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Theory of Mind May Have Spontaneously Emerged in Large Language Models

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Re: Theory of Mind May Have Spontaneously Emerged in Large Language Models

#311

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Does sentience need memory? I would say it's orthogonal. There are examples of people in the real world who only remember things for about 3 minutes before they lose it. They can't form any real memories. These people are still sentient despite lack of memory. See: https://www.damninteresting.com/living-in-the-moment/ If chatGPT was sentient, I would say it has nothing to do with the 4000 character limit. The 4000 ch…

I dont think of the 4000 tokens as its memory as such. Its more like the size of its thinking workspace

It also functions as memory in practice, though. With some complex tasks that can be broken down into steps, it often makes a big difference if you tell GPT to summarize its "understanding" of the current state as it goes through those steps - by repeating a phrase, keeping it within the token window, it effectively "remembers" something.

Re: Theory of Mind May Have Spontaneously Emerged in Large Language Models

#312

Earlier quoted context omitted.

> Something that is able to simulate having a theory of mind sufficiently well does actually have a theory of mind. That presupposes that our existing tools for detecting the presence of ToM are 100% accurate. Might it be possible that they are imprecise and it’s only now that their critical flaws have been exposed?

But if our understanding of ToM is so flawed in practice, what does it say about all the confident proclamations that AIs "aren't real" because they don't have it?

Your question aligns with the argument I'm trying to make which is: If it turns out that our understanding of ToM is wrong, should we be making proclamations about--whether for or against--the real-ness of our current AI implementations?

Re: Theory of Mind May Have Spontaneously Emerged in Large Language Models

#313
post #283
post #282

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> A few errors show quickly there is no such concept being weilded I would have given similar examples to show that ChatGPT makes the same kinds of mistakes that humans do. The first one is good, because ChatGPT can solve it easily when you present it as a riddle rather than being a genuine question. Humans use context and framing in the same way; I'm sure you've heard of the Wason selection task: https://en.wikipedi…

>>trick the reader into thinking of the number 30 by putting the phrase "half my age" before the number 60 Yet it is exactly the process of conceptualizing "half" and applying it to "at six years old" instead of "of 60" that is the key to solving it. These things aren't abstracting out any concepts, they only operate at the level of "being fooled by" semantics. The fact that humans sometimes fail this way gives us li…

You might like some of the work being done under the label "Factored Cognition". It's an approach that treats LLMs as building blocks instead of being complete AIs. Instead of asking the LM to solve a problem directly in one pass, you ask it to divide the problem between several different virtual copies of itself, which then themselves subdivide further, and so on until each subtask is small enough that the LM can solve it directly. For this to work the original problem needs to be acyclic and fairly tree-like, i.e., not something that requires having a sudden "Eureka!" moment to solve.

But I've only seen this done with a single model. Sometimes it gets prompted to act like a different agent in different contexts, or given API access to external tools, but it's still just one set of weights.

Re: Theory of Mind May Have Spontaneously Emerged in Large Language Models

#314

Earlier quoted context omitted.

> In the end It fully imagined a bash shell, an imaginary internet, an imaginary chatGPT on the imaginary internet, then on the imaginary chatGPT it created a new imaginary bash shell. In the general case, a shell is merely a particular prompt-response format with special verbs; the internet is merely a mapping from URLs to HTML and JSON documents; those document formats are merely particular facades for presenting i…

>Uh, what?... Your attempt to trivialize it doesn't make any sense. It's like watching someone try to trivialize the moon landing. "Oh all we did was put a bunch of people in some metal cylinder then light the tail end on fire. Boom simple propulsion! and then we're off to the moon! You don't need any intelligence to do that!" >I'm saying that it "understands" your query only insofar as its words can be tied to the w…

> If this is what you mean by "reasoning by analogy" then I hate to tell you this, but "reasoning by analogy" is "reasoning" in itself. There's really no form of reasoning beyond associating things you already know. Think about it.

What's special about humans is that we can obtain an understanding of what chains of associations to make and when, to achieve the goal at hand, even without being told which method to use. We know when to do arithmetic, trace a program, decipher someone else's thoughts, etc. Also, we know to resort to a fallback method if the current one isn't working. We can assist models with this process in the special case (e.g., that tool-using model), but I suspect the general case will remain elusive for a while yet.

That is to say, I'll grant you that associations can act as a primitive operation of intelligence, much as metal cylinders and flames are primitive parts of a rocket, but I suspect that making a LLM "generally intelligent" or "sentient" will be far harder still.

