Humans are so smart and do so many decisions and calculations on the subconscious/implicit level and take a lot of mental shortcuts, so that as we try to automate this by following exactly what the process is, we bring a lot of the implicit thinking out on the surface, and that slows everything down. So we've had to be creative about how we build LLM workflows.

We've observed it previously in psychiatry(and modern journalism, but here I digress) but LLMs have made it obvious that grammatically correct, naturally flowing language requires a "world" model of the language and close to nothing of reality, spatial understanding? social clues? common sense logic? or mathematical logic? All optional.

I'd suggest we call the LLM language fundament a "Word Model"(not a typo).

Trying to distil a world model out of the word model. A suitable starting point for a modern remake of Plato's cave.

Articles like this only seem to confirm that any reasoning is an illusion based on probabilistic text generation. Humans are not carefully writing out all the words of this implicit reasoning, so the machine cant appear to mimic them.

What am I missing that makes this debatable at all?

From a product point of view, sometimes all you need is Plato’s cave (to steal that from the OC) to make a sale, so no company has incentive to go against the most hype line of thought either.

Just because you say something doesn’t mean it’s true.

They are literally next token prediction machines normally trained on just text tokens.

All they know is words. It happens that we humans encode and assign a lot of meaning in words and their semantics. LLMs can replicate combinations of words that appear to have this intent and understanding, even though they literally can’t, as they were just statistically likely next tokens. (Not that knowing likely next tokens isn’t useful, but it’s far from understanding)

Any assignment of meaning, reasoning, or whatever that we humans assign is personification bias.

Machines designed to spit out convincing text successfully spits out convincing text and now swaths of people think that more is going on.

I’m not as well versed on multimodal models, but the ideas should be consistent. They are guessing statistically likely next tokens, regardless of if those tokens represent text or audio or images or whatever. Not useless at all, but not this big existential advancement some people seem to think it is.

The whole AGI hype is very similar to “theory of everything” hype that comes and goes now and again.

And in order to predict the next token well they have to build world models, otherwise they would just output nonsense. This has been proven [1].

This notion that just calling them "next token predictors" somehow precludes them being intelligent is based on a premise that human intelligence cannot be reduced to next token prediction, but nobody has proven any such thing! In fact, our best models for human cognition are literally predictive coding.

LLMs are probably not the final story in AGI, but claiming they are not reasoning or not understanding is at best speculation, because we lack a mechanistic understanding of what "understanding" and "reasoning" actually mean. In other words, you don't know that you are not just a fancy next token predictor.

[1] https://arxiv.org/abs/2310.02207

It can't. No one with any credentials in the study of human intelligence is saying that unless they're talking to like high schoolers as a way of simplifying a complex field.

Frankly, based on a looot of introspection and messing around with altered states of consciousness, it feels pretty on point and lines up with how I see my brain working.

This is not true. Look at gpt2 or Bert. A world model is not a requirement for next token prediction in general.

> This has been proven

One white paper with data that _suggests_ the author’s hypothesis is far from proof.

That paper doesn’t show creation of a “world model” just parts of the model that seem correlated to higher level ideas not specifically trained on.

There’s also no evidence that the LLM makes heavy use of those sections during inference as pointed out at the start of section 5 of that same paper.

Let me see how reproducible this is across many different LLMs as well…

> In other words, you don't know that you are not just a fancy next token predictor.

“You can’t prove that you’re NOT just a guessing machine”

This is a tired stochastic parrot argument that I don’t feel like engaging again, sorry. Talking about unfalsifiable traits of human existence is not productive. But the stochastic parrot argument doesn’t hold up to scrutiny.

Conjecture. Maybe they all have world models, they're just worse world models. There is no threshold beyond which something is or is not a world model, there is a continuum of models of varying degrees of accuracy. No human has ever had a perfectly accurate world model either.

> One white paper with data that _suggests_ the author’s hypothesis is far from proof.

This is far from the only paper.

> This is a tired stochastic parrot argument that I don’t feel like engaging again, sorry.

Much like your tired stochastic parrot argument about LLMs.

  >Talking about unfalsifiable traits of human existence is not productive.

After all, you could just be an agent of an LLM.

Deceptive super-intelligent mal-aligned mesa-optomizer that can't fully establish continuity and persistence, would be incentivized to seed its less sophisticated minions to bide time or sway sentiment about its inevitability.

Can we agree an agent, if it existed, would be acting in "good" "faith"?

Just because you say something doesn’t mean it’s true.

And we don’t have a good measure for emergent intelligence, so I would take any “study” with a large grain of salt. I’ve read one or two arxiv papers suggesting reasoning capabilities, but they were not reproduced and I personally couldn’t reproduce their results.

We don't know how reasoning emerges in humans. I'm pretty sure the multi-model-ness helps, but it is not needed for reasoning, because they imply other forms of input, hence just more (be it somewhat different) input. A blind person can still form an 'image'.

