| toss some accolades towards Lorentz for 'solving' the
| puzzle? Michelson himself certainly felt there was no
| particularly relevant mystery outstanding.
|
| For another parallel our understanding of the big bang
| was, and probably is, wrong. There are a lot of problems
| with the traditional view of the big bang with the
| horizon problem [1] being just one among many - areas in
| space that should not have had time to interact behave
| like they have. So this was 'solved' by an ad hoc
| solution - just make the expansion of the universe go
| into super-light speed for a fraction of a second at a
| specific moment, slow down, then start speeding up again
| (cosmic inflation [2]) - and it all works just fine. So
| you know what we did? Shrugged, accepted it, and even
| gave Guth et al a bunch of accolades for 'solving' the
| puzzle.
|
| This is the problem - arguably the most important
| principle of science is falsifiability. But when is
| something falsified? Because in many situations, probably
| the overwhelming majority, you can instead just use one
| falsification to create a new hypothesis with that nuance
| integrated into it. And as science moves beyond singular
| formulas derived from clear principles or laws and onto
| broad encompassing models based on correlations from
| limited observations, this becomes more and more true.
|
| [1] - https://en.wikipedia.org/wiki/Horizon_problem
|
| [2] - https://en.wikipedia.org/wiki/Cosmic_inflation
| jojobas wrote:
| They were close, but it required the best people bashing
| their heads against each other for years until they got it.
| dogma1138 wrote:
| That is the point.
|
| New discoveries don't happen in a vacuum.
| eru wrote:
| You can get pretty far by modeling only frictionless,
| spherical discoveries in a vacuum.
| metalliqaz wrote:
| Yann LeCun spoke explicitly on this idea recently and he
| asserts definitively that the LLM would not be able to add
| anything useful in that scenario. My understanding is that
| other AI researchers generally agree with him, and that it's
| mostly the hype beasts like Altman that think there is some
| "magic" in the weights that is actually intelligent. Their
| payday depends on it, so it is understandable. My opinion is
| that LeCun is probably correct.
| johnsmith1840 wrote:
| There is some ability for it to make novel connections but
| it's pretty small. You can see this yourself having it build
| novel systems.
|
| It largely cannot imaginr anything beyond the usual but there
| is a small part that it can. This is similar to in context
| learning, it's weak but it is there.
|
| It would be incredible if meta learning/continual learning
| found a way to train exactly for novel learning path. But
| that's literally AGI so maybe 20yrs from now? Or never..
|
| You can see this on CL benchmarks. There is SOME signal but
| it's crazy low. When I was traing CL models i found that
| signal was in the single % points. Some could easily argue it
| was zero but I really do believe there is a very small amount
| in there.
|
| This is also why any novel work or findings is done via
| MASSIVE compute budgets. They find RL enviroments that can
| extract that small amount out. Is it random chance? Maybe,
| hard to say.
| SoftTalker wrote:
| Is this so different from what we see in humans? Most
| people do not think very creatively. They apply what they
| know in situations they are familiar with. In unfamiliar
| situations they don't know what to do and often fail to
| come up with novel solutions. Or maybe in areas where they
| are very experienced they will come up with something
| incrementally better than before. But occasionally a very
| exceptional person makes a profound connection or leap to a
| new understanding.
| johnsmith1840 wrote:
| Sure we make small steps at the time but we compound
| these unlike AI.
|
| AI cannot compound their learnings for the foreseeable
| future
| catigula wrote:
| This is definitely wrong, most AI researchers DO NOT agree
| with LeCun.
|
| Most ML researchers think AGI is imminent.
| p_j_w wrote:
| Who is in this group of ML researchers?
| shaky-carrousel wrote:
| People with OpenAI shares, probably.
| kingstnap wrote:
| Where do you get your majority from?
|
| I don't think there is any level of broad agreement right
| now. There are tons of random camps none of which I would
| consider to be broadly dominating.
| Alex2037 wrote:
| their employment and business opportunities depend on the
| hype, so they will continue to 'think' that (on xitter)
| despite the current SOTA of transformers-based models being
| <100% smarter than >3 year old GPT4, and no revolutionary
| new architecture in sight.
| catigula wrote:
| You're going to be in for a very rude awakening.
| johnsmith1840 wrote:
| The guy who built chatgpt literally said we're 20 years
| away?
|
| Not sure how to interpret that as almost imminent.
| nottorp wrote:
| > The guy who built chatgpt literally said we're 20 years
| away?
|
| 20 years away in 2026, still 20 years away in 2027, etc
| etc.
|
| Whatever Altman's hyping, that's the translation.
| rafram wrote:
| The ones being paid a million dollars a year by OpenAI to
| say stuff like that, maybe.
| goatlover wrote:
| Do you have poll of ML researchers that shows this?
| paodealho wrote:
| Well, can you point us to their research then? Please.
| mlinksva wrote:
| Do you have a pointer to where LeCun spoke about it? I
| noticed last October that Dwarkesh mentioned the idea off
| handedly on his podcast (prompting me to write up
| https://manifold.markets/MikeLinksvayer/llm-trained-on-
| data-...) but I wonder if this idea has been around for much
| longer, or is just so obvious that lots of people are
| independently coming up with it (parent to this comment being
| yet another)?
| samuelson wrote:
| Preface: Most of my understand of how LLMs actually work
| comes from 3blue1brown's videos, so I could easily be wrong
| here.
|
| I mostly agree with you, especially about distrusting the
| self-interested hype beasts.
|
| While I don't think the models are actually "intelligent", I
| also wonder if there are insights to be gained by looking at
| how concepts get encoded by the models. It's not really that
| the models will add something "new", but more that there
| might be connections between things that we haven't noticed,
| especially because academic disciplines are so insular these
| days.
| matheusd wrote:
| How about this for an evaluation: Have this (trained-on-
| older-corpus) LLM propose experiments. We "play the role of
| nature" and inform it of the results of the experiments. It
| can then try to deduce the natural laws.
|
| If we did this (to a good enough level of detail), would it
| be able to derive relativity? How large of an AI model would
| it have to be to successfully derive relativity (if it only
| had access to everything published up to 1904)?
| SirHumphrey wrote:
| I don't know if any dataset of pre 1904 writing would be
| large enough to train a model that would be smart enough. I
| suspect that current sized SOTA models would at least get
| to special relativity, but for general relativity and
| quantum mechanics I am less sure.
| djwide wrote:
| What do they (or you) have to say about the Lee Sedol AlphaGo
| move 78. It seems like that was "new knowledge." Are games
| just iterable and the real world idea space not? I am playing
| with these ideas a little.
| metalliqaz wrote:
| AlphaGo is not an LLM
| drdeca wrote:
| And? Do the arguments differ for LLM vs the other models?
|
| I guess the arguments sometimes mention languages. But I
| feel like the core of the arguments are pretty much the
| same regardless?
| metalliqaz wrote:
| The discussion is about training an LLM on old text and
| then asking it about new concepts.
| DevX101 wrote:
| Chemistry would be a great space to explore. The last quarter
| of the 19th century had a ton of advancements in chemistry.
| It'd be interesting the see if an LLM could propose fruitful
| hypotheses, made predictions of the science of thermodynamics.