> The other thing is, I feel it knows math as well as some D student in highschool. Are you saying the D student in highschool can't understand anything? No. So you really can't use this logic to dismiss LLMs because PLENTY of people don't know math well either, and you'd have to dismiss them as sentient beings if you followed your own reasoning to the logical conclusion.

I was just using that as a specific example of the general issue: it doesn't notice that its answer is wrong and its particular method can never work, and it refuses to try a meaningfully different method (no matter how much I prompt it to). Its immediate mistakes might look similar to those of a poor student, but I suspect they come from a different underlying problem. (After all, the student has seen perhaps a thousand algebra problems at most, whereas the model has seen millions and millions. Also, the student often )

> What's impossible here is to flip your bias. You and others like you will still be naysaying LLMs even after they take your job.

You have me wrong: I'm not saying that augmenting LLMs can't make them reliable enough to take over some people's jobs. But I am disputing that LLMs alone will produce AGIs capable of outwitting any human, taking over the world, advancing the limits of math and science, or many of those other grandiose claims.

Anyway, I'm not trying to be particularly stubborn about this like some people are; I'm keeping a close eye on the space. But I'll only believe it when I see it (and no later), and I don't think I've quite seen it yet.

Re: Theory of Mind May Have Spontaneously Emerged in Large Language Models

#315
post #313
post #283

Earlier quoted context omitted.

>>trick the reader into thinking of the number 30 by putting the phrase "half my age" before the number 60 Yet it is exactly the process of conceptualizing "half" and applying it to "at six years old" instead of "of 60" that is the key to solving it. These things aren't abstracting out any concepts, they only operate at the level of "being fooled by" semantics. The fact that humans sometimes fail this way gives us li…

You might like some of the work being done under the label "Factored Cognition". It's an approach that treats LLMs as building blocks instead of being complete AIs. Instead of asking the LM to solve a problem directly in one pass, you ask it to divide the problem between several different virtual copies of itself, which then themselves subdivide further, and so on until each subtask is small enough that the LM can so…

Hmm, that sounds like a nod in the right direction, but a rapid initial skim maybe indicates that it's more parallelizing the problem than abstracting it. I've got to read more about it - thanks!

While Minsky & Papert's book on Perceptrons was enormously destructive, I think there is something to their general concept of Society Of Mind, that multiple sub-calculating 'agents' collude to actually produce real cognition.

We aren't doing conscious reasoning about the edges detected in the first couple layers of our visual cortex (which we can't really even access, 'tho I think Picasso maybe could). We're doing reasoning about the concepts of the people or objects or abstract concepts or whatever many layers up. The first layers are highly parallel - different parts of the retina connecting to different parts of the visual cortex, and then starting to abstract out edges, zones, motion, etc. and then synthesize objects, people, etc.

I think we need to take a GPT and a Stable Diffusion and some yet-to-be-built 3D spatial machine learning/reasoning engine, and start combining them, then adding more layer(s) synthesizing about that, and maybe that'll get closer to reasoning...

Re: Theory of Mind May Have Spontaneously Emerged in Large Language Models

#316
post #117

Earlier quoted context omitted.

Defining what "knowing" is would be useful, yes, and analytic philosophers in epistemology do argue about this. One attribute that's classically part of the definition of "knowing" is that the thing which is known must be true. LLMs are pretty bad at this, but perhaps that can be fixed. But I would challenge you to imagine the situation the LLM is actually in. Do you understand Thai? If so, in the following, feel fre…

Ok I like your thought experiment. Lets change it a bit. Instead of it being an unknown language, its English (a language you know), but every single Noun, Verb, Adjective or Preposition has been changed to Thai (a language you dont know). The Mæw Nạ̀ng Bn the S̄eụ̄̀x. If you had sufficient opportunity to study this pile of text, you'd begin to pick out patterns of which words appear together, and what order words of…

I love this theory. You're saying that a distinction can be drawn between our linguistic concepts and our lived experience, and that the former can be learned without the latter. And that a model could operate upon those linguistic concepts in a useful way, but without the benefit (or drawback?) of the mappings we keep between language and experience. And that it can learn this based on the large amount of texts we have.

Fascinating, and seems like a plausible description of what's going on.

Re: Theory of Mind May Have Spontaneously Emerged in Large Language Models

#317
post #118

Earlier quoted context omitted.

The problem with this facile view of things is that it seems to be a dead end for scientific theories. What if we just limited the science of birds to explaining how limb-flapping could produce levitation? Hmm yes. Birds are kind of like helicopters, it seems. Who’s to say that they are not basically one and the same? Moving on. If you are only interested in the most superficial tests and theories—like the Turing Tes…

> If you are only interested in the most superficial tests and theