[cauch]

I think, for me, the thing is that when you do basic ML, you discover that ML will very often find data pattern that fit the goal but does not correspond to a real mechanism.

So, I think there is a flaw in the logic of saying that human text have a pattern of "consciousness mechanism" and therefore LLM will learn "consciousness mechanism" in order to return sentence continuation that is convincing. There is probably tons of data pattern that LLM can learn from to be able to reproduce a sentence continuation that is convincing without having to learn the specific mechanism that is "conscious".

For me, one element that shows it is the case is the absence of world model (or "human-like" world model) despite the fact that the sentence continuation is convincing. If indeed the only way to produce sentence continuation convincingly would be by "simulating a brain", then it would not explain the first LLM from several years ago (before the extra layers of RLHF, ...). They were able to have quite convincing conversation on a lot of non-trivial aspect, and yet failed on some aspects that should have been basic for a system that would have been trained to work like a human brain. It shows that it is possible to "cleverly disguise examples of sentence continuation" without having to build elements that one expect on a conscious being.

[Nevermark]

I didn't make the claim that a model can learn consciousness.

Understanding is not consciousness.

Their training is all about understanding. There is nothing in their architecture or training that credibly optimizes for rich self-awareness.

Given non-persistent experience, non-continuous operation, no ability to build up generalizations and aggregate experience of their own self-awareness over time, they seem to be structurally designed to not have consciousness.

This is a case where acting is very credible. Understanding of other's consciousness, in a functional and third party sense, isn't a substrate for personal experience.

In stark contrast, humans develop consciousness gradually over continuous time with persistent aggregation of experience. By the time we can recognize our own consciousness in the abstract, and reason about it, we have had it for some time.

[cauch]

I use "consciousness" because it's the point of the original argument, but in fact, I think my whole comment still work well if you replace "consciousness" with "understanding".

My point is that the fact that AI can reproduce convincingly human sentence continuation does not imply that the AI has no choice but ending up using a mechanism that "understand" rather than just have learned data patterns that are very effective to fake human sentence continuation but are meaningless in term of understanding the concepts.

And I think that if indeed the only way for AI to reproduce convincingly human sentence continuation would be to end up in a configuration that uses the "understand" mechanism to do so, the behaviour of the first LLM would not show that they are so good at sounding human and yet so bad at failing basic "understanding" tests.

[Nevermark]

> the fact that AI can reproduce convincingly human sentence continuation does not imply that the AI has no choice but ending up using a mechanism that "understand" rather than just have learned data patterns

Taken as an absolute without any addition context you are right.

But we are not talking about abstractions but specific successful models. The number of parameters models they have may seem large, but they are very small relative to the training data that they have to summarize. That cannot do it without discovering that patterns that make sense out of it.

And we can verify that. Simply discuss completely disparate topics, with some kind of intersection. Converge several highly unlikely topics, there are so many it would take billions of years to exhaust unlikely combinations.

If the model is only interpolating it will produce gibberish.

But that isn't what happens.

The fact that models can be near expert, and sometimes expert, across vast areas of human knowledge is a clue. If they don't understand that, then the question is, why do we think people understand things. Does having an answer mean a human understands something, or is their intuition and stream of conscious reasoning also not understanding? To be even handed about what we mean by understanding.

[cauch]

> That cannot do it without discovering that patterns that make sense out of it.

I don't think it's true at all, and I think we have indication that proves it is false.

We have "basic" LLM, the ones from 2023. They were producing _very convincing_ human text, and yet, they were too often failing basic tests that require understanding.

Now, we have more advanced models, but the counter-example of "basic" LLM demonstrates your assertion is incorrect: these model _did_ produce very convincing human text and yet did not make sense out of it.