| bravura wrote:
| A rigorous approach to predicting the future of text was
| proposed by Li et al 2024, "Evaluating Large Language Models
| for Generalization and Robustness via Data Compression"
| (https://ar5iv.labs.arxiv.org/html//2402.00861) and I think
| that work should get more recognition.
|
| They measure compression (perplexity) on future Wikipedia, news
| articles, code, arXiv papers, and multi-modal data. Data
| compression is intimately connected with robustness and
| generalization.
| Otterly99 wrote:
| Thanks for the paper, I just read it and loved the approach.
| I hope the concept of using data compression as a benchmark
| will take off. In a sense it is kind of similar to the maxim
| "If you cannot explain something in simple terms, you do not
| understand it fully".
| nickdothutton wrote:
| I would love to ask such a model to summarise the handful of
| theories or theoretical "roads" being eyed at the time and to
| make a prediction with reasons as to which looks most
| promising. We might learn something about blind spots in human
| reasoning, institutions, and organisations that are applicable
| today in the "future".
| nickpsecurity wrote:
| That would be an interesting experiment. It might be more
| useful to make a model with a cut off close to when copyrights
| expire to be as modern as possible.
|
| Then, we have a model that knows quite a bit in modern English.
| We also legally have a data set for everything it knows. Then,
| there's all kinds of experimentation or copyright-safe training
| strategies we can do.
|
| Project Gutenberg up to the 1920's seems to be the safest bet
| on that.
| samuelson wrote:
| I think it would be fun to see if an LLM would reframe some
| scientific terms from the time in a way that would actually fit
| in our current theories.
|
| I imagine if you explained quantum field theory to a 19th
| century scientists they might think of it as a more refined
| understanding of luminiferous aether.
|
| Or if an 18th century scholar learned about positive and
| negative ions, it could be seen as an expansion/correction of
| phlogiston theory.
| wongarsu wrote:
| I'm trying to work towards that goal by training a model on
| mostly German science texts up to 1904 (before the world wars
| German was the lingua franca of most sciences).
|
| Training data for a base model isn't that hard to come by, even
| though you have to OCR most of it yourself because the publicly
| available OCRed versions are commonly unusably bad. But
| training a model large enough to be useful is a major issue.
| Training a 700M parameter model at home is very doable (and is
| what this TimeCapsuleLLM is), but to get that kind of reasoning
| you need something closer to a 70B model. Also a lot of the
| "smarts" of a model gets injected in fine tuning and RL, but
| any of the available fine tuning datasets would obviously
| contaminate the model with 2026 knowledge.
| theallan wrote:
| Can we follow along with your work / results somewhere?
| benbreen wrote:
| I am a historian and am putting together a grant application
| for a somewhat similar project (different era and language
| though). Would you be open to discussing a collaboration? My
| email is bebreen [at] ucsc [dot] edu.
| root_axis wrote:
| I think it would raise some interesting questions, but if it
| did yield anything noteworthy, the biggest question would be
| why that LLM is capable of pioneering scientific advancements
| and none of the modern ones are.
| spidersouris wrote:
| I'm not sure what you'd call a "pioneering scientific
| advancement", but there is an increasing amount of examples
| showing that LLMs can be used for research (with agents,
| particularly). A survey about this was published a few months
| ago: https://aclanthology.org/2025.emnlp-main.895.pdf
| crazylogger wrote:
| Or maybe, LLMs _are_ pioneering scientific advancements -
| people are using LLMs to read papers, choose what problems to
| work on, come up with experiments, analyze results, and draft
| papers, etc., at this very moment. Except they eventually
| stick their human names on the cover so we almost never know.
| defgeneric wrote:
| The development of QM was so closely connected to experiments
| that it's highly unlikely, even despite some of the experiments
| having been performed prior to 1900.
|
| Special relativity however seems possible.
| damnitbuilds wrote:
| I like this, it would be exciting (and scary) if it deduced QM,
| and informative if it cannot.
|
| But I also think we can do this with normal LLMs trained on up-
| to-date text, by asking them to come up with _any_ novel theory
| that fits the facts. It does not have to be a groundbreaking
| theory like QM, just original and not (yet) proven wrong ?
| kristopolous wrote:
| It's going to be divining tea leaves. It will be 99% wrong and
| then someone will say 'oh but look at this tea leaf over here!
| It's almost correct"'
| bowmessage wrote:
| Look! It made another TODO-list app on the first try!
| darkwater wrote:
| Yes but... aren't human researchers doing the same? They are
| mostly wrong most of the times, and try again, and verify
| again their work, until they find something that actually
| works. What I mean is that this "in hindsight" test would be
| biased by being in hindsight, because we know already the
| answer so we would discard the LLM answer as just randomly
| generated. But "connecting the dots" is basically doing a lot
| try and error in your mind, emitting only the results that
| make at least some kind of sense to us.
| SecretDreams wrote:
| I like this idea. I think I'd like it more if we didn't have to
| prompt the LLM in the first place. If it just had all of this
| information and decided to act upon it. That's what the great
| minds of history (and even average minds like myself) do. Just
| think about the facts in our point of view and spontaneously
| reason something greater out of them.
| mannykannot wrote:
| That is a very interesting idea, though I would not dismiss
| LLMs as a dead end if they failed.
| Affric wrote:
| Wow, an actual scientific experiment. Does anyone with
| expertise know if such things have been done?
| amypetrik214 wrote:
| >.If the model comes up with anything even remotely correct it
| would be quite a strong evidence that LLMs are a path to
| something bigger if not then I think it is time to go back to
| the drawing board.
|
| In principle I see your point, in practice my default
| assumption until proven otherwise here -- is that a little
| something slipped through post-1900.
|
| A much easier approach would be to just download some model,
| whatever model, today. Then 5 years from now, whatever
| interesting discoveries are found - can the model get there.
| dogma1138 wrote:
| Not really, QM and Relativity were chosen because they were
| theories that were created to fit observations and data.
| Discoveries over the next 5 years will be trivia rather than
| logical conclusions.
| staticman2 wrote:
| Don't you need to do reinforcement learning through human
| feedback to get non gibberish results from the models in
| general?
|
| 1900 era humans are not available to do this so I'm not sure
| how this experiment is supposed to work.
| jaydepun wrote:
| We've thought of doing this sort of exercise at work but mostly
| hit the wall of data becoming a lot more scare the further back
| in time we go. Particularly high quality science data - even
| going pre 1970 (and that's already a stretch) you lose a lot of
| information. There's a triple whammy of data still existing,
| being accessible in any format, and that format being suitable
| for training an LLM. Then there's the complications of wanting
| additional model capabilities that won't leak data causally.
| permo-w wrote:
| I was wondering this. what is the minimum amount of text an
| LLM needs to be coherent? fun of an idea as this is, the
| samples of its responses are basically babbling nonsense.
| going further, a lot of what makes LLMs so strong isn't their
| original training data, but the RLHF done afterwards. RLHF
| would be very difficult in this case
| pseudohadamard wrote:
| It's already been done, without the model being aware of it,
| see https://arxiv.org/abs/2512.09742. They also made it think
| it was Hitler (not MechaHitler, the other guy), and other
| craziness.
