Comment by cjbprime | Hacker News Reader

Comment by cjbprime | Hacker News Reader

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> To date, no neural network shows a capacity for reasoning and creativity worthy of an animal level. Current LLMs are autoregressive models that predict the probability distribution of the next token given the set of previous tokens called context:

This part seems unsubstantiated. The author implies that next-token-prediction does not lead to reasoning and creativity "of an animal level". But arguably GPT-4 is already at that level (can your dog predict what a novel Python function would output when it is executed, or write a rap song about autoregressive training where every word starts with the letter "a"¹?).

Regardless of whether GPT-4 is there already, it would certainly be a risky bet to say that it is impossible for a GPT-5 to get to "animal level" (whatever that means) reasoning and creativity while still using next-token-prediction.

As the article points out in the next paragraphs, there is no obvious limitation to next-token-prediction, because making the best prediction requires fully understanding the words in the context, as well as their true meaning and the reason they were written.

Related to creativity, here's a Midjourney image resulting from my spouse prompting "a studious corn snake": https://chris.printf.net/a_studious_corn_snake.png

¹: via GPT-4:

==

(Verse 1) Autoregressive, arranging, assessing answers, Adjacent activations, astounding, acting as anchors, Associating abstracts, analyzing antecedents, Aptly approximating, articulating, all adjacent aspects

(Chorus) Astonishing achievements, astounding accolades, Anomalous arrangements, ascending always, Audacious algorithms, autoregressive amaze, Applying ample artistry, as alliteration ablaze

(Verse 2) Accelerating acumen, anonymous aspirations, Anecdotal adventures, allegorical animations, Applied apparatus, assertive applications, Astral alliterations, assembling aggregations

(Chorus)

(Outro) Amplified abilities, augmenting atmosphere Adapting, acclimating, always aiming austere Autoregressive training, ascending, advocates Awe-inspiring artistry, as alliteration activates

>But arguably GPT-4 is already at that level

There really is no arguably about it. There isn't a verifiable definition/criteria for general intelligence that GPT-4 fails that a significant chunk of the human population doesn't also fail.

GPT-4 performs nearly all tasks it's given at at least the average human level, usually well above the average baseline. In some cases (nlp, analogical reasoning - https://arxiv.org/abs/2212.09196, and some others it's top %)

You basically have to make up your own definition that can't be substantiated (don't worry, plenty seem to do this). It almost always boils down "Well it's not "real" understanding, the difference is huge, i can't show you this supposed huge difference but trust me bro, it's not real"

> GPT-4 performs nearly all tasks it's given at at least the average human level

yet, you will afraid to trust gpt-4 to file your taxes through installed browser plugin.

There is a huge gap between some narrow cherry picked (researchers have strong incentive to report mind boggling results) and diverse and multi modal real life situations

I wouldn't trust the vast majority of humans to file my taxes either, and I definitely wouldn't trust any below a certain intelligence level. Nevertheless that doesn't mean those humans are completely devoid of any intelligence at all.

I don't need to cherry pick, I use GPT4 daily. At this point naysayers can only be willfully ignorant or uncreative in their use of it.

I also tried to use gpt4 daily(canceled lately), and limitations of its "intelligence" are very visible to me.

So which one are you? Willfully ignorant or uncreative in using it?

It's not possible for me to believe it is anything else.

> It's not possible for me to believe it is anything else.

you should try to work on your limitations..

There still isn't any evidence that it can pass novel Winograd schemas (as in, ones it cannot possibly be trained on), which almost every human can pass. The abstract reasoning corpus is more of a "lots of people will fail this too" test, but if a human is approaching it carefully they will be able to do it and there's still no evidence that GPT-4 can do it reliably (I expect that to change once it's multimodal).

The systems are very impressive and I think there is a 'there' there, they're clearly intelligent to some significant degree. But it's incorrect to say that they pass every test.

I just tested GPT-4 on the Winograd schemas below, which I made up and (as far as I know) are novel.

It scored 3/4, incorrectly answering "the pouch" instead of "the baby kangaroo" for Question 2 of Schema 1. And now that I look at the question, one could easily imagine the kangaroo being afraid to jump into a pouch that's too large! In hindsight, if a human complained to me that this question was ambiguous, I'd have a hard time disputing them.

Granted these are just two examples, but they aren't cherry-picked. Moral of the story: before one makes a strong claim such as "there isn't any evidence a model can do X", it's often worth making a minimal effort to investigate one's own claim.