But for the more advanced models, the problem is that they are "on top" of basic LLM. So, the first step is a training that build a mechanism that produce convincing text without understanding, and then, the "residuals" are fine-tuned. The result is very unlikely to add "understanding" to the model, because to do so, the whole system needs to deconstruct the basic LLM, to go back towards less efficient situations in order to rebuild almost from scratch. The fact that modern LLM are based on basic LLM means that the first step put the cursor in the bottom of the "basic LLM mechanism" valley, which is a local minimum. And any layer on top of it cannot "climb up" the slope of the valley, pass the ridge and fall into the next valley, even if this next valley has a lower minimum.

> The number of parameters models they have may seem large, but they are very small relative to the training data that they have to summarize.

That is demonstrably an incorrect logic jump. For example, CNN are able to distinguish between pictures of cats and pictures of dogs. The weights in these models are very small relative to the number of pixels they have been trained on. Yet, they distinguish cats and dogs by finding specific shapes in the pictures, without understanding what a 3-D cat and a 3-D dog is.

They have done that without discovering the typical human pattern that make sense of "cat" and "dog". And yet, the number of weights is very very small with respect to the number of pixel used in training.

> And we can verify that. Simply discuss completely disparate topics, ... > If the model is only interpolating it will produce gibberish.

What you are saying is that the model is not simplistic interpolation. But that is a straw man argument: people who say that LLM don't understand don't say LLM are equivalent to simple interpolation machine.

But the problem is that you can have very good predictions in novel situations without understanding.

For example, if you have 10 totally different situations that can be described with a Gaussian curve, and that I show you points for a new situations that cover the left side of a Gaussian curve. Then you will be able to guess that the right side of the curve, which is not an interpolation as it corresponds to situations you never saw, will behave like the rest of the Gaussian curve. And yet, in these 11 situations, I did not even say which real physical phenomenon I'm talking about. You haven't understood anything about these phenomenon, all you have done is guessed that a typical pattern that you have observed somewhere else is more likely to apply here too, without even having to understand anything about the reality of this situation.

And of course, this prediction is "a guess": maybe, for once, in this 11th situation, the curve will start as a Gaussian curve but will suddenly be different. But it happens that the reality is that in this 11th situation, the correct description is a Gaussian curve (because, due to the maths, Gaussian curves are really common). So, when you make your prediction, it looks like you understand the situation, it looks like you understood the physical mechanism that applied here. But it is not the case.

So, no, correctly doing such prediction does not demonstrate understanding.

> The fact that models can be near expert, and sometimes expert, across vast areas of human knowledge is a clue.

That is not at all sufficient. A Chinese room experiment will do that despite the system not understanding Chinese. A pocket calculator will be able to be expert in math computation.

> If they don't understand that, then the question is, why do we think people understand things.

That's the wrong question. The correct question is: we know people understand things, and we see AI behaving similarly to people in some aspect, but is this behaviour _requires_ understanding, or can we reproduce this behaviour without needing to understand?

The fact that "basic" LLM were able to reproduce very convincing text that look like they understood X and yet were demonstrably showing lack of understanding of X demonstrates that we cannot just jump to the conclusion that just because it looks the same, the only possibility is that the core mechanism is identical.

[hatthew]

I think most debates about LLMs understanding boil down to different definitions of the word "understand." For example, with the definition of "understand" that I typically use in my daily life, I would argue that in the chinese room, the system as a whole "understands" chinese.

[cauch]

Fair enough, but then, a pocket calculator also understands math, and a pocket translator also understands language. And a wikipedia page that inform you about radioactivity understands nuclear physics. Some will maybe say it is the case, but if we talk about the LLM capabilities as a novelty, then it implies that we are talking about something else, because otherwise, it is not novel at all and it does not make sense to pretend it is.

[lstodd]

Thank you for writing this out.

[Nevermark]

It turns out that the optimal way to highly compress complex information is to understand it.

Sometimes, a problem being hard means you only get bad solutions, or increasingly accurate ones.

The planet isn't big enough for the proverbial interpolative stochastic parrot, over the training set of global human communication.

[cauch]

Two problems with that.

Firstly, how do you know that the optimal way to highly compress complex information is to understand it? You think it is obvious because you are very familiar with "understanding" as a way to summarise complex information. But there can be billions of different ways, outside of human imagination, that is as good or even better.