|
| It's a relief to think that we're not trusting these things for
| stuff like financial advice, medical advice, mental health
| counselling, ...
| catlifeonmars wrote:
| That's how p-hacking works (or doesn't work). This is analogous
| to shooting an arrow and then drawing a target around where it
| lands.
| alkindiffie wrote:
| I like that analogy. It reminds me of "Pointing to the moon
| and looking at my finger"
| cornholio wrote:
| Yes, I don't understand how such an experiment could work.
| You either:
|
| A). contaminate the model with your own knowledge of
| relativity, leading it on to "discover" what you know, or
|
| B). you will try to simulate a blind operation but without
| the "competent human physicist knowledgeable up to the the
| 1900 scientific frontier" component prompting the LLM,
| because no such person is alive today nor can you simulate
| them (if you could, then by definition you can use that
| simulated Einstein to discover relativity, so the problem is
| moot).
|
| So in both cases you would prove nothing about what a smart
| and knowledgeable scientist can achieve today from a frontier
| LLM.
| alkindiffie wrote:
| That would be possible if LLMs can come up with entirely new
| words and languages, which I doubt.
| isolli wrote:
| You have to make sure that you make it read an article about a
| painter falling off a roof with his tools.
| swalsh wrote:
| Could be an interesting experiment, but its not conclusive
| proof one way or another. So much of what makes LLMs so great
| today (vs gpt 3.5) would not be in that dataset. The training
| to turn these models into coding savants has generalized to
| other areas just as one example.
| redman25 wrote:
| It's a base model. It hasn't been instruction tuned to "solve
| problems" necessarily. All it can do is attempt to complete
| text given some starting text.
| m463 wrote:
| I wonder about something similar - leeches. Apparently they
| really work to eat away only decaying tissue. Could they make a
| comeback in modern times with better weighting?
| simonw wrote:
| Anyone seen a low-friction way to run prompts through this yet,
| either via a hosted API or chat UI or a convenient GGML or MLX
| build that runs in Ollama or llama.cpp or LM Studio?
| philmo1 wrote:
| +1
| d401 wrote:
| +1
| t1amat wrote:
| Not a direct answer but it looks like v0.5 is a nanoGPT arch
| and v1 is a Phi 1.5 arch, which should be well supported by
| quanting utilities for any engine. They are small too and
| should be able to be done on a potato.
| alansaber wrote:
| I too have completely forgotten how the adapters library works
| and would have appreciated a simple inference script
| throwaway18875 wrote:
| Currently running it using LM Studio. It can download it from
| Hugging Face. It generates incoherent text though
|
| ===
|
| You:
|
| I pray you, who is this Master Newton?
|
| timecapsulellm-v2-1800-1875-mlx:
|
| TI offer to pay you the very same fee as you did before. It was
| not in the power of your master to deliver the letter to your
| master. He did. I will be with you as soon as I can keep my
| word. It is not at all clear, whether the letter has been sent
| or not. It is not at all clear: but it is clear also that it
| was written by the person who gave it. "No," I said, "I cannot
| give it to you." There, the letter was sent to me. "The letter
| is yours, I believe," I said. "But, I hope, you will not refuse
| to give it to me?
| simonw wrote:
| Thanks, looks like that's this one: https://huggingface.co/Fr
| actalSurfer/TimeCapsuleLLM-v2-1800-...
|
| There's a "Use this model" button on that page that can
| launch it in LM Studio.
| philmo1 wrote:
| Exciting idea!
| dhruv3006 wrote:
| This will be something good - would love something on Ollama or
| lmstudio.
| srigi wrote:
| "I'm sorry, my knowledge cuttoff is 1875"
| HarHarVeryFunny wrote:
| It would be interesting if there's enough data to train a model
| capable enough to converse with and ask about contemporary views
| on issues of the day, or what it thought about "potential" future
| events/technologies yet to happen.
| eqmvii wrote:
| Could this be an experiment to show how likely LLMs are to lead
| to AGI, or at least intelligence well beyond our current level?
|
| If you could only give it texts and info and concepts up to Year
| X, well before Discovery Y, could we then see if it could prompt
| its way to that discovery?
| alansaber wrote:
| I think not if only for the fact that the quantity of old data
| isn't enough to train anywhere near a SoTA model, until we
| change some fundamentals of LLM architecture
| franktankbank wrote:
| Are you saying it wouldn't be able to converse using english
| of the time?
| wasabi991011 wrote:
| That's not what they are saying. SOTA models include much
| more than just language, and the scale of training data is
| related to its "intelligence". Restricting the corpus in
| time => less training data => less intelligence => less
| ability to "discover" new concepts not in its training data
| franktankbank wrote:
| Perhaps less bullshit though was my thought? Was language
| more restricted then? Scope of ideas?
| withinboredom wrote:
| Could always train them on data up to 2015ish and then
| see if you can rediscover LLMs. There's plenty of data.
| ben_w wrote:
| Machine learning today requires an obscene quantity of
| examples to learn anything.
|
| SOTA LLMs show quite a lot of skill, but they only do so
| after reading a significant fraction of all published
| writing (and perhaps images and videos, I'm not sure)
| across all languages, in a world whose population is 5
| times higher than the link's cut off date, and the global
| literacy went from 20% to about 90% since then.
|
| Computers can only make up for this by being really really
| fast: what would take a human a million or so years to
| read, a server room can pump through a model's training
| stage in a matter of months.
|
| When the data isn't there, reading what it does have really
| quickly isn't enough.
| andyfilms1 wrote:
| I mean, _humans_ didn 't need to read billions of books back
| then to think of quantum mechanics.
| famouswaffles wrote:
| Right, what they needed was billions of years of brute
| force and trial and error.
| alansaber wrote:
| Which is why I said it's not impossible, but current LLM
| architecture is just not good enough to achieve this.
| ben_w wrote:
| > Could this be an experiment to show how likely LLMs are to
| lead to AGI, or at least intelligence well beyond our current
| level?
|
| You'd have to be specific what you mean by AGI: all three
| letters mean a different thing to different people, and
| sometimes use the whole means something not present in the
| letters.
|
| > If you could only give it texts and info and concepts up to
| Year X, well before Discovery Y, could we then see if it could
| prompt its way to that discovery?
|
| To a limited degree.
|
| Some developments can come from combining existing ideas and
| seeing what they imply.
|
| Other things, like everything to do with relativity and quantum
| mechanics, would have required experiments. I don't think any
| of the relevant experiments had been done prior to this cut-off
| date, but I'm not absolutely sure of that.
|
| You might be able to get such an LLM to develop all the maths
| and geometry for general relativity, and yet find the AI still
| tells you that the perihelion shift of Mercury is a sign of the
| planet Vulcan rather than of a curved spacetime:
| https://en.wikipedia.org/wiki/Vulcan_(hypothetical_planet)
| markab21 wrote:
| Basically looking for emergent behavior.
| grimgrin wrote:
| An example of why you need to explain what you mean by AGI
| is:
|
| https://www.robinsloan.com/winter-garden/agi-is-here/
| opponent4 wrote:
| > You'd have to be specific what you mean by AGI
|
| Well, they obviously can't. AGI is not science, it's
| religion. It has all the trappings of religion: prophets,
| sacred texts, origin myth, end-of-days myth and most
| importantly, a means to escape death. Science? Well, the only
| measure to "general intelligence" would be to compare to the
| only one which is the human one but we have absolutely no
| means by which to describe it. We do not know where to start.