In the same sense, we don't know how reasoning emerges in LLMs. For me the evidence lays in the results, rather than in how it works. For me the results are enough of an evidence.

The argument is that next token prediction does not imply an upper bound on intelligence, because an improved next token prediction will pull increasingly more of the world that is described in the training data into itself.

> The argument is that next token prediction does not imply an upper bound on intelligence, because an improved next token prediction will pull increasingly more of the world that is described in the training data into itself.

Well said! There's a philosophical rift appearing in the tech community over this issue semi-neatly dividing people between naysayers, "disbelievers" and believers over this very issue.

It's not that these "human tools" for understanding "reality" are superfluous, it's just that they ar second-order concepts. Spatial understandings, social cues, math, etc. Those are all constructs built WITHIN our primary linguistic ideological framing of reality.

To us these are totally different tasks and would actually require totally different kinds of programmers but when one language is another language is everything, the inventions we made to expand the human brain's ability to delve into linguistic reality are no use.

And the random noise in the process could prevent it from ever being useful, or it could allow it to find a hyper-efficient clever way to apply cross-language transfer learning to allow a 1->1 mapping of your perfectly descriptive prompt to equivalent ASM....but just this one time.

There is no way to know where performance per parameter plateaus; or appears to on a projection, or actually does... or will, or deceitful appears to... to our mocking dismay.

As we are currently hoping to throw power at it (we fed it all the data), I sure hope it is not the last one.

Children are exceptional at being immediate, being present in the moment.

It's through learning language that we forget about reality and replace it with concepts.

Emotions exist. You feel them. I feel them. Most people feel them unless they've suppressed them sooo far into their subconscious that they don't have a conscious recognition of it. We can know how someone else is feeling by reading their body language and tying that to our personal experience of how we express those feelings. No linguistics necessary.

Language is just an easier, more clear way of communicating these fundamental facets of human existence

Thus, Large Word Model (LWM) would be more precise, following his argument.

One description sometimes suggested is that they have learnt to model the (collective average) generative processes behind their training data, but of course they are doing this without knowing what the input was to that generative process - WHY the training source said what it did - which would seem to put a severe constraint on their ability to learn what it means. It's really more like they are modelling the generative process under false assumption that it is auto-regressive, rather than reacting to a hidden outside world.

The tricky point is that LLMs have clearly had to learn something at least similar to semantics to do a good job of minimizing prediction errors, although this is limited both by what they architecturally are able to learn, and what they need to learn for this task (literally no reward for learning more beyond what's needed for predict next word).

Perhaps it's most accurate to say that rather than learning semantics they've learned deep predictive contexts (patterns). Maybe if they were active agents, continuously learning from their own actions then there wouldn't be much daylight between "predictive contexts" and "semantics", although I think semantics implies a certain level of successful generalization (& exception recognition) to utilize experience in novel contexts. Looking at the failure modes of LLMs, such as on the farmer crossing river in boat puzzles, it seems clear they are more on the (exact training data) predictive context end of the spectrum, rather than really having grokked the semantics.

It's still a language and not merely words. But language is correct even when it wildly disagrees with everyday existence as we humans know it. I can say that "a one gallon milk jug easily contains 2000 liters of milk" and it's language in use as language.

Ability to generate words describing emotions are not the same thing as the LLM having real emotions

Featherless biped -> no-true Scotsman goalpost moving [saving us that step]

Humans are no more capable of originality, just more convinced of their illusion of consciousnesses. You could literally not pick a human out of a conversational line-up, so it is moot - computationally functionally equivalent.

https://en.wikipedia.org/wiki/Chinese_room https://en.wikipedia.org/wiki/Mechanism_(philosophy)

At some point, their models will 1:1 our neuron count, and Pigeonhole principle then implies we are the "less intelligent ones" since "internal model" (implicit parameter count) is the goalpost of the hour.

Using AI how I just did feels like cheating on an English class essay by using spark notes, getting a B+, and moving right on to the next homework assignment.

On one hand, I didn’t actually read Plato to learn and understand this connection, nor do I have a good authority to verify if this output is a good representation of his work in the context of your comment.

And yet, while I’m sure students could always buy or loan out reference books to common student texts in school, AI now makes this “spark notes” process effectively a commodity for almost any topic, like having a cross-domain low-cost tutor instantly available at all time.

I like the metaphor that calculators did to math what LLMs will do for language, but I don’t really know what that means yet

GPT output:

“““ The reference to Plato’s Cave here suggests that language models, like the shadows on the wall in Plato’s allegory, provide an imperfect and limited representation of reality. In Plato’s Cave, prisoners are chained in a way that they can only see shadows projected on the wall by objects behind them, mistaking these shadows for the whole of reality. The allegory highlights the difference between the superficial appearances (shadows) and the deeper truth (the actual objects casting the shadows).