But secondly, LLM don't find the optimal way, they find the local minimum. Everyone who worked with NN knows that they are prone to come up with spurious pattern, incorrect correlations and bad workaround to guess the correct answer. You regularly need to nudge the NN by creating specifically engineered features to avoid them to fall into the first local minimum.

When it comes to LLM, it is extremely complicated to control to see if the LLM has triggered on a misleading pattern that, by chance, links two "tokens" together, or on a real concept that indeed links two "tokens" together. Basic probability implies that there are probably tons of "fake patterns" engraved into the weight during the LLM training, "fake patterns" that should not exist if there was any kind of "understanding" of the abstract mechanism that links these tokens.

[Nevermark]

> Firstly, how do you know that the optimal way to highly compress complex information is to understand it?

What is your non-performance baseline for "Understanding"? We don't have such a measure for humans.

Understanding is the behavioral ability demonstrated by learning to model something complex well. Beyond mappings, associations, interpolations.

Models clearly do. Mix up the most unlikely combination of non-trivial subjects, and they response sensibly. Those are not averaged, interpolated by any order, or even combinatorially interactions.

There is a reason those kinds of encodings, mappings, associations, interpolations, statistics / stochastics, all failed miserably for decades. Still fail. It took topological transforms, reminiscent of how we compute (dendrite-soma-axon, tensor-sum-nonlinear), and then they lept several orders of magnitude ahead of any alternative.

The problem with models composed of relationships of lower order than the phenomena they are trying to model, is they require combinatorially more parameters to model anything complex.

For simple problems, poor models fail gracefully. For complex problems, poor models just fail.

[cauch]

> Models clearly do. Mix up the most unlikely combination of non-trivial subjects, and they response sensibly. Those are not averaged, interpolated by any order, or even combinatorially interactions.

How do you even know it is the case?

How do you know the output is not the result of combinatorial interactions?

How do you even know that the "sensible" response on unlikely combination is not the result of a simple recipe that "make the response sounds sensible"? Either you, yourself, have some expertise on the subject, and therefore the combination does probably exist in the AI training data, or you don't and you have no idea if the response is sensible or is the usual smooth talk that everyone could come up spending 2 or 3 hours googling on the subject and crafting something sensible.

Worse, you are saying that the model "understand", which means that it discovers the underlying mechanism that drive the output. This "understanding" is a set of equation that link different concept, that explain how one concept affects another concept. So, it is "combinatorial interaction". Not a simple linear one, but guess what, LLM are designed to introduce non-linearity.

Even when AI are able to find new solution of math problem, the result is, like when done by humans, by using existing basic tools to build more complicated ones.

> It took topological transforms, reminiscent of how we compute (dendrite-soma-axon, tensor-sum-nonlinear), and then they lept several orders of magnitude ahead of any alternative.

And yet, the LLM elements that are "similar" or "analogue" to how the human brain works are very small. The human brain has thoughts "flowing", while LLM can only work "by step". The human brain is able to learn on a very reduced dataset, while LLM need more data that a human will ever be able to analyse, even less store. The human brain has "memory" and "context" intrinsically intertwined with how it works, while you can decouple these from the LLM. ...

Finally, here is a good contradiction of having you in one side saying that AI is mimicking the human brain and it is why it works well and on the other hand saying that AI will find the lowest minimum and that this minimum is "understanding how the phenomenom works" rather than "repeating by hearth what it was told during training".

As a human, when you mentally compute 6 times 7, what do you do? Do you do: "6 follows 5, which follows 4, which follows 3, ... and 7 follows 6, which follows 5, ... so we have (1 + 1 + 1 + 1 + 1 + 1) times (1 + 1 + 1 + 1 + 1 + 1 + 1), which is 1 + 1 + 1 + 1 + ..."? I guess you probably don't, you just remember the most helpful element you remember by heart. For example, you remember by hearth that 6x7 is 42. Or you remember that 3x7 is 21, and therefore 6x7 is the double, 42. Or you remember that 7x7 is 49, and therefore 6x7 is 42. Or even have a "feeling" from a mixture of all these (6x7 is somewhere around 40 because 5x7 feels like being around 30 and 7x7 feels like being around 50, and if I think of number in the 40 that "feels" like they are from the 7-multiple-table, I remember 42).

