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 (classical 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"
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 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.
It's not possible for me to believe it is anything else.
you should try to work on your limitations..
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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.
(Would it bother you to be corrected by a lookup table?)
https://phys.org/news/2014-01-discovery-quantum-vibrations-m...
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 vacuoles 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.
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.
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.
Neurons are not of unbounded size nor energy.
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.
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
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.
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)
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?
Sentences look something like this to model, [754 76 4752 67 456]
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.
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.
The french man is not unintelligent to be unable to fulfill your request.
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.
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)
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.
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.
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.
>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.
> 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.
(No, I don't know why.)
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.
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.
Related to creativity, here's a Midjourney image resulting from my prompting "a studious corn snake": https://chris.printf.net/a_studious_corn_snake.png
¹: via GPT-4:
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(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