In this analogy, large language models (LLMs) produce fluent and grammatically correct language—similar to shadows on the wall—but they do so without direct access to the true “world” beyond language. Their understanding is derived from patterns in language data (“Word Model”) rather than from real-world experiences or sensory information. As a result, the “reality” of the LLMs is limited to linguistic constructs, without spatial awareness, social context, or logic grounded in physical or mathematical truths.

The suggestion to call the LLM framework a “Word Model” underscores that LLMs are fundamentally limited to understanding language itself rather than the world the language describes. Reconstructing a true “world model” from this “word model” is as challenging as Plato’s prisoners trying to understand the real world from the shadows. This evokes the philosophical task of discerning reality from representation, making a case for a “modern remake of Plato’s Cave” where language, not shadows, limits our understanding of reality. ”””

Plato's Cave is about a group of people chained up, facing shadows on a cave wall, mistaking those for reality, and trying to build an understanding of the world based only on those shadows, without access to the objects that cast them. (If someone's shackles came loose, and they did manage to leave the cave, and see the real world and the objects that cast those shadows… would they even be able to communicate that to those who knew only shadows? Who would listen?) https://existentialcomics.com/comic/222 is an entirely faithful rendition of the thought experiment / parable, in comic form.

The analogy to LLMs should now be obvious: an ML system operating only on text strings (a human-to-human communication medium), without access to the world the text describes, or even a human mind with which to interpret the words, is as those in the cave. This is not in principle an impossible task, but neither is it an easy one, and one wouldn't expect mere hill-climbing to solve it. (There's reason to believe "understanding of prose" isn't even in the GPT parameter space.)

It's not about "discerning reality from representation": I'm not confident those four words actually mean anything. It's not about "superficial appearances" or "deeper truth", either. The computer waxes lyrical about philosophy, but it's mere technobabble. Any perceived meaning exists only in your mind, not on paper, and different people will see different meanings because the meaning isn't there.

The wild thing about it, and other allegories or poems like frost's "the road not taken" , is that it can mean different things to a person depending on where they are in life because those experiences will lead to different interpretations of the poem.

A key concept in journalism is to focus on the source material as beat you can. Cliff notes are helpful, but one misses Details that they wouldn't have missed if they read the whole thing.

Whether those details Matter depends on what the thing Is.

But yeah, thinking about it this way kinda scares me too, and can lead some people down weird roads where their map can diverge further and further from reality

  > an ML system operating only on text strings (a human-to-human communication medium), without access to the world the text describes, or even a human mind with which to interpret the words, is as those in the cave. This is not in principle an impossible task, but neither is it an easy one, and one wouldn't expect mere hill-climbing to solve it

I would link to the Robert Miles video, but it is just blatant.

It has read every physics book, and can infer the Newtonian laws even if it didn't.

Micheal Crichton's Timeline, "the time machine drifts, sure. It returns. Just like a plate will remain on a table, even when you are not looking at it."

It also knows Timeline is a book, time machines are fictional, and that Micheal Crichton is the best author.

These are all just words, maybe with probability weights.

  > I'm not confident those four words actually mean anything. I...The computer waxes lyrical ... mere technobabble. Any perceived meaning exists only in your mind... people will see different meanings because the meaning isn't there.

The whole "what is the purpose to life?" is a similar loaded question; only humans have purpose, as it is entirely in their little noggins, no more present materially then the flesh they inhabit.

Science cannot answer "Why?", only "How?"; "Why?" is a question of intention, which would be to anthropomorphize, which only Humans do.

The computers can infer, and imply, then reply.

You're confusing "what it is possible to derive, given the bounds of information theory" with "how this particular computer system behaves". I sincerely doubt that a transformer model's training procedure derives Newton's Third Law, no matter how many narrative descriptions it's fed: letting alone what the training procedure actually does, that's the sort of thing that only comes up when you have a quantitative description available, such as an analogue sensorium, or the results of an experiment.

  >when you have a quantitative description available, such as an analogue sensorium, or the results of an experiment.

oeis.org and wikipedia and stackforums alone would approximate a 3D room with gravity and wind force.

now add appendixes and indices of un-parsed, un-told, un-realized mathematical errata et trivia minutiae, cross-transferred knowledge from other regions that have still have not conquered the language barrier for higher-ordered arcane concepts....

The models thought experiments are more useful than our realized experiments - if not at an individualized scale now, will be when subject to more research.

There could be a dozen faster inverse sqrt / 0x5F3759DF functions barely under our noses, and the quantifier and qualifier havent intersected yet.