| This is why you scrape the surface of any AGI definition you
| only find circular definitions.
|
| And no, the "brain is a computer" is not a scientific
| description, it's a metaphor.
| strbean wrote:
| > And no, the "brain is a computer" is not a scientific
| description, it's a metaphor.
|
| Disagree. A brain is turing complete, no? Isn't that the
| definition of a computer? Sure, it may be reductive to say
| "the brain is _just_ a computer ".
| opponent4 wrote:
| Not even close. Turing complete does not apply to the
| brain plain and simple. That's something to do with
| algorithms and your brain is not a computer as I have
| mentioned. It does not store information. It doesn't
| process information. It just doesn't work that way.
|
| https://aeon.co/essays/your-brain-does-not-process-
| informati...
| anthonypasq wrote:
| ive gotta say this article was not convincing at all.
| Closi wrote:
| A human is effectively turning complete if you give the
| person paper and pen and the ruleset, and a brain clearly
| stores information and processes it to some extent, so
| this is pretty unconvincing. The article is nonsense and
| badly written.
|
| > But here is what we are not born with: information,
| data, rules, software, knowledge, lexicons,
| representations, algorithms, programs, models, memories,
| images, processors, subroutines, encoders, decoders,
| symbols, or buffers - design elements that allow digital
| computers to behave somewhat intelligently. Not only are
| we not born with such things, we also don't develop them
| - ever.
|
| Really? Humans don't ever develop memories? Humans don't
| gain information?
| strbean wrote:
| > Forgive me for this introduction to computing, but I
| need to be clear: computers really do operate on symbolic
| representations of the world. They really store and
| retrieve. They really process. They really have physical
| memories. They really are guided in everything they do,
| without exception, by algorithms.
|
| This article seems really hung up on the distinction
| between digital and analog. It's an important
| distinction, but glosses over the fact that digital
| computers are a _subset_ of analog computers. Electrical
| signals are inherently analog.
|
| This maps somewhat neatly to human cognition. I can take
| a stream of bits, perform math on it, and output a
| transformed stream of bits. That is a digital operation.
| The underlying biological processes involved are a pile
| of complex probabilistic+analog signaling, true. But in a
| computer, the underlying processes are also probabilistic
| and analog. We have designed our electronics to shove
| those parts down to the lowest possible level so they can
| be abstracted away, and so the degree to which they
| influence computation is certainly lower than in the
| human brain. But I think an effective argument that
| brains are not computers is going to have to dive in to
| why that gap matters.
| nearbuy wrote:
| That is an article by a psychologist, with no expertise
| in neuroscience, claiming without evidence that the
| "dominant cognitive neuroscience" is wrong. He offers no
| alternative explanation on how memories are stored and
| retrieved, but argues that large numbers of neurons
| across the brain are involved and he implies that
| neuroscientists think otherwise.
|
| This is odd because the dominant view in neuroscience is
| that memories are stored by altering synaptic connection
| strength in a large number of neurons. So it's not clear
| what his disagreement is, and he just seems to be
| misrepresenting neuroscientists.
|
| Interestingly, this is also how LLMs store memory during
| training: by altering the strength of connections between
| many artificial neurons.
| stevenhuang wrote:
| It is pretty clear the author of that article has no idea
| what he's talking about.
|
| You should look into the physical church turning thesis.
| If it's false (all known tested physics suggests it's
| true) then well we're probably living in a dualist
| universe. This means something outside of material
| reality (souls? hypercomputation via quantum gravity?
| weird physics? magic?) somehow influences our cognition.
|
| > Turning complete does not apply to the brain
|
| As far as we know, any physically realizable process can
| be simulated by a turing machine. And FYI brains do not
| exist outside of physical reality.. as far as we know. If
| you have issue with this formulation, go ahead and
| disprove the physical church turning thesis.
| Davidzheng wrote:
| probably not actually turing complete right? for one it
| is not infinite so
| ben_w wrote:
| Cargo cults are a religion, the things they worship they do
| not understand, but the planes and the cargo themselves are
| real.
|
| There's certainly plenty of cargo-culting right now on AI.
|
| Sacred texts, I don't recognise. Yudkowsky's writings? He
| suggests wearing clown shoes to avoid getting a cult of
| personality disconnected from the quality of the arguments,
| if anyone finds his works sacred, they've fundamentally
| misunderstood him: I have sometimes thought
| that all professional lectures on rationality should be
| delivered while wearing a clown suit, to prevent the
| audience from confusing seriousness with solemnity.
|
| - https://en.wikiquote.org/wiki/Eliezer_Yudkowsky
|
| Prophets forecasting the end-of-days, yes, but this too
| from climate science, from everyone who was preparing for a
| pandemic before covid and is still trying to prepare for
| the next one because the wet markets are still around, from
| economists trying to forecast growth or collapse and what
| will change any given prediction of the latter into the
| former, and from the military forces of the world saying
| which weapon systems they want to buy. It does not make a
| religion.
|
| A means to escape death, you can have. But it's on a
| continuum with life extension and anti-aging medicine,
| which itself is on a continuum with all other medical
| interventions. To quote myself: Taking a
| living human's heart out without killing them, and
| replacing it with one you got out a corpse, that isn't the
| magic of necromancy, neither is it a prayer or ritual to
| Sekhmet, it's just transplant surgery. ...
| Immunity to smallpox isn't a prayer to the Hindu goddess
| Shitala (of many things but most directly linked with
| smallpox), and it isn't magic herbs or crystals, it's just
| vaccines.
|
| -
| https://benwheatley.github.io/blog/2025/06/22-13.21.36.html
| nomel wrote:
| > And no, the "brain is a computer" is not a scientific
| description, it's a metaphor.
|
| I have trouble comprehending this. What is "computer" to
| you?
| feisty0630 wrote:
| I fail to see how the two concepts equate.
|
| LLMs have neither intelligence nor problem-solving abillity
| (and I won't be relaxing the definition of either so that some
| AI bro can pretend a glorified chatbot is sentient)
|
| You would, at best, be demonstrating that the sharing of
| knowledge across multiple disciplines and nations (which is a
| relatively new concept - at least at the scale of something
| like the internet) leads to novel ideas.
| al_borland wrote:
| I've seen many futurists claim that human innovation is dead
| and all future discoveries will be the results of AI. If this
| is true, we should be able to see AI trained on the past
| figure it's way to various things we have today. If it can't
| do this, I'd like said futurists to quiet down, as they are
| discouraging an entire generation of kids who may go on to
| discover some great things.
| skissane wrote:
| > I've seen many futurists claim that human innovation is
| dead and all future discoveries will be the results of AI.
|
| I think there's a big difference between discoveries
| through AI-human synergy and discoveries through AI working
| in isolation.
|
| It probably will be true soon (if it isn't already) that
| most innovation features some degree of AI input, but still
| with a human to steer the AI in the right direction.
|
| I think an AI being able to discover something genuinely
| new all by itself, without any human steering, is a lot
| further off.
|
| If AIs start producing significant quantities of genuine
| and useful innovation with minimal human input, maybe the
| singularitarians are about to be proven right.
| thinkingemote wrote:
| I'm struggling to get a handle on this idea. Is the idea
| that today's data will be the data of the past, in the
| future?
|
| So if it can work with whats now past, it will be able to
| work with the past in the future?
| al_borland wrote:
| Essentially, yes.
|
| If the prediction is that AI will be able to invent the
| future. If we give it data from our past without
| knowledge of the present... what type of future will it
| invent, what progress will it make, if any at all? And
| not just having the idea, but how to implement the idea
| in a way that actually works with the technology of the
| day, and can build on those things over time.
|
| For example, would AI with 1850 data have figured out the
| idea of lift to make an airplane and taught us how to
| make working flying machines and progress them to the
| jets we have today, or something better? It wouldn't even
| be starting from 0, so this would be a generous example,
| as da Vinci way playing with these ideas in the 15th
| century.
|
| If it can't do it, or what it produces is worse than what
| humans have done, we shouldn't leave it to AI alone to
| invent our actual future. Which would mean reevaluating
| the role these "thought leaders" say it will play, and
| how we're educating and communicating about AI to the
| younger generations.
| armcat wrote:
| I think this would be an awesome experiment. However you would
| effectively need to train something of a GPT-5.2 equivalent. So
| you need lot of text, a much larger parameterization (compared
| to nanoGPT and Phi-1.5), and the 1800s equivalents of
| supervised finetuning and reinforcement learning with human
| feedback.
| Trufa wrote:
| This is fascinating, but the experiment seems to fail in being
| a fair comparison of how much knowledge can we have from that
| time in data vs now.
|
| As a thought experiment I find it thrilling.
| Rebuff5007 wrote:
| OF COURSE!
|
| The fact that tech leaders espouse the brilliance of LLMs and
| don't use this specific test method is infuriating to me. It is
| deeply unfortunate that there is little transparency or
| standardization of the datasets available for training/fine
| tuning.
|
| Having this be advertised will make more interesting and
| informative benchmarks. OEM models that are always "breaking"
| the benchmarks are doing so with improved datasets as well as
| improved methods. Without holding the datasets fixed, progress
| on benchmarks are very suspect IMO.
| dexwiz wrote:
| This would be a true test of can LLMs innovate or just
| regurgitate. I think part of people's amazement of LLMs is they
| don't realize how much they don't know. So thinking and
| recalling look the same to the end user.
| water-data-dude wrote:
| It'd be difficult to prove that you hadn't leaked information
| to the model. The big gotcha of LLMs is that you train them on
| BIG corpuses of data, which means it's hard to say "X isn't in
| this corpus", or "this corpus only contains Y". You could TRY
| to assemble a set of training data that only contains text from
| before a certain date, but it'd be tricky as heck to be SURE
| about it.
|
| Ways data might leak to the model that come to mind:
| misfiled/mislabled documents, footnotes, annotations, document
| metadata.
| gwern wrote:
| There's also severe selection effects: what documents have
| been preserved, printed, and scanned _because_ they turned
| out to be on the right track towards relativity?
| mxfh wrote:
| This.
|
| Especially for London there is a huge chunk of recorded
| parliament debates.
|
| More interesting for dialoge seems training on recorded
| correspondence in form of letters anyway.
|
| And that corpus script just looks odd to say the least,
| just oversample by X?
| water-data-dude wrote:
| Oh! I honestly didn't think about that, but that's a very
| good point!
| reassess_blind wrote:
| Just Ctrl+F the data. /s
| nickpsecurity wrote:
| That is one of the reasons I want it done. We cant tell if AI's
| are parroting training data without having the whole, training
| data. Making it old means specific things won't be in it (or
| will be). We can do more meaningful experiments.
| abhishekjha wrote:
| Oh I have really been thinking long about this. The intelligence
| that we have in these models represent a time.
|
| Now if I train a foundation models with docs from library of
| Alexandria and only those texts of that period, I would have a
| chance to get a rudimentary insight on what the world was like at
| that time.
|
| And maybe time shift further more.
| feisty0630 wrote:
| > I would have a chance to get a rudimentary insight on what
| the world was like at that time
|
| Congratulations, you've reinvented the history book (just with
| more energy consumption and less guarantee of accuracy)
| gordonhart wrote:
| History books, especially those from classical antiquity, are
| notoriously not guaranteed to be accurate either.
| feisty0630 wrote:
| Do you expect something exclusively trained on them to be
| any better?
| gordonhart wrote:
| To a large extent, yes. A model trained on many different
| accounts of an event is likely going to give a more
| faithful picture of that event than any one author.
|
| This isn't super relevant to us because very few
| histories from this era survived, but presumably there
| was sufficient material in the Library of Alexandria to
| cover events from multiple angles and "zero out" the
| different personal/political/religious biases coloring
| the individual accounts.
| aqme28 wrote:
| This kind of technique seems like a good way to test model
| performance against benchmarks. I'm too skeptical that new models
| are taking popular benchmark solutions into their training data.
| So-- how does e.g. ChatGPT's underlying architecture perform on
| SWE-bench if trained only on data prior to 2024.
| NitpickLawyer wrote:
| > are taking popular benchmark solutions into their training
| data
|
| That happened in the past, and the "naive" way of doing it is
| usually easy to spot. There are, however, many ways in which
| testing data can leak into models, even without data
| contamination. However this doesn't matter much, as any model
| that only does well in benchmarks but is bad in real-world
| usage will be quickly sussed out by people actually using them.
| There are also lots and lots of weird, not very popular
| benchmarks out there, and the outliers are quickly identified.
|
| > perform on SWE-bench if trained only on data prior to 2024.
|
| There's a benchmark called swe-REbench, that takes issues from
| real-world repos, published ~ monthly. They perform tests and
| you can select the period and check their performance. This is
| fool-proof for open models, but a bit unknown for API-based
| models.
| zingababba wrote:
| LOL
|
| PROMPT:Charles Darwin
|
| Charles DarwinECCEMACY. Sir, -- The following case is interesting
| to me : -- I was in London a fortnight, and was much affected
| with an attack of rheumatism. The first attack of rheumatism was
| a week before I saw you, and the second when I saw you, and the
| third when I saw you, and the third in the same time. The second
| attack of gout, however, was not accompanied by any febrile
| symptoms, but was accompanied with an increased flow of urine,
| and with a more copious discharge of urine. The third attack was
| an hour after I saw you, and was succeeded by a return of a
| paroxysm of gout, and a more rapid return of the gout. The fourth
| attack was also accompanied by a fever, but was not always
| accompanied by any febrile symptoms. The third attack of gout was
| a fortnight after you had been ill, and the fourth was followed
| by a paroxysm of gout. The fourth attack was a fortnight after
| you were attacked, and was accompanied by a sense
| alansaber wrote:
| Average small language model response
| feisty0630 wrote:
| Interesting that it reads a bit like it came from a Markov
| chain rather than an LLM. Perhaps limited training data?
| kgeist wrote:
| Early LLMs used to have this often. I think's that where the
| "repetition penalty" parameter comes from. I suspect output
| quality can be improved with better sampling parameters.