Same thing when a human does 324x42: the majority of humans will decompose it in "simpler" multiplication that they remember by hearth and, and only then, they will combine them. It is a good example of how the brain optimise: by balancing the trade-off of "using memory" and "using understanding": basic operations use memory, but of course it is inefficient to use memory for all numbers, in which case it will use a combination of both.

The way human do basic math operation is not purely by "understanding" arithmetic, it is by relying on what they remember from their training. At the same time, humans know how arithmetic works, and they will use it when relevant. Yet, the human brains prefer to rely on some "learnt by hearth" elements. This is in contradiction with your assertion that optimisation will always lead to "understanding" and that human brains is optimizing the same way AIs do.

This is only one example with numbers, but of course it works with plenty of other things. This is also exactly why humans get "the wrong idea" on plenty of phenomenon, that are then described as "counter-intuitive".

The reason "by hearth" is part of a good strategy rather than "purely understanding" is because there is a trade-off between "memory" and "compute", in both the human brain and AI: it is easier (and therefore a stronger attractor during the optimisation of the process of "getting the correct answer") to do the faster operation "retrieve from memory" than to do the slower operation "retrieve the theory from memory, compute the first step, store it in the short term memory, compute the second step, store it in the short term memory, compute the final answer by adding the first step answer and the second step answer".

[missingrib]

I think Searle's Chinese Room argument refutes this. LLMs are simply manipulating symbols, they do not have semantic understanding. This is why hallucinations exist. And Searle's argument extends even further than LLMs.

You are basically arguing for a functional account of consciousness, but things like this have been debated for literally decades/centuries in philosophy.

[rerdavies]

Millenia, in fact. The big difference, of course, being that we now have experimental philosophy machines (aka computers). So we can actually put some of these theories to the test, and recognize how utterly inadequate most of the work done on the subject has been. We had a pretty good idea anyway, so it's not a big surprise. Theories of mind have evolved dramatically in the late 20th century. And it's pretty clear that theories of mind will have to be re-done all over again with the advent of LLMs (particularly current-generation LLMs).

The problem with the hallucination argument is (1) that is much less of a problem with good current generation AIs, and (2) living conscious breathing human beings also have a disturbing tendency to make shit up, too. So a tendency to make stuff up doesn't really serve as a disqualifier for consciousness.

Also worth mentioning that the guiding rule of what's philosophical or not is whether it's actually useful. Actually useful philosophy usually becomes something else. Usual some scientific discipline or another. And as it turns out, theories of mind are likely to become extremely useful in the near future. Expect huge advances!

[cauch]

I think one could argue the opposite.

1) Good current generation AIs are specifically trained to reduce hallucinations. If we had new AI system that happened to not have hallucinations as a side effect of their training, then it would be convincing. But here, it looks like we have built a pocket calculator that answer 7+13 = 14, and on top of it, we added a layer that says "if the input is 7+13, then replace the output by 20". This pocket calculator still does not know how to calculate, we just added a layer to hide its mistakes.

2) Not only "make shit up" is not the same as "hallucination" (either "making shit it" is done when the individual knows it is unreliable, or when the individual was given wrong inputs), but the point is not to say "hallucination implies no consciousness", but "large quantities of hallucinations in situations where a conscious system would be unlikely to hallucinate implies no consciousness"

[rerdavies]

Yeah,don't use GPT for that. It really can't do basic arithmetic.

Try Claude, which can.

[cauch]

Wow, I don't think you understood at all.

First, the "13+7" is an analogy. In this analogy, "13+7" is not the real question you ask, it represents _any questions_, not just arithmetic.