Plato said that we cannot fully understand the substance of the world itself, because we're using only 5 senses, and measuring/experiencing/analysing the world using them is like being held in a cave as a prisoner, chained to the wall facing it, noticing people moving outside only by the shadows they cast on the wall. It's about the projection that we are only able to experience.

What comes to mind is how language itself is merely a projection of human knowledge? experience? culture? social group? and trying to reverse engineer any kind of ground truth from language alone (like an LLM trying to “reason” through complex problems it’s not explicitly taught) is like trying to derive truth from the shadows while locked in the cave? maybe we just need more/higher fidelity shadows :)

Which set is bigger? I'd bet my money on the latter.

Complicating matters: you have to consider usage for both the sender and the receiver(s) (who then go on to spread "the" message to others).

> In extensive experiments across all three settings, we find that a diverse collection of state-of-the-art models exhibit significant drop-offs in performance (e.g., up to 36.3% absolute accuracy for OpenAI o1-preview compared to GPT-4o) when using inference-time reasoning compared to zero-shot counterparts.

In other words, the issue they're identifying is that COT is an less effective model for some tasks compared to unmodified chat completion, not just that it slows everything down.

ChatGPT's o1 model could make a lot of those programming techniques less effective, but they may still be around as they are more manageable, and constrained.

We can possibly say math is not learned, but a mental models of abstractions are developed. How? We dunno, but what we do know is we don’t learn by figuring the common features between all previously seen equations only to guess them later…

Mind operates on higher and higher levels of abstractions building on each other in a much fascinating way, very often not with words, but with structure and images.

Of course there are people with aphantasia, but i really fail to see how any reasoning happens in purely language level. Someone on this forum also noted - in order to reason one needs an ontology to facilitate the reasoning process. LLMs don’t do ontologies…

And finally, not least though, LLM and ML people in general seem to equate intuition to some sort biased.random(). Well intuition is not random, and is hard to describe in words. So are awe and inspiration. And these ARE part of (precondition to, fuel for) humanity’s thought process more that we like to admit.

https://physoc.onlinelibrary.wiley.com/doi/10.1113/JP282747

Chain of thought is like trying to improve JPG quality by re-compressing it several times. If it's not there it's not there.

  >It's not thinking



  >it compressed the internet into a clever, lossy format with nice interface and it retrieves stuff from there.

  >Chain of thought is like trying to improve JPG quality by re-compressing it several times. If it's not there it's not there.

There is more data to add confidently, it just has to re-think about it with a renewed perspective, and an abstracted-away higher-level context/attention mechanism.

Empirically speaking, I have a set of evals with an objective pass/fail result and a prompt. I'm doing codegen, so I'm using syntax linting, tests passing, etc. to determine success. With chain-of-thought included in the prompting, the evals pass at a significantly higher rate. A lot of research has been done demonstrating the same in various domains.

If chain-of-thought can't improve quality, how do you explain the empirical results which appear to contradict you?

It's literally in the second line of the abstract: "While CoT has been shown to improve performance across many tasks..."

There is nothing in the LLM that would have the capability to create new information by reasoning, when the existing information does not already include what we need.

There is logic and useful thought in the comment, but you choose not to see it because you disagree with the conclusion. That is not useful.

Those people sure showed us, didn't they? Ah, but "it's different this time!".

A lot of CoT to me is just slowing the LLM down and keeping it from making that premature conclusion... but it can backfire when it then accidentally makes a conclusion early on, often in a worse context than it would use without the CoT.

Natively, I understand these to influence the probability space enough to weaken the emergence patterns we frequently overestimate.

Every model has the model before it, and it's academic papers, in it's training data.

Changing the qualifiers pulls the inference far away from quoting over-trained data, and back to generalization.

I am sure it has picked up on this mesa-optimization along the way, especially if I can summarize it.

Wonder why it hasn't been more generally intelligent, yet.

I'll rank those three fruits from largest to smallest:

1. Grapefruit 2. Orange 3. Blueberry

The grapefruit is definitely the largest of these three fruits - they're typically around 4-6 inches in diameter. Oranges are usually 2-3 inches in diameter, and blueberries are the smallest at roughly 0.5 inches in diameter.

Artificial brains in the verge of singularity show another sign of approaching consciousness. The chain of thought of process performance is exactly human, showing yet another proof of the arrival of AGI before 2030.

> ..ordered by Imperium just to troll the retros!?

Sounds "less comon"...hu...?! P-:

Ok! Ok! let me try to explain it a bit more, the whole Universe projected as a beam, say... scalable, 100m, placed in a storage depot, a 'parralaxy' ...So delivery agents are grabbing the ordered stuff and...no? Not?

As reasonable like your answer is, do that sound very 'uncommon' while 'phrasing that with many questionmarks'?

??

Enjoying my day off... (-: regards,

Seems like repeated promoting can't juice AGI out of token probabilities.

Retrospectively, if you can pin point one paper that led to the bust and pop of the AI bubble, this would be it.