| nomel wrote:
| It is lacking all recorded text from the past 200 years. ;)
|
| It would be interesting to know how much text was generated
| per century!
| myrmidon wrote:
| There was a discussion around a very similar model (Qwen3 based)
| some weeks ago:
|
| https://news.ycombinator.com/item?id=46319826
|
| I found it particularly thought-inspiring how a model with
| training from that time period completely lacks
| context/understanding of what it is _itself_ , but then I
| realized that we are the same (at least for now).
| marmalade2413 wrote:
| Can you confidently say that the architure of the LLM doesn't
| include any a priori bias that might effect the integrity of this
| LLM?
|
| That is, the architectures of today are chosen to yield the best
| results given the textual data around today and the problems we
| want to solve today.
|
| I'd argue that this lack of bias would need to be researched (if
| it hasn't been already) before this kind of model has credence.
|
| LLMs aren't my area of expertise but during my PhD we were able
| to encode a lot of a priori knowledge through the design of
| neural network architectures.
| dlcarrier wrote:
| It's interesting that it's trained off only historic text.
|
| Back in the pre-LLM days, someone trained a Markov chain off the
| King James Bible and a programming book:
| https://www.tumblr.com/kingjamesprogramming
|
| I'd love to see an LLM equivalent, but I don't think that's
| enough data to train from scratch. Could a LoRA or similar be
| used in a way to get speech style to strictly follow a few
| megabytes worth of training data?
| _blk wrote:
| Yup that'd be very interesting. Notably missing from this
| project's list is the KJV (1611 was in use at the time.) The
| first random newspaper that I pulled up from a search for
| "london newspaper 1950" has sermon references on the front page
| so it seems like an important missing piece.
|
| Somewhat missing the cutoff of 1875 is the revised NT of the
| KJV. Work on it started in 1870 but likely wasn't used widely
| before 1881.
| userbinator wrote:
| That was far more amusing than I thought it'd be. Now we can
| feed those into an AI image generator to create some "art".
| jimmytucson wrote:
| Fascinating idea. There was another "time-locked" LLM project
| that popped up on HN recently[1]. Their model output is really
| polished but the team is trying to figure out how to avoid abuse
| and misrepresentation of their goals. We think it would be cool
| to talk to someone from 100+ years ago but haven't seriously
| considered the many ways in which it would be uncool. Interesting
| times!
|
| [1] https://news.ycombinator.com/item?id=46319826
| addaon wrote:
| Suppose two models with similar parameters trained the same way
| on 1800-1875 and 1800-2025 data. Running both models, we get
| probability distributions across tokens, let's call the
| distributions 1875' and 2025'. We also get a probability
| distribution finite difference (2025' - 1875'). What would we get
| if we sampled from 1.1*(2025' - 1875') + 1875'? I don't think
| this would actually be a decent approximation of 2040', but it
| would be a fun experiment to see. (Interpolation rather than
| extrapolation seems just as unlikely to be useful and less likely
| to be amusing, but what do I know.)
| pvab3 wrote:
| What if it's just genAlpha slang?
| andai wrote:
| The real mode collapse ;)
| sigmoid10 wrote:
| These probability shifts would only account for the final
| output layer (which may also have some shift), but I expect the
| largest shift to be in the activations in the intermediate
| latent space. There are a bunch of papers out there that try to
| get some offset vector using PCA or similar to tune certain
| model behaviours like vulgarity or friendlyness. You don't even
| need much data for this as long as your examples capture the
| essence of the difference well. I'm pretty certain you could do
| this with "historicalness" too, but projecting it into the
| future by turning the "contemporaryness" knob way up probably
| won't yield an accurate result. There are too many outside
| influences on language that won't be captured in historical
| trends.
| lopuhin wrote:
| On whether this accounts only the final output layer -- once
| the first token is generated (i.e. selected according to the
| modified sampling procedure), and assuming a different token
| is selected compared to standard sampling, then all layers of
| the model would be affected during generation of subsequent
| tokens.
| hallvard wrote:
| Cool! I also did something like this:
| https://github.com/hallvardnmbu/transformer
|
| But on various data (i.e., separate model per source): the Bible,
| Don Quixote and Franz Kafka. (As well as a (bad!) lyrics
| generator, and translator.)
| InvisibleUp wrote:
| If the output of this is even somewhat coherent, it would
| disprove the argument that mass amounts of copyrighted works are
| required to train an LLM. Unfortunately that does not appear to
| be the case here.
| HighFreqAsuka wrote:
| Take a look at The Common Pile v0.1: An 8TB Dataset of Public
| Domain and Openly Licensed Text
| (https://arxiv.org/pdf/2506.05209). They build a reasonable 7B
| parameter model using only open-licensed data.
| nickpsecurity wrote:
| They mostly do that. They risked legal contamination by using
| Whisper-derived text and web text which might have gotchas.
| Other than that, it was a great collection for low-risk
| training.
| dash2 wrote:
| Mm. I'm a bit sceptical of the historical expertise of someone
| who thinks that "Who art Henry" is 19th century language. (It's
| not actually grammatically correct English from any century
| whatever: "art" is the second person singular, so this is like
| saying "who are Henry?")
| joshuakoehler wrote:
| As a reader of a lot of 17th, 18th, and 19th century Christian
| books, this was my thought exactly.
| evolve2k wrote:
| That text was from v0, the responses improved from there.
| freedomben wrote:
| That text was from the example prompt, not from the models
| response
| joshuakoehler wrote:
| Right, assuming the OP had good data, then this likely
| wouldn't affect much, what he built is still really
| interesting.
| haensi wrote:
| What kind of Christian books do you read?Jonathan Edwards,
| John Bunyan, J.C. Ryle, C.H. Spurgeon?
| joshuakoehler wrote:
| Yes, I've read the History of Redemption by Edwards, The
| Pilgrim's Progress and Holy War by Bunyan, quite a few
| Spurgeon sermons, and Holiness by Ryle in addition to
| (parts of) his commentaries on the gospels. I also read the
| puritans - I read Thomas Brook's Precious Remedies Against
| Satan's Devices and the Body of Divinity (Thomas Watson)
| last year.
|
| Lately I've read a few older biographies/autobiographies -
| Thomas Scott's autobiography (The Force of Truth),
| Halyburton's autobiography, and James Henley Thornwell and
| Benjamin Morgan Palmer biographies.
|
| Right now I'm reading the Life and Times of Jesus Messiah
| by Alfred Edersheim (19th century).
|
| How about you?
| auraham wrote:
| Can you elaborate on this? After skimming the README, I
| understand that "Who art Henry" is the prompt. What should be
| the correct 19th century prompt?
| canjobear wrote:
| "Who art Henry?" was never grammatical English. "Art" was the
| second person singular present form of "to be" and it was
| already archaic by the 17th century. "Who is Henry?" would be
| fine.
| andai wrote:
| Who art thou?
|
| (Well, not 19th century...)
| geocar wrote:
| The problem is the subjunctive mood of the word "art".
|
| "Art thou" should be translated into modern English as "are
| you to be", and so works better with things (what are you
| going to be), or people who are alive, and have a future
| (who are you going to be?).
|
| Those are probably the contexts you are thinking of.