========================================

Winograd Schema 1:

Sentence 1: The baby kangaroo was afraid to jump into the pouch because it was too small. Question 1: What was too small? (Correct answer: the pouch)

Sentence 2: The baby kangaroo was afraid to jump into the pouch because it was too large. Question 2: What was too large? (Correct answer: the baby kangaroo)

Winograd Schema 2:

Sentence 1: The gardener couldn't grow the flowers in the greenhouse because it was too hot. Question 1: What was too hot? (Correct answer: the greenhouse)

Sentence 2: The gardener couldn't grow the flowers in the greenhouse because they were too delicate. Question 2: What were too delicate? (Correct answer: the flowers)

I have tested them on novel Winograd schemas, they didn't pass; that's why I said what I said. Your second schema was incorrectly constructed, in both the first and second sentence the pluralisation of the pronoun in question gives away the answer. "It" won't apply to "flowers" because they're plural, and "they were" won't apply to "the greenhouse" because it's singular. As a result, those sentences aren't Winograd schemas because they're not testing common sense pronoun attribution specifically, they're testing the ability to maintain plural status consistency, which is doable by much less intelligent systems. It's also standard to ask "What does the pronoun '[pronoun]' refer to in that sentence?" rather than "What was too small?" or similar, as using descriptive words is also giving away information that you're meant to be testing the model on. But plenty of people make that mistake, so it's not that important.

Summing up, out of your four (not an acceptable sample size to form evidence) attempts to subject a SOTA LLM to Winograd schemas, two of them were actually Winograd schemas and only one of those got answered correctly. That isn't evidence that LLMs can pass novel Winograd schemas. If you actually come across such evidence, you should post it publicly on at least a blog page if you can't access the infrastructure to publish the results in a paper, even though the finding would certainly be novel enough to deserve a paper. As I said: there is no evidence that LLMs can pass novel Winograd schemas as of yet. We could get said evidence tomorrow, but it doesn't exist right now.

I would put a condescending moral of the story here, but I don't feel any particular need to belittle you, and instead hope you just learn from the information as presented.

Hey — you're quite correct and I appreciate this feedback. Thank you.

Moral of the story: before one makes a strong claim such as "there isn't any evidence a model can do X", it's often worth making a minimal effort to investigate one's own claim.

Do you think this was a strong refutation ? I just flipped a coin for fun and also got 3/4 correct.

I understand your point, but I mean, something that's intelligent really should've got the second question in that test correct.

> But it's incorrect to say that they pass every test.

Interesting! So there is published evidence, that it cannot pass novel Winograd schemas? Could you please provide me with the sources? I would like to have a look at the prompts. From my experience you can make GPT-4 pass novel Winograd Tests by giving GPT-4 additional context.

I'm not sure if you're joking or trolling, but on the chance that you're not:

>So there is published evidence, that it cannot pass novel Winograd schemas?

There is no published evidence that they can, which is what I said.

>Could you please provide me with the sources?

I cannot prove a negative to you with a source. You will have to review the literature like I did.

>From my experience you can make GPT-4 pass novel Winograd Tests by giving GPT-4 additional context.

This is why I am pretty sure you're trolling. This is a lot like saying "You can make GPT-4 do prime factorisation of a semiprime by noting the constituent primes." The point of testing the model is to see if the model can give you the answer, not to give it the answer. The point of Winograd schemas as a test is that they test the ability to attribute pronouns using common sense, without additional context. The reason it's a good test is that every competent human being can pass the test, and as of yet no computer can.

Not everyone is trolling on the internet my fellow human.

> This is why I am pretty sure you're trolling. This is a lot like saying "You can make GPT-4 do prime factorisation of a semiprime by noting the constituent primes." The point of testing the model is to see if the model can give you the answer, not to give it the answer.

It is not "giving the answer to the model" if you adjust the prompt with additional instructions like "this is a test question" or "use common sense". It is about creating the correct frame of context. With these kind of adjustments GPT-4 passes every novel Winograd scheme I have presented it with.

If that's actually true, please publish it. A blog post will do, it'll probably get popular and force a scientist to reproduce it.

> There isn’t a verifiable definition/criteria for general intelligence that GPT-4 fails that a significant chunk of the human population doesn’t also fail.

The usual broad indicia of intelligence (which, e.g., IQ is considered as a useful proxy measure for in humans because it predicts/correlates with them) are lifelong success in a wide variety of tasks GPT-4 can’t even begin to approach.

>are lifelong success in a wide variety of tasks GPT-4 can’t even begin to approach.