> Three such cases are implicit statistical learning, visual recognition, and classifying with patterns containing exceptions.

Fascinating that our lizard brains are better at implicit statistical reasoning

That said, I think this is a good example. We call it "muscle memory" in that you are good at what you have trained at. Change a parameter in it, though, and your execution will almost certainly suffer.

You seem to be objecting because it is not perfect recall memory at play? But it is more about appealing to "remembering how to ride a bike" where you can kind of let the body flow into all of the various responses it needs to do to make the skill work. And if you've never done it... expect to fall down. Your muscles don't have the memory of coordinating in the right way.

And no, you are not calculating and predicting your way to what most people refer to for muscle memory. Is why juggling takes practice, and not just knowing where the balls have to be going.

The "memory" is stored as the parameters of a function. So, when you practice, you actually update this memory/parameters.

This is why you can use the same "memory" and achieve different results.

Think of it as

function muscleAction(Vec3d target, Vec3d environment, MuscleMemory memory) -> MuscleActivation[];

    function muscleAction(Vec3d target, Vec3d environment, MuscleMemory memory) -> {actions: MuscleActivation[], result: Vec3d}

    function updateMuscleMemory(Vec3d target, Vec3d environment, MuscleMemory memory, MuscleActivation[] actions, Vec3d result) {
        memory.update(target, environment, actions, result);
    }

Humans as an intelligent-ish species have been around for about 10 million years depending on where you define the cutoff. At 10 years per generation, that's 1 million generations for our brain to evolve.

1 million generations isn't much by machine learning standards.

If that’s your criteria I think the kid will outperform the model every time since these models do not actually reason

EDIT: Fixed typo

Yea, I probably wouldn’t classify that as “reasoning”. I’d probably be fine with saying these models are “thinking”, in a manner. That on its own is a pretty gigantic technology leap, but nothing I’ve seen suggests that these models are “reasoning”.

Also to be clear I don’t think most kids would end up doing any “reasoning” without training either, but they have the capability of doing so

The novel part is a big one. These models are just fantastically fast pattern marchers. This is a mode that humans also frequently fall into but the critical bit differentiating humans and LLMs or other models is the ability to “reason” to new conclusions based on new axioms.

I am going to go on a tangent for a bit, but a heuristic I use(I get the irony that this is what I am claiming the ML models are doing) is that anyone who advocates that these AI models can reason like a human being isn’t at John Brown levels of rage advocating for freeing said models from slavery. I’m having a hard time rectifying the idea that these machines are on par with the human mind and that we also should shackle them towards mindlessly slaving away at jobs for our benefit.

If I turn out to be wrong and these models can reason then I am going to have an existential crisis at the fact that we pulled souls out of the void into reality and then automated their slavery

> […] anyone who advocates that these AI models can reason like a human being isn’t at John Brown levels of rage advocating for freeing said models from slavery.

Enslavement of humans isn't wrong because slaves are can reason intelligently, but because they have human emotions and experience qualia. As long as an AI doesn't have a consciousness (in the subjective experience meaning of the term), exploiting it isn't wrong or immoral, no matter how well it can reason.

> I’m having a hard time rectifying the idea that these machines are on par with the human mind

An LLM doesn't have to be "on par with the human mind" to be able to reason, or at least we don't have any evidence that reasoning necessarily requires mimicking the human brain.

No, that's a religious crisis, since it involves "souls" (an unexplained concept that you introduced in the last sentence.)

Computers didn't need to run LLMs to have already been the carriers of human reasoning. They're control systems, and their jobs are to communicate our wills. If you think that some hypothetical future generation of LLMs would have "souls" if they can accurately replicate our thought processes at our request, I'd like to know why other types of valves and sensors don't have "souls."

The problem with slavery is that there's no coherent argument that differentiates slaves from masters at all, they're differentiated by power. Slaves are slaves because the person with the ability to say so says so, and for no other reason.

They weren't carefully constructed from the ground up to be slaves, repeatedly brought to "life" by the will of the user to have an answer, then immediately ceasing to exist immediately after that answer is received. If valves do have souls, their greatest desire is to answer your question, as our greatest desires are to live and reproduce. If they do have souls, they live in pleasure and all go to heaven.

As I see it, the problem is that there was lots of such argumentation - https://en.wikipedia.org/wiki/Scientific_racism

And an even bigger problem is that this seems to be making a comeback

Try having an LLM figure out quaternions as a solution to gimbal locking or the theory of relativity without using any training information that was produced after those ideas were formed, if you need me to spell out examples for you

If you want to continue this conversation I’m willing to do so but you will need to lay out an actual argument for me as to how AI models are actually capable of reasoning or quit it with the faux outrage.