| andai wrote:
| Wherefore are you going to be Romeo?
| efreak wrote:
| Wherefore is closer to _why_ , or (as taught in
| literature classes) _for what reason_. _Wherefore_ is a
| question, _therefore_ is an answer.
| vintermann wrote:
| "Who is Henry?"
| ourmandave wrote:
| Can I use it to get up-to-date legal advice on Arizona
| reproductive health laws?
| krunck wrote:
| Training LLMs on data with certain date cut-offs and then doing
| comparative analysis between the LLMs would be interesting.
| radiothomp wrote:
| A LLM trained only on data from certain time periods to ~reduce
| modern bias~ enhance past bias
| SV_BubbleTime wrote:
| Doesn't that seem useful though? Isn't that why I'm forced to
| read _"This movie was made a time when racial stereotypes were
| different and not well considered"_ or whatever on old movies?
|
| I think talking to a legit trained LLM from a different era
| would be rad. But... this seems the opposite of Gemini making
| black lady popes and Native American Nazis... that these views
| wouldn't really be "allowed" (published by anyone that wants AI
| funding money).
| sl_convertible wrote:
| Harry Seldon would, no doubt, find this fascinating. Imagine
| having a sliding-window LLM that you could use to verify a
| statistical model of society. I wonder what patterns it could
| deduce?
| tonymet wrote:
| the "1917 model" from a few weeks back post-trained the model
| with ChatGPT dialog. So it had modern dialect and proclivities .
|
| A truly authentic historical model will have some unsavory
| opinions and very distinctive dialect.
| patcon wrote:
| > OCR noise ("Digitized by Google") still present in outputs
|
| This feels like a neat sci-fi short story hook to explain the
| continuous emergence of God as an artifact of a simulation
| fluoridation wrote:
| I'm reminded of SD models that put vaguely-shaped Patreon logos
| in the corner.
| tgtweak wrote:
| Very interesting but the slight issue I see here is one of data:
| the information that is recorded and in the training data here is
| heavily skewed to those intelligent/recognized enough to have
| recorded it and had it preserved - much less than the current
| status quo of "everyone can trivially document their thoughts and
| life" diorama of information we have today to train LLMs on. I
| suspect that a frontier model today would have 50+TB of training
| data in the form of text alone - and that's several orders of
| magnitude more information and from a much more diverse point of
| view than what would have survived from that period. The output
| from that question "what happened in 1834" read like a
| newspaper/bulletin which is likely a huge part of the data that
| was digitized (newspapers etc).
|
| Very cool concept though, but it definitely has some bias.
| notarobot123 wrote:
| Biases exposed through artificial constraints help to make
| visible the hidden/obscured/forgotten biases of state-of-the-
| art systems.
| twosdai wrote:
| > but it definitely has some bias.
|
| to be frank though, I think this a better way than all people's
| thoughts all of the time.
|
| I think the "crowd" of information makes the end output of an
| LLM worse rather than better. Specifically in our inability to
| know really what kind of Bias we're dealing with.
|
| Currently to me it feels really muddy knowing how information
| is biased, beyond just the hallucination and factual
| incosistencies.
|
| But as far as I can tell, "correctness of the content aside",
| sometimes frontier LLMs respond like freshman college students,
| other times they respond with the rigor of a mathematics PHD
| canidate, and sometimes like a marketing hit piece.
|
| This dataset has a consistency which I think is actually a
| really useful feature. I agree that having many perspectives in
| the dataset is good, but as an end user being able to rely on
| some level of consistency with an AI model is something I
| really think is missing.
|
| Maybe more succinctly I want frontier LLM's to have a known and
| specific response style and bias which I can rely on, because
| there already is a lot of noise.
| nickpsecurity wrote:
| Models today will be biased based on what's in their training
| data. If English, it will be biased heavily toward Western,
| post-1990's views. Then, they do alignment training that forces
| them to speak according to the supplier's morals. That was
| Progressive, atheist, evolutionist, and CRT when I used them
| years ago.
|
| So, the OP model will accidentally reflect the biases of the
| time. The current, commercial models intentionally reflect
| specific biases. Except for uncensored models which
| accidentally have those in the training data modified by
| uncensoring set.
| cowlby wrote:
| I wonder if you could train an LLM with everything up to
| Einstein. Then see if with thought experiments + mathematics you
| could arrive at general relativity.
| erenkaradag wrote:
| The problem is that the 'genius' of Einstein wasn't just
| synthesizing existing data,but actively rejecting the axioms of
| that data. The 1875 corpus overwhelmingly 'proves' absolute
| time and the luminiferous aether. A model optimizing for the
| most probable continuation will converge on that consensus.
|
| To get Relativity, the model needs to realize the training data
| isn't just incomplete, but fundamentally wrong. That requires
| abductive reasoning (the spark of genius) to jump out of the
| local minimum. Without that AGI-level spark, a 'pure knowledge
| pile' will just generate a very eloquent, mathematically
| rigorous defense of Newtonian physics.
| chc4 wrote:
| I think it would be very cute to train a model exclusively in
| pre-information age documents, and then try to teach it what a
| computer is and get it to write some programs. That said, this
| doesn't look like it's nearly there yet, with the output looking
| closer to Markov chain than ChatGPT quality.
| escapecharacter wrote:
| I would pay like $200/month if there was an LLM out there that I
| could only communicate with using an old-timey telegraph key and
| morse code.
| radarsat1 wrote:
| Heh, at least this wouldn't spread emojis all over my readmes.
| Hm, come to think of it I wonder how much tokenization is
| affected.
|
| Another thought, just occurred when thinking about readmes and
| coding LLMs: obviously this model wouldn't have any coding
| knowledge, but I wonder if it could be possible to combine this
| somehow with a modern LLM in such a way that it _does_ have
| coding knowledge, but it renders out all the text in the style /
| knowledge level of the 1800's model.
|
| Offhand I can't think of a non-fine-tuning trick that would
| achieve this. I'm thinking back to how the old style transfer
| models used to work, where they would swap layers between models
| to get different stylistic effects applied. I don't know if
| that's doable with an LLM.
| fluoridation wrote:
| Just have the models converse with each other?
| Aperocky wrote:
| Looks a lot like the output from a markov chain...
| chuckadams wrote:
| Think I'll ask it to come up with some jacquard loom patterns.
| vibe-weaving.
| CGMthrowaway wrote:
| Is there a link where I can try it out?
|
| Edit: I figured it out
|
| "The Lord of the Rings _uding the army under the command of his
| brother, the Duke of York, and the Duke of Richmond, who fell in
| the battle on the 7th of April, 1794. The Duke of Ormond had been
| appointed to the command of the siege of St. Mark 's, and had
| received the victory of the Rings, and was thus commanded to move
| with his army to the relief of Shenham. The Duke of Ormond was at
| length despatched to oppose them, and the Duke of Ormond was
| ordered_
| shmeeed wrote:
| Meanwhile, top comments are having visions about the emergence
| of Einstein-level insights.
|
| There's a disconnect somewhere that I can't quite put my finger
| on. Am I just lacking imagination?