Right. and what are these tasks and why is intelligence the barrier? (meaning don't tell me something like driving, blind people are not unintelligent)

Well I've tried asking for lists of words that satisfy certain simple constraints, like a theme and a given number of syllables. Almost no word satisfied the constraint, despite my many tries. So if you can have a conversation that goes "give me a list of 3 syllables word", you get a list and then you ask for each word "how many syllables is that word?" and get answers like 2, 4, 5, sometimes 3. You can ask the question again and still get the same words with wrong numbers of syllables. Despite a very obvious logical implication of facts here.

This sounds quite similar to Bing famously explaining to a user that 2022 is in the future while we are in 2023.

It's something you would expect maybe a child to do (it reminds of "Patrick Star's Wallet" meme), but to me that clearly fails the notion of intelligence in a very fundamental way. What would be the counter-argument?

Language Models don't see words. They see tokens. So asking them to perform minute operations on letters/syllables/words etc don't work well. Doesn't really have anything to do with Intelligence.

Sentences look something like this to the model, [754 76 4752 67 456]

Here you have two predicates on words P: Sigma* -> N and T: Sigma* -> N. The question is give a list of words w such that for all w, P(w) and T(w) are both true. It has access to the correct values for P(w) and T(w), so there is a simple algorithm that can produce all words, you just enumerate them each one and verify that the word satisfies both predicates. An intelligent agent should be able to understand this without any understanding of the underlying domain.

If I try to reformulate your answer, this is like words are a different domain than what GPT has been trained on (which is tokens), and so it is not unexpected that GPT cannot generalize to this other domain.

Is that a correct interpretation of your statement?

In my view failing to see the similarity in the relationship between class objects is a sign of a lack of 'intelligence', whatever that may be in a very precise sense.

>In my view failing to see the similarity in the relationship between class objects is a sign of a lack of 'intelligence', whatever that may be in a very precise sense.

Ok but not all relationship similarities transfer across representations like that.

It's like you give an instruction in English, "Find a word that rhymes with ingenious" to a french man. The french man sees, "Trouver un mot qui rime avec ingénieux". Guess what ? His response to "Trouver un mot qui rime avec ingénieux" would not fulfill the request in english that it might in French.

Ingenious and heterogeneous rhyme. ingénieux and hétérogène have no such property

The french man is not unintelligent to be unable to fulfill your request.

I’ve yet to see an llm score well on non-verbal iq tests like https://www.creativeorgdesign.com/tests/non-verbal-iq-test/

(I agree with you.)

If it's so smart already, why hasn't it replaced the most basic functionality in human society yet? Like, can a GPT4 drive a car because most people can drive a car.

Can a blind man drive ? are blind people unintelligent ?

The "basic functionality" may just be more complicated than intelligence. Intelligence and language are relatively new capabilities built on top of older, better honed ones, so it shouldn't necessarily come at a surprise that computers get smart before they can properly deal with the physical world.

Because we haven't hooked it up to visual input yet?

GPT4 is multimodal and supports visual input.

I meant the >20fps of continuous motion image input that would be required to drive safely in real-time.

Any artificial neural net has a finite set of inputs and a finite set of outputs. That makes it equivalent to some lookup table. You must therefore claim a lookup table has the ability to reason.

Not even going to bother with the weird leap of logic you've made here but Memory augmented LLMs are computationally universal - https://arxiv.org/abs/2301.04589

Then I won’t bother with your citation that says if you add infinite memory you can achieve infinite states :)

infinite memory ? guess you can't read then. Well you do you.

And what, humans are made out of an infinite number of neurons?

One neuron has an infinite number of possible states (as far as we know).

We don't know that in any way that is relevant. A biological neuron is complex and made of lots of elements where very fine interactions can plausibly have effect, so in a sense, there is a combinatorially infinite space of neuron-states, but there's no evidence its upper-level computational function that leads to learning and producing adaptive outputs in response to inputs is not discrete. Indeed it seems that most of the complexity [that isn't just "piping" for life functions – don't forget these things aren't semiconductors in metallic sheets fabricated by TSMC, they have to migrate to their sites and branch out on their own, they consume, they excrete, they function for many decades!] is redundant and serves to compensate for various biological shortcomings like low transmission speed, limited bandwidth, noise and stochasticity, developmental abnormalities, cell death etc.

Analog computers also don't have infinite states, because of the noise floor.