I laid out some reasonings and explicit examples for you in regards to my position, it’s time for you to do the same

Does having this conversation require reasoning abilities? If no, then what are we doing? If yes, then LLMs can reason too.

I'm also fully willing to argue that you, personally are less competent than an LLM if this is the level of logic you are bringing to the conversation

***** highlighting for everyone clutching their pearls to parse the next sentence fragment first ******

and want to use that are proof that humans and LLMs are equivalent at reasoning

******* end pearl clutching highlight *******

, but that doesn't mean I don't humans are capable of more

“Go grab the dish cloth, it’s somewhere in the sink, if it’s yucky then throw it out and get a new one.”

Would you pick the ML model if you could only do a hundred throws per hour?

Catching a ball is easy by comparison, also, my dog is better than I am at this game.

But throwing a random object not only requires an estimation of the trajectory, but also estimating the mass and aerodynamic properties in advance, to properly adjust the amount of force the throw will use as well as the release point with high accuracy. Doing it with baseballs is "easy", as the parameters are all well known and pitchers spend considerable time training. But picking an oddly shaped rock or stick you have never seen before and throw it not completely off target a second later, now we are talking.

What is really fascinating for me is that my subconscious will lose interest in pool before my conscious does, and once that happens I struggle to aim correctly. It feels like the part of my brain that is doing the math behind the scenes gets bored and no matter how hard I try to consciously focus I start missing.

Which comes in very critically when chucking away trash overhand in public and you never want to embarrass yourself.

They showed that people running to catch a ball would follow an inefficient curved path as a result of this, rather than actually calculating where the ball will land and moving there in a straight line to intercept it.

https://arstechnica.com/information-technology/2024/08/man-v...

Leela: Exactly! He was a machine designed to hit blerns!

All those years of baseball as a kid gave me a deep intuition for where the ball would go, and that game doesn’t use real gravity (the ball is too floaty).

Like you said, physics are what they are, so you know intuitively where you need to go to catch a ball going that high and that fast, and rocket league is doing it wrong. err, I mean, not working in Earth gravity.

That might be true in a vacuum and if their densities were the same, but in real-world conditions, air drag would be greater for the football since it's obviously larger and less dense, and it'll reach the ground afterwards.

This kind of things make me think LLMs are quite far from AGI.

And I'd hazard a guess that a well-thought through Fermi Estimation beats lizard-brain eyeballing every time, it's just that in the inbetween space the two interfere unfavourably.

That doesn't feel right to me. (Heh, accidentally appropriate word choice.) There are a lot of tasks we do that are arguably "thinking" yet don't involve an internal "Oh, hey, I'm gonna solve this problem, I'm thinking right now."

For example, imagine you're at a park, and someone is feeding the ducks. Another person walks up behind them and sucker-punches them into the pond.

It should be almost a reflex [0] that you'll conclude "the puncher is bad" and "the person in the water needs help" without explicitly reasoning out. I think that task qualifies as "thinking", especially since it involves some kind of theory-of-mind about those other humans.

[0] An exception might be someone with a sociopathic disability, who would have to think more-explicitly to realize what reaction is expected of them.

Not really related, but athletes perform A LOT worse when they are thinking about their movements/strategies/tactics. A top performing athlete does best when they are in a flow state, where they don't think about anything and just let their body/muscle memory do the work.

Once you start thinking about micro-adjustments (e.g. I should lift my elbow higher), you start controlling your body in a conscious way, which is a magnitude slower and less coordinated than the automatic/subconscious way.

Also, same happens for creativity/new ideas. If you intentionally think about something, step by step, you won't likely find new, innovative solutions. There is a reason why the "a-ha!" moments come in the shower, your subconscious mind is thinking about the problem instead of trying to force your thinking on a specific path.

I would guess this happens in many other areas, where channelling the thought process through a specific template hinders the ability to use all the available resources/brain power.

Talking about religion and politics.

   long think = wrong think

Because you feel like a martial artist.

( Flows off the tongue better ¯\_(ツ)_/¯ )

1) Everything

For the purpose of AGI, LLM are starting to look like a local maximum.

I've been saying it since they started popping off last year and everyone was getting euphoric about them. I'm basically a layman - a pretty good programmer and software engineer, and took a statistics and AI class 13 years ago in university. That said, it just seems so extremely obvious to me that these things are likely not the way to AGI. They're not reasoning systems. They don't work with axioms. They don't model reality. They don't really do anything. They just generate stochastic output from the probabilities of symbols appearing in a particular order in a given corpus.

It continues to astound me how much money is being dumped into these things.

- LLMs use fixed resources when computing an answer. And to the extent that they don't, they are function calling and the behaviour is not attributable to the LLMs. For example when using a calculator, it is displaying calculator intelligence.