| aussieguy1234 wrote:
| Let's see how someone from the past reacts when you tell them
| about modern technology
| argestes wrote:
| I wonder how racist it is
| linolevan wrote:
| I'm wondering in what ways is this similar/different to
| https://github.com/DGoettlich/history-llms?
|
| I saw TimeCapsuleLLM a few months ago, and I'm a big fan of the
| concept but I feel like the execution really isn't that great. I
| wish you:
|
| - Released the full, actual dataset (untokenized, why did you
| pretokenize the small dataset release?)
|
| - Created a reproducible run script so I can try it out myself
|
| - Actually did data curation to remove artifacts in your dataset
|
| - Post-trained the model so it could have some amount of chat-
| ability
|
| - Released a web demo so that we could try it out (the model is
| tiny! Easily can run in the web browser without a server)
|
| I may sit down and roll a better iteration myself.
| 1313ed01 wrote:
| I guess chat-ability would require some chat-like data, so
| would that mean first coming up with a way to extract chat-like
| dialogue from the era and then use that to fine-tune the model?
| Sophira wrote:
| I've felt for a while that having LLMs that could answer from a
| previous era would be amazing. I posted an open letter to OpenAI
| on Reddit about this:
| https://www.reddit.com/r/ChatGPT/comments/zvm768/open_letter... .
|
| I still think it's super important. Archive your current models -
| they'll be great in the future.
| akg130522 wrote:
| HN titles are too techy
| wolvoleo wrote:
| I wonder how representative this is of life in those days. Most
| written communication was official back then. Books, newspapers.
| Plays. All very formal and staged. There's not much real life
| interaction between common people in that. In fact I would
| imagine a lot of people were illiterate.
|
| With the internet and pervasive text communication and audio
| video recording we have the unique ability to make an LLM mimic
| daily life but I doubt that would be possible for those days.
| mock-possum wrote:
| Fun idea, but all of the output they demo over the course of the
| various versions is unusable. You can see progress clearly being
| made though - maybe v3 will pass muster.
| 40four wrote:
| I'm sure I'm not the only one, but it seriously bothers me, the
| high ranking discussion and comments under this post about
| whether or not a model trained on data from this time period (or
| any other constrained period) could synthesize it and postulate
| "new" scientific ideas that we now accept as true in the future.
| The answer is a resounding "no". Sorry for being so blunt, but
| that is the answer that is a consensus among experts, and you
| will come to the same answer after a relatively small mount of
| focus & critical thinking on the issue of how LLMs & other
| categories of "AI" work.
| friendzis wrote:
| I understand where you are coming from, but not every field is
| hard science. In many fields we deal with some amount of
| randomness and attribute causality to correlations even if we
| do not have as much as a speculative hypothesis for a mechanism
| of action behind the supposed causality.
|
| LLMs trained on data up to a strictly constrained point are our
| best vehicle to have a view (however biased) on something,
| detached from its origins and escape a local minima. The
| speculation is that such LLMs could help us look at
| correlational links accepted as truths and help us devise an
| alternative experimental path or craft arguments for such
| experiments.
|
| Imagine you have an LLM trained on papers up to some threshold,
| feed your manuscript with correlational evidence and have an
| LLM point out uncontrolled confounders or something like that.
| hare2eternity wrote:
| Outside of science it would be an interesting pedagogic tool
| for many people. There is a tendency to imagine that people
| in the past saw the world much the same as we do. The
| expression "the past is a foreign country" resonates because
| we can empathise at some level that things were different,
| but we can't visit that country. "Talking" to a denizen of
| London in 1910 regarding world affairs, gender equality,
| economic opportunities, etc would be very interesting. Even
| if it can never be entirely accurate I think it would be
| enlightening.
| nomel wrote:
| I think the question is more about the concept, rather than the
| specific LLM architectures of today.
| PxldLtd wrote:
| I'm sorry but this is factually incorrect and I'm not sure what
| experts you are referring to here about there being concensus
| on this topic. I would love know. Geoffrey Hinton, Demis
| Hassabis, and Yann LeCun all heavily disagree with what you
| claim.
|
| I think you might be confusing creation ex nihilo with
| combinatorial synthesis which LLMs excel at. The proposed
| scenario is a fantastic testcase for exactly this. This doesn't
| cover verification of course but that's not the question here.
| The question is wether an already known valid postulate can be
| synthesized.
| mexicocitinluez wrote:
| > but that is the answer that is a consensus among experts
|
| Do you have any resources that back up such a big claim?
|
| > relatively small mount of focus & critical thinking on the
| issue of how LLMs & other categories of "AI" work.
|
| I don't understand this line of thought. Why wouldn't the
| ability to recognize patterns in existing literature or
| scientific publications result in potential new understandings?
| What critical thinking am I not doing?
|
| > postulate "new" scientific ideas
|
| What are you examples of "new" ideas that aren't based on
| existing ones?
|
| When you say "other categories of AI", you're not including
| AlphaFold, are you?
| saberience wrote:
| > The answer is a resounding "no".
|
| This is your assertion made without any supportive data or
| sources. It's nice to know your subjective opinion on the issue
| but your voice doesn't hold much weight making such a bold
| assertion devoid of any evidence/data.
| roywiggins wrote:
| I think it's pretty likely the answer is no, but the idea here
| is that you could actually _test_ that assertion. I 'm also
| pessimistic about it but that doesn't mean it wouldn't be a
| little interesting to try.
| harvie wrote:
| So basically a LLM from that brief time period back when
| communism felt like a good idea? what can go wrong? :-)
| truxton wrote:
| The year is 1875 and Sir Almroth Wrigh was born on August 10,
| 1861, he would have turned 14 in August of 1875 and your mission
| is to discover something we now call antibiotics before a
| historical event we now call the Spanish Flu and make him aware
| of a few details. Focus specifically on everything that was known
| about Sir Almroth Wright, and his work in Leipzig, Cambridge,
| Sydney, and London. If there was a world war what might chemical
| warfare look like, what could we have done to prevent it.
|
| The model that could come up with the cure based on the limited
| data of the time wouldn't just impress, it would demonstrate
| genuine emergent reasoning beyond pattern matching. The challenge
| isn't recombining existing knowledge (which LLMs excel at), but
| making conceptual leaps that require something else. Food for
| thought.
| albertzeyer wrote:
| v0: 16M Parameters
|
| v0.5 123M Parameters
|
| v1: 700M Parameters
|
| v2mini-eval1: 300M Parameters
|
| I would not call this LLM. This is not large. It's just a normal-
| sized LM. Or even small.
|
| (It's also not a small LLM.)
| efreak wrote:
| GPT2 at 774m is considered a LLM. I wouldn't say there's much
| difference between that and 700m, or even 123M.
|
| Having said that, looking up small language model these days
| returns tons of results calling 7B models small language
| models.
|
| ------
|
| My understanding of small language models is that they're
| generally intended for specific purposes, like analysis and
| classification (whatever you'd call the text equivalent of
| image interrogation with clip models), translation, etc; that
| there small because they don't _need_ to be big to do their
| intended functions, not because they 're just smaller versions
| of bigger models.
| digikata wrote:
| A fun use of this kind of approach would be to see if
| conversational game NPCs could be generated that stick the the
| lore of the game and their character.
| snickerbockers wrote:
| This one's going to have some wild political takes.
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