A synapse can encode at most some low tens of states[1], synapses are discrete, spiking is discrete, receptor density is discrete, each neurotransmitter release is made up of discrete number of vesicles with a discrete number of molecules, even epigenetic expression is discrete. That it's not all perfectly algorithmically executed is not key to the function being performed – and anyway, at the limits of high-performance compute we also start to deal with esoteric processes, and ways of suppressing them. We didn't design the brain nor do we have perfect ways of observing it in vivo, so we can't always neatly distinguish the wheat and the chaff; but this cannot be seriously taken as a cause to think it's all wheat. Non-human animals show that it's not magic either; even apes, with brains architecturally almost (not wholly) identical to ours but smaller, cannot learn any of the hard symbol-manipulating stuff that we or later GPTs can and that we colloquially refer to as intelligent behavior and reward economically. This, I think, is a very strong prior for that sort of intelligence not being dependent on some low-level expressivity of biological neural computational substrate.

Penrose-Hamerroff's "quantum effects in microtubules" thesis is crank science not supported by any direct evidence.

1: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5247597/

GPT-4 thinks you should provide a citation:

==

Me: Is it true that, in humans, one neuron has an infinite number of possible states (as far as we know)?

GPT-4: It is not accurate to say that a single neuron in humans has an infinite number of possible states. Neurons are complex cells that can exhibit a wide range of behaviors and responses, but the number of states is not infinite.

Neurons communicate with each other through electrical and chemical signals, and their primary functional unit is the synapse, where communication between neurons occurs. The strength and patterns of these connections can vary, resulting in a large number of potential states for a single neuron. However, this number is still finite, albeit extremely large.

Moreover, the activity of a neuron can be described in terms of its membrane potential, which is a continuous variable ranging from hyperpolarized to depolarized states. While there are many possible values for the membrane potential, it is still a bounded range, not infinite.

So, while the number of possible states for a single neuron is vast and complex, it is not infinite. The complexity and diversity of neuronal states contribute to the rich repertoire of cognitive and behavioral functions that humans possess.

That implies a lookup table requires me to provide a citation :) In fact, assuming space is not discrete, not only can a neuron be in any of infinite states, per quantum mechanics it can be in multiple at the same time.

The lookup table says I should tell you that quantum mechanics applies to subatomic particles, not very large objects like neurons.

(Would it bother you to be corrected by a lookup table?)

How does that hold in the case that connections among neurons exchange state information via subatomic particles?

Schrödinger's cat will be so relieved!

quantum mechanics ? you think that is applicable for neurons ?

Neurons apparently contain microscopic tubes that make use of some quantum effects:

https://phys.org/news/2014-01-discovery-quantum-vibrations-m...

The user was implying neurons could be in multiple states at the same time because...quantum mechanics. Wrong conclusion even from this link.

Since we're providing citations :) https://iopscience.iop.org/article/10.1088/2399-6528/ac94be

That doesn't mean that neurons could be in multiple states at the same time which is what you were implying. That particular assertion is still horribly wrong.

'...the assumed brain function producing entanglement, must use a kind of quantum distillation process on mixed states.'

again, that doesn't mean neurons can exist in multiple states. There is also no also indication higher level computational functions in the brain responsible for reasoning or whatever have you require any quantum computations.

But can you distinguish those states reliably?

That clearly isn’t true, since a neuron that could be in infinite states would thereby be capable of storing infinite information, and (as the neuron itself is finite in size) have infinite information density, but there is an established physical upper limit on information density.

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

Mm not sure that bound applies because neurons are neither of fixed size nor fixed energy. But on the other hand one starts to stretch the definition of neuron.

> Mm not sure that bound applies because neurons are neither of fixed size nor fixed energy

Neurons are not of unbounded size nor energy.

What’s the bound?

Can you not work this out yourself? Use some common sense. Do you disagree, are you saying that animal body cells can be infinitely large?

We hit comment cooldown but it's not foolery exactly, more like reflecting the futility of trying to make these kinds of arguments. The problem with this whole chain is we haven't actually defined what a neuron is. Take the purported bound above. Suppose I move an associated potassium ion farther away. Have I not changed the state? If yes, what's the limit to how far away I can move that ion and have it still count as part of the neuron? That's just one of the limits of physical laws, you can make very precise statements, but only under very narrow constraints.

Here are some bounds, from most theoretical to most practical:

The size of the earth's biosphere

The size of the largest animal.

The size of a human skull cavity.

The size of a human skull cavity, divided by the number of neurons therein ( a few Billion).

Is this not obvious? There's nothing futile about stating these obvious facts.

Do not fool around with "if I move a potassium ion farther away, isn't that a different bound" ? correct, but moving the bound slightly, is irrelevant to the obvious statement that there are still bounds, and they are practical.