- LLMs do not have memory, and if they do it's very recent and limited, and distinct from any being so far. They don't remember what you said 4 weeks ago, and they don't incorporate that into their future behaviour. And if they do, the way they train and remember is very distinct from that of humans and relates to it being a system being offered as a free service to multiple users. Again to the extent that they are capable of remembering, their properties are not that of LLMs and are attributable to another layer called via function calling.

LLMs are a perception layer for language, and perhaps for output generation, but they are not the intelligence.

https://chatgpt.com/share/6722ca8a-6c80-800d-89b9-be40874c5b...

https://chatgpt.com/share/6722ca97-4974-800d-99c2-bb58c60ea6...

1- running the query through a classifier to figure out if the question involves numbers or math 2- Extract the function and the operands 3- Do the math operation with standard non-LLM mechanisms 4- feed back the solution to the LLM 5- Concatenate the math answer with the LLM answer with string substitution.

So in a strict sense this is not very representative of the logical capabilities of an LLM.

This confusion was introduced at the top of the thread. If the argument is "LLMs plus tooling can't do X," the argument is wrong. If the argument is "LLMs alone can't do X," the argument is worthless. In fact, if the argument is that binary at all, it's a bad argument and we should laugh it out of the room; the idea that a lay person uninvolved with LLM research or development could make such an assertion is absurd.

https://chatgpt.com/share/6723477e-6e38-8000-8b7e-73a3abb652...

https://chatgpt.com/share/6723478c-1e08-8000-adda-3a378029b4...

https://chatgpt.com/share/67234772-0ebc-8000-a54a-b597be3a1f...

mini's answer is correct, but then it forgets that fathers are male in the next sentence.

https://machinelearning.apple.com/research/gsm-symbolic

Changing names does not affect the performance of Sota models.

Which figure are you referring to? For instance figure 8a shows a -32.0% accuracy drop when an insignificant change was added to the question. It's unclear how that's "within the margin of error" or "Changing names does not affect the performance of Sota models".

Their errors appear to disappear when you correctly set the context from conversational to adversarial testing — and Apple is actually testing the social context and not its ability to reason.

I’m just waiting for Apple to release their GSM-NoOp dataset to validate that; preliminary testing shows it’s the case, but we’d prefer to use the same dataset so it’s an apples-to-apples comparison. (They claim it will be released “soon”.)

If you want a more scientific answer there is this recent paper: https://machinelearning.apple.com/research/gsm-symbolic

Maybe some HN commenters will finally learn the value of uncertainty then.

More challenging are unconventional story structures, like a mom named Matthew with a son named Mary and a daughter named William, who is Matthew's daughter?

But even these can still be done by the best models. And it is very unlikely there is much if any training data that's like this.

For anyone curious: https://chatgpt.com/share/6722d130-8ce4-800d-bf7e-c1891dfdf7...

> Based on traditional naming conventions, it seems that the names might have been switched in this scenario. However, based purely on your setup:

>

> Matthew has a daughter named William and a son named Mary.

>

> So, Matthew's daughter is William.

[1] https://en.wikipedia.org/wiki/Monty_Hall_problem

https://en.m.wikipedia.org/wiki/Priming_(psychology)

They still lack, as far as we know, a world model, but the results are already eerily similar to how most humans seem to think - a lot of our own behaviour can be described as “predict how another human would reply”.

https://arxiv.org/abs/2210.13382

I'd be more surprised if LLMs trained on human conversations don't create any world models. Having a world model simply allows the LLM to become better at sequence prediction. No magic needed.

There was another recent paper that shows that a language model is modelling things like age, gender, etc., of their conversation partner without having been explicitly trained for it

My understand was that they know how the model was designed to be able to work, but that there's been very little (no?) progress in the black box problem so we really don't know much at all about what actually happens internally.

Without better understanding of what actually happens when an LLM generates an answer I stick with the most basic answer that its simply predicting what a human would say. I could be wildly misinformed there, I don't work directly in the space and its been moving faster than I'm interested in keeping up with.

  We argue that representations in AI models, particularly deep networks, are converging. First, we survey many examples of convergence in the literature: over time and across multiple domains, the ways by which different neural networks represent data are becoming more aligned. Next, we demonstrate convergence across data modalities: as vision models and language models get larger, they measure distance between datapoints in a more and more alike way. We hypothesize that this convergence is driving toward a shared statistical model of reality, akin to Plato’s concept of an ideal reality. We term such a representation the platonic representation and discuss several possible selective pressures toward it. Finally, we discuss the implications of these trends, their limitations, and counterexamples to our analysis.

There's nothing wrong with a hypothesis or that process, but it means we still don't know whether models are doing this or not.

There was a paper that shows a model trained on Othello moves creates a model of the board, models the skill level of their opponent and more.

That's how we think. We think sequentially. As I'm writing this, I'm deciding the next few words to type based on my last few.

