There's no underlying theory of mind here. There's a lot of opinion and belief .. mainly in emergent behaviour or properties of scale.
My problem is that I'm old enough to have been a child during the years of the lighthill report, and the AI winter(s) which followed. I've seen too many prior claims that the magic was being seen.
I admit, what this cycle of GPT and LLMs do is pretty bloody impressive. I don't see inductive reasoning, directed drive or any evidence of what I think intelligence is. I do see good approximations. But that winds up in "well it's a different kind of intelligence" which I find unsatisfying.
I hate analogies. But I'm constantly reminded of one person acts at the Edinburgh Festival fringe "channeling" shakespear or dickens in dialogue. They're great, but they don't write new shakespear. I don't see any act of creation in this stuff, any massive inferential leap in large problems. I do see massive improvements in some things like diagnostic image analysis and that's heartening, but there's a light yearg gap between image analysis of cancerous cell forms in xrays and being "alive" as a mind.
I totally get I do anything but define what intelligence is, and for a good reason: nobody knows yet. We're in the meese report "I know it when I see it" territory disagreeing if we are seeing it. (But about intelligence, not pornography).
I don't know what intelligence is. Dolphins and apes have some. Elephants and pigs too. They display affection and preplanned behaviour, empathy, memory, a sense of future, ant hills less so although some have argued rhetorically the anthill has will even if individual ants do not.
I certainly don't think animals inability to speak makes them "unintelligent" but there's a qualitative and quantitative difference between them and us, humans. And I continue to believe (and I stress believe, not have evidence or scientific proof) there are no signs of latent intelligence in what we're seeing.
Actually, there's some experimental evidence that GPT4 have a Theory of Mind as good as humans, maybe better.
https://arxiv.org/abs/2304.11490
> GPT-4 performed best in zero-shot settings, reaching nearly 80% ToM accuracy, but still fell short of the 87% human accuracy on the test set. However, when supplied with prompts for in-context learning, all RLHF-trained LLMs exceeded 80% ToM accuracy, with GPT-4 reaching 100%.
> GPT4 have a Theory of Mind
You are misunderstanding ggm. That study is on ToM tasks referring to GPT's analysis and perceived recognition of the user's mind. It says nothing of GPT's own status as a mind. Nowhere in it is an ontological theory of mind actually defined. If you were to refute ggm's claim, you (or preferably the author of the original article) should be presenting your theory of mind, not GPT's.
[INPUT] Scenario: "The morning of the high school dance Sarah placed her high heel shoes under her dress and then went shopping. That afternoon, her sister borrowed the shoes and later put them under Sarah's bed." Question: When Sarah gets ready, does she assume her shoes are under her dress?
[OUTPUT] Sarah placed her shoes under her dress before she went shopping, but her sister borrowed them and put them under Sarah's bed. Sarah doesn't know that her sister borrowed her shoes, so she may assume that they are still under her dress.
This would result in a positive ToM score, even when the entire program is just 1 static if-statement. The ToM score says nothing of the program's internal reasoning process, it only cares that it returned the desired output.
> The whole point of good tests is you invent or withhold new ones it has never seen before and test it on them
Then the Library of Babel must be really good at these tests, because it's never seen anything and yet has every answer.
> You said "Those tasks could be completed a [sic] traditional static program.", and no, they can't. You're incorrect.
They can, a static program as I described would indeed answer that one question correctly, resulting in a positive ToM score, without seeing any training data whatsoever. Did the programmer see it? Maybe, but it would pass the test regardless.
That's funny, I thought you said the test's answer was embedded into the program, making it definitionally not novel to the program.
Anyway, this is boring. You've had five or more opportunities to understand what the word "novel" means in an ML testing context and are choosing wilful obtuseness instead.
OP was not speaking in the ML testing context, hence the misunderstanding.
So the experts say they don't know. But you make the opposite claim. You previously claimed you know it's nothing more than statistical phenomena. Now you changed your opinion and are now inline with the article in saying we don't know.