But secondly, did you even noticed that in my example, the system answer CORRECTLY "13+7"? So, in my example, the thing I'm talking about and I argue does not "understand" is Claude, even if it is able to answer correctly.

My point is: the "basic LLM" part is creating a mechanism that answer without understanding (as demonstrated for example by ChatGPT failing arithmetic), and the fine-tuning or the harness is just hiding the lack of understanding by adding ad-hoc correction on the residuals. And because it is on the residuals, it looses the logical links (13+7 -> 20 is "logical", it corresponds to the math logic, it corresponds to what you get when you add 13 stones and 7 stones together. The residual is "14 -> 20", which has no meaning in itself)

The ad-hoc correction is either: 1. by training the model so it learns by heart, without understanding, that the symbols "13+7" should lead to "20", 2. or by training the model to use a pocket calculator without understanding arithmetic so it can do it itself.

You can prove that the model does not understand it very simply. Let's take the normal fine-tuned model M1. Now, let's go back to the pre-tuned version, and fine-tune it so it answer "21" to the question "13+7", and use an harness that does "sum(x, y): return x+y+1". This is model M2. M2 will fail to answer "13+7" correctly, it will say "21". And yet, M2 has been trained exactly the same way M1 was. If it is true that the additional tuning "add understanding", M2 will not be possible, it will say "error, error, do not compute, you try to train me to say that 13+7 is 21, but it does not make logical sense to me". But it does not happen: the pre-tuned model has no idea that 13+7=20 is more logical than 13+7=21, and the additional tuning is just helping him returning a more correct answer while still having no idea where this answer comes from.

[VirusNewbie]

>LLMs are simply manipulating symbols, they do not have semantic understanding.

falsify this. Show me a way you'd be able to prove they do/don't, that would work for humans.

[bondarchuk]

Searle's Chinese Room argument is wrong.

[miyoji]

This is not helpful. In what way is it wrong? Does the person in the room know Chinese?

[bondarchuk]

It is a helpful pointer for people who might otherwise assume that a well-known argument by a famous philosopher is sound without checking too deeply. Straightforward refutations can be found on wikipedia or by thinking about it.

[miyoji]

That just isn't true, there are no straightforward refutations of the Chinese Room that are widely accepted. Philosophers disagree about it. It's highly controversial and pretending that it's decided one way or another is not a helpful pointer for anyone.

[datsci_est_2015]

I’m also fixated on the term “experience” in the context of this debate. To me, consciousness is something that one “experiences”, and the two concepts are intertwined.

I am far from convinced that the training and inference regimes of LLMs would qualify as “experience” by any sense of the word.

Now, if we hooked up a plethora of audiovisual and tactile sensors with live feedback directly to a neural network rich with transformers, that was always powered on and fully autonomous, we may be getting there. But we’d probably also be on the verge of manmade horrors beyond our comprehension.

Biological rodent neural networks in a Petri dish stimulated by electrical impulses - more or less conscious than LLMs?

Human on life support, unable to respond to any external stimuli, “braindead” - more or less conscious than LLMs?

[rerdavies]

I point of sorts. Assuming that is true (I don't think it is), the big question that urgently needs to be addressed is what happens when we DO give LLMs tools to interact with the real (or virtual) world. And people are doing that, right now, in both real and virtual worlds. And people ARE giving LLMs the ability to run continuously for long periods of time, sometimes with enormous context buffers. People ARE putting LLMs into robots with front-end ML and LLM systems for visual processing, and back-end ML systems for autonomous control.

And, yes, concerns about whether biological rodent neural networks are or are not conscious come up frequently in the biological neural network papers. I'm not sure I would want to be a researcher trying to get an experiment past an ethics committee if my biological neural network had 25B rat neurons. (I would hope that they could not).

[Terr_]

> a flaw in the logic [...] mechanism

Similar to: "Birds fly, my spinning helical device flies, therefore we've started to replicate how birds fly."

> without having to build elements that one expect on a conscious being

One of the elements I expect in a conscious being is that you can't rewrite it by changing the introductory paragraph.