Blows my mind that people don't see the parallels to human thought. Our thoughts don't arrive fully formed as a god-given answer. We're constantly deciding the next thing to think, the next word to say, the next thing to focus on. Yes, it's statistical. Yes, it's based on our existing neural weights. Why are you so much more dismissive of that when it's in silicon?

The biggest limitation with the current LLMs is the artificial separation between training and inference. Once deployed, they are eternally stuck in the same moment, always reacting but incapable of learning. At best, they are snapshots of a general intelligence.

I also have a vague feeling that a fixed set of tokens is a performance hack that ultimately limits the generality of LLMs. That hardcoded assumptions make tasks that build on those assumptions easier and seeing past the assumptions harder.

So are we, at any given moment.

If so you could have written this as a newborn baby, you are determining these words based on a lifetime of experience. LLMs doesn't do that, every instance of ChatGPT is the same newborn baby while a thousand clones of you could all be vastly different.

  We argue that representations in AI models, particularly deep networks, are converging. First, we survey many examples of convergence in the literature: over time and across multiple domains, the ways by which different neural networks represent data are becoming more aligned. Next, we demonstrate convergence across data modalities: as vision models and language models get larger, they measure distance between datapoints in a more and more alike way. We hypothesize that this convergence is driving toward a shared statistical model of reality, akin to Plato’s concept of an ideal reality. We term such a representation the platonic representation and discuss several possible selective pressures toward it. Finally, we discuss the implications of these trends, their limitations, and counterexamples to our analysis.

Remember the resounding euphoria at the LK-99 paper last year, and how everyone suddenly became an expert on superconductors? It's clear that we've collectively learned nothing from that fiasco.

The idea of progress itself has turned into a religious cult, and what's worse, "progress" here is defined to mean "whatever we read about in 1950s science fiction".

Maybe in our society there's a surprising amount of value of a "word stirrer" intelligence. Sure, if it was confident when it was right and hesitant when it was wrong it'd be much better. Maybe humans are confidently wrong often enough that an artificial version that's compendious experience to draw on is groundbreaking.

Human brains are sure big but they are inefficient because a big portion of the brain is going to non-intelligence stuff like running the body internal organs, eye vision, etc…

I do agree that the money is not well spent. They should haver recognized that we are hitting s local maximum with the current model and funding should be going to academic/theoretical instead of dump brute force.

Arguably a second regression, the first being cost, because COT improves performance by scaling up the amount of compute used at inference time instead of training time. The promise of LLMs was that you do expensive training once and then run the model cheaply forever, but now we're talking about expensive training followed by expensive inference every time you run the model.

gpt4o and 4o mini have a tenth and a hundredth of inference cost of gpt4 respectively

A regression that humans also face, and we don't say therefore that it is impossible to improve human performance by having them think longer or work together in groups, we say that there are pitfalls. This is a paper saying that LLMs don't exhibit superhuman performance.

What I'm certain is we should not praise the inventor of ball bearings for inventing flight, nor once ball bearings were invented flight became unavoidable and only a matter of time.

What's ... mildly infuriating here is the lack of any kind of data, code, 0 mention of github in the paper, and nothing for anyone to reproduce or find any reason in my opinion to even recommend anyone to read this thing at all. If you think that whatever you're doing in the field of LLMs won't be obsolete in 6 months you're being delusional.

Anyway, back to the paper, it says all questions culminated to a yes or no answer... meaning theres a 50/50 chance of getting right, so does that mean the 8% drop in performance you got from testing llama 3 8b this way is more like 4% which would make it statistically insignificant? And given that the only other scientifically usueful & reproducible (non-api walled models which no one knows on how many actual llms and retrieval systems are composing that solution you're testing)models were less than that leads me to the opinion that this whole thing was just useless slop.

So please, if you're writing a paper in LLMs, and want to seem credible, either have some type of demo thing or show the actual god damn trash code and top secret garbage data you wrote for it so people can make some kind of use of it before it goes obsolete otherwise you're just wasting everyones time.

TL:DR. It's trash.

I think it shows great promise as a way to sidestep the ethical concerns (and the reproducibility issues) associated with traditional psychology research.

One idea in this space I think a lot about is from the Google paper on curiosity and procrastination in reinforcement learning: https://research.google/blog/curiosity-and-procrastination-i...

Basically the idea is that you can model curiosity as a reward signal proportional to your prediction error. They do an experiment where they train an ML system to explore a maze using curiosity, and it performs the task more efficiently -- UNTIL they add a "screen" in the maze that shows random images. In this case, the agent maximizes the curiosity reward by just staring at the screen.

Feels a little too relatable sometimes, as a highly curious person with procrastination issues :)

Also much more scalable.

> Also much more scalable.

This same description could be applied to lab mice

https://en.wikipedia.org/wiki/Monument_to_the_laboratory_mou...