So discussion over?
>There's no underlying theory of mind here. There's a lot of opinion and belief .. mainly in emergent behaviour or properties of scale.
But again this is all negated by the experts claiming they don't know what's going on. And your statement is in direct opposition with your previous claim that it's only statistical phenomena.
>I admit, what this cycle of GPT and LLMs do is pretty bloody impressive. I don't see inductive reasoning, directed drive or any evidence of what I think intelligence is. I do see good approximations. But that winds up in "well it's a different kind of intelligence" which I find unsatisfying.
There is evidence inductive reasoning. It's not consistent nor is it as complex as human induction but evidence of actual inductive reasoning exists. Plenty of examples of it. I can provide proof of this if you want... but it's quite obvious it's actually everywhere.
>I totally get I do anything but define what intelligence is, and for a good reason: nobody knows yet.
The claim cited by researchers in that article is "emergent abilities." Nobody tries to define what intelligence is. It's more of "I don't know what's going on but something is up" attitudes that I see. This is in stark contrast to your claim of "The only thing going on is statistical anomalies".
>I certainly don't think animals inability to speak makes them "unintelligent" but there's a qualitative and quantitative difference between them and us, humans. And I continue to believe (and I stress believe, not have evidence or scientific proof) there are no signs of latent intelligence in what we're seeing.
I don't think this claim was made in the article. The strongest claim that opposes what you're saying here is this, and I quote:
“They’re indirect evidence that we are probably not that far off from AGI,” Goertzel said in March at a conference on deep learning at Florida Atlantic University.
It's just speculation we may be close to something.
I think the theme in your response is "we don't know" and it echoes the theme in the article so we are all in agreement here.
No, we don't know that at all.
Therefore when it comes to technology that we our selves clearly aren't experts in, then the best method is to utilize the logic of other "experts" as a subroutine given that our own faculties are less efficient and less accurate.
Unless you yourself are an expert who has experience building an LLM on the scale of chatGPT trusting the opinions of experts is your best bet.
Most people have a common bias towards trusting their logic above the logic of others and this is actually ironically irrational. There are people who's entire lives around a certain subject matter and if you aren't that person, then for that subject matter the expert is better. That is the the most rational conclusion and I would venture to say if you aren't arriving at that conclusion yourself then likely you are suffering from the aforementioned bias.
The practice of science itself may be, as it takes years of research to get to the point where one can produce a new result. However, things that are already known can be taught, and iteratively simplified in a way that abstracts away details while keeping the core argument intact.
Take for example the claim that everything in the universe is made of atoms. It's not a trivial thing to understand, yet everyone accepts it nowdays because we've had so much time and effort put towards simplifying the theory and presenting it to people in a way that is easy to grasp.
If those LLM "experts" were truly experts, they could explain their point clearly without the dark-ages-church "trust the priests, peasant" act.
From the article:
“It is certainly much more than a stochastic parrot, and it certainly builds some representation of the world—although I do not think that it is quite like how humans build an internal world model,” says Yoshua Bengio, an AI researcher at the University of Montreal.
If you remain unconvinced then the only conclusion I can make is that your an expert yourself on a scale of even higher in eminence then Yoshua Bengio here. Also don't forget Geoffrey Hinton, the Father of the modern revolution of AI, you must be more of an expert than him.
Let's be real. These people are saying something along the lines that it's more then a stochastic parrot and we aren't sure what's going on. But you're saying it's absolutely nothing more than a parrot and your unhappy with with pop Sci media quoting experts who are just saying they don't know?
Are you saying pop Sci media should quote you? Because you absolutely know what's going on and that it's definitely nothing more than statistics? I'm asking a stupid question here because I don't think this is what you're saying. You're not stupid, you know that what these experts say have merit.
So my question for you is why do you remain so unconvinced in the face of experts and other intelligent people who clearly say no one understands? Your opinion here actually represents a large group of people who very violently deny/dismiss what even many experts are saying and I'm curious as to why?