When it comes to LLMs, almost every "mind" we humans perceive is a fictional character in an LLM-generated story-document, one we are either reading or which is being "acted" at us by regular code. Our own instinct for pareidolia and simulating/inferring other minds is very strong, which means we should require really good evidence/logic to counter our instincts.

Even if one believes the LLM has a single "real mind" as an author of every document... what evidence do we have that it is conscious or "self-inserting" itself as one of the characters in the document?

[famouswaffles]

>One of the elements I expect in a conscious being is that you can't rewrite it by changing the introductory paragraph.

If we had enough knowledge of the workings of the human brain, you could alter the perception of every single memory you've ever had. And limited versions of this already happen all the time. Human memory is notoriously unreliable for a reason.

Are you aware of the Recovered Memory Therapy Scandals of the 80s/90s ? Boy did that ruin a lot of lives. You can rewrite a human by changing their 'introductory paragraph'. It's just not as accessible.

[NateEag]

> If we had enough knowledge of the workings of the human brain, you could alter the perception of every single memory you've ever had. And limited versions of this already happen all the time. Human memory is notoriously unreliable for a reason.

Knowing how something works is not the same as having the tools to change it.

Discovering memories are incorrect does not massively change who we are. As someone with a very defective memory, I discover on an hourly basis that I'm won't about something I thought was true, but there's still continuity and consistency to my personality and general approach to life.

...in fact, as someone who was raised an evangelical Christian and believed wholeheartedly without a shadow of doubt, then lost my faith entirely in my late thirties, I sort of did have my "introductory paragraph" changed, yet my wife, children, and friends would all say I'm still me, and that my core personality and nature remains largely the same.

> Are you aware of the Recovered Memory Therapy Scandals of the 80s/90s ? Boy did that ruin a lot of lives. You can rewrite a human by changing their 'introductory paragraph'. It's just not as accessible.

The recovered memory scandals are not even close to evidence that you can rewrite a human.

The people who thought they had learned new facts about themselves did not suddenly lose their context as humans in 20th century America.

They did not suddenly lose their sense of humor, or develop a previously-unseen penchant for murdering small children.

They experienced a revision of belief, and a pretty major one that really distressed them, but it did not change everything about them.

LLMs _do_ manifest wildly differently based on the first paragraph.

[hackinthebochs]

> I think there is a flaw in the logic of saying that human text have a pattern of "consciousness mechanism" and therefore LLM will learn "consciousness mechanism" in order to return sentence continuation that is convincing.

There is no independent "consciousness mechanism" that one might imagine humans have learned or evolved for its own sake. Evolution learns various solutions to optimization problems, and so if consciousness evolved then it was either useful instrumentally, or it is a byproduct of some organization that is useful instrumentally. The point is that as a solution to certain kinds of optimization problems, consciousness can conceivably be the solution to the optimization problem of predicting the next token of text written by humans who themselves have complex phenomenology. There is nothing that a priori constrains token prediction from the domain of consciousness.

>For me, one element that shows it is the case is the absence of world model (or "human-like" world model) despite the fact that the sentence continuation is convincing

World models don't have to be rich and detailed to count as a world model. Lower life forms might be conscious but they only model the part of the world useful for their existence in their ecological niche.

[cauch]

> The point is that as a solution to certain kinds of optimization problems, consciousness can conceivably be the solution to the optimization problem of predicting the next token of text written by humans who themselves have complex phenomenology.

Yes, I agree with that. Consciousness is a good way of generating convincing human text.

What I don't agree with is that consciousness is the only way to generate convincing human text and that because we have convincing human text, it can only imply we have consciousness.

There is a huge probability that generating convincing human text can be done without consciousness. Either because there are efficient mechanisms as efficient as the way the human brain deal with this problem and that the LLM found one of them (and these mechanism may be quite difficult to imagine for a human). Or even because the LLM found a local minimum and is stuck there.