Stating that the neuron is not well-defined is similar irrelevance, if you want to precisely measure the bound, you will need to know precisely what is bounded, but I did not, merely stated the obvious truth that every such definition will come with a bound. One can say "definitely smaller than this" in a wide range of ways, without defining the exact edge.

What's the alternate argument: "I won't define it, therefor it might be larger than the solar system". This is not a serious thing to say.

And to think that such bounded systems "have infinite states would thereby be capable of storing infinite information" is pseudo-mystical foolery. Large, or even "vast", is not infinite. And as sibling comment has mentioned, there is a "noise floor" below which analog resolution means nothing.

No bound, got it.

Which animal body cells can be infinitely large then? This is foolery. "Neurons are of unbounded size" is a ridiculous statement, we any fool can see it's false. IDK what you're aiming at here, try making a case for something sensible?

One of the reasons I think LLMs are somehow analogous to brains is that they scale based on size. This seems true for animal brains as well.

But you seem to be suggesting that all animal brains are equally powerful, because they have infinite neuron states, quantum superpositioned neurons, etc.

As far as I can tell, you seem to be suggesting that my old cat’s brain has the same complexity as my brain - right?

So, I’m interested in understanding why my cat is demonstrably stupider than I am, despite having infinite complexity, and why whatevrr you come up with doesn’t also apply to LLM size.

No, I’m saying having a finite number of neurons doesn’t imply a particular number of brain states. Scaling laws actually don’t apply very strictly to brain size. For example, whales have very large brains but aren’t generally thought to be more capable of reasoning than humans. Women generally have smaller brains than men but aren’t thought to be less capable of reasoning.

> No, I’m saying having a finite number of neurons doesn’t imply a particular number of brain states.

But of course it does imply that. It's vast but necessarily finite number of "brain states" however that's defined, which is itself a subset of the vast but also finite number of states of the finite number of component particles.

Idk about “above average baseline”. Maybe, because quite a lot of people aren’t very smart. But even GPT4 struggles with complex logic problems, particularly ones with multiple steps, and when it makes logic errors it makes ones a human would spot. It also has trouble counting letters and words, though that’s largely due to its autoregressive nature. It has more knowledge and memory than any human, and I really think it’s emergent, but at its core it doesn’t seem very “intelligent”

>Idk about “above average baseline”

That's the minimum. It's far above average for most things.

>Maybe, because quite a lot of people aren’t very smart.

Lol okay..and ?

>But even GPT4 struggles with complex logic problems

So does everyone else

>and when it makes logic errors it makes ones a human would spot

people spot mistakes they're capable of making all the time

and so can GPT-4 - https://arxiv.org/abs/2303.11366 https://twitter.com/johnjnay/status/1639362071807549446

>It also has trouble counting letters and words, though that’s largely due to its autoregressive nature.

It sees tokens not words or letters and there aren't many instances of counting occurring in text so it doesn't get learnt.

>and I really think it’s emergent, but at its core it doesn’t seem very “intelligent”

and we're back to the trust me bro argument. It's not intelligent because you say it isn't not because it doesn't perform a great many tasks intelligently. if you say so

Why does the studious corn snake appear to have claws and whiskers?

Not just claws, but feet! I also like the ouroborous nature of the snake becoming the scroll, or perhaps being created by its own writing.

(No, I don't know why.)

One of my big issues with these computational artifacts is that they are not modular -- in the sense that, if I wanted a studious snake without claws -- what things do I need to do to get it to produce a version of the snake, but "fixed" in the way which I describe.

E.g. here, when I say modular -- I mean that a "fix" can't be implemented in a local, small way. I need to try again with a new prompt, etc -- and it's not clear to me how to "fix" my prompt to get the right thing.

one way to support this sort of modularity might be with a GPT-4 / langagent integration which would query Midjourney until it gets a picture which satisfies my criteria, changing the prompt, etc.

The problem is that, if the integration or langagent has an issue -- how do I modularly fix those systems? And so on.

This sort of question "how do I figure out how to fix this prompt-based thing" makes me worry about a sort of "turtles all the way down" problem. Okay, so maybe I don't need to understand exactly how to use these systems to get what I want -- just let another LLM do that ... but what if that LLM has an issue ... and what if the LLM which is prompting that LLM has an issue ...

It concerns me. I'm not sure how well founded my concerns really are.

GPT-4 does fine with a reply prompt of "keep the rest as-is but fix this part". I imagine Midjourney would too if it supported conversational prompting.

> I mean that a "fix" can't be implemented in a local, small way

inpainting.

Great point, I'll think deeper!

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