To re-use the evolution approach: evolution solved the "flying problem" with bird feathers, but also with insect wings or bat wings. The fact that evolution ended up using feather does not imply that everything that flies can only fly with feathers.

> World models don't have to be rich and detailed to count as a world model

I agree in general, but here, we are talking about machine that reproduce all human language. The argument I'm answering to is pretending that "all of human knowledge" is understood, which include every single human concept. This has to be everything, because LLM is able to provide convincing text about every subject. If on some subject, the LLM is able to provide convincing text without "understanding" it, then the argument that it is impossible to provide convincing text without understanding it collapse.

[trescenzi]

> There is no independent "consciousness mechanism" that one might imagine humans have learned or evolved for its own sake.

> There is nothing that a priori constrains token prediction from the domain of consciousness.

We don’t know either of these are true or false though. We simply don’t know. There is no agreed upon definition of consciousness, aside from maybe _the having of qualia_, so arguing that some can or cannot be conscious a priori can’t be done.

[hackinthebochs]

>There is no agreed upon definition of consciousness

No one genuinely engaged with the topic is confused about the target of the term (phenomenal) consciousness. Definitions come once the theoretical work is complete, to be articulated as part of a fully worked out theory. The lack of a definition doesn't prevent us from investigating the subject or offering conjectures. What we can do is offer a precise description of the target and argue for or against whether LLMs reach the description. We will of course debate whether the offered description captures the relevant phenomena. But this is all just part of the process.

[int_19h]

> There is no agreed upon definition of consciousness, aside from maybe _the having of qualia_

Try to define qualia though without explicitly or implicitly recursing into consciousness.

It's all a large house of cards that's built on handwaving and "I know it when I see it".

[dnautics]

I think, for me, the thing is that when you tutor undergrads in abstract math, you discover that students will very often find data pattern that fit the goal but does not correspond to a real mathematical principle.

sometimes humans making claims about AI intelligence or consciousness also identify spurious patterns that do not correspond to the problems of intelligence or hard consciousness.

[Terr_]

> students will very often find data pattern that fit the goal but does not correspond to a real mathematical principle.

That reminds me of a niche paper [0] critiquing a certain way of teaching remedial math that was over-focused on tests. A kid named Benny (12) was building up (wrong) "rules" for math which still somehow gave enough of an illusion of progress in terms of test scores that his misunderstandings hadn't been caught earlier.

> Benny was able to explain his procedure; e.g. for 5/10=1.5, he said: "The one stands for 10; the decimal; then there’s 5... shows how many ones." In another example, 400/400 = 8.00 because "The numbers are the same [number of digits]... say like 4000 over 5000. All you do is add them up; put the answer down; then put your decimal in the right place... in front of the [last] three numbers."

[0] https://people.wou.edu/~girodm/library/benny.pdf

[cauch]

Of course. But in my explanation "consciousness" or "understanding" is not "finding pattern", it is the pattern itself.

CNN are finding patterns, sometimes relevant, sometimes spurious, but I don't think people argue that CNN have evolved consciousness or understanding of what a cat or a dog is.

Here, the argument is "LLM are able to understand, because 'understanding' is the only pattern to reach the goal". I'm saying that it is unlikely to be the only pattern, and that it is likely that they find a local minimum on a system that reaches the goal that does not use 'understanding'.

The reason I'm saying it is likely is because "basic" LLM shows behaviours where they are producing convincing human text and yet doing things that are really difficult to reconciliate with the fact that they have understanding.

(And before that old argument is used, yes, I know sometimes some humans fail to understand. The problem is that the majority of humans don't fail to understand basic stuff in the majority of the time, while the "basic" LLMs do. The fact that you roll 10 dices 100 times and 1 of them never land on 1 does not convince me that that set of dice is loaded. The fact that you roll 10 dices 100 times and 9 of them never land on 1 does convince me that that set of dice is loaded.)

[saimiam]

Not just undergrads. Even folks who believe in astrology or numerology depend on finding patterns in unrelated events to explain human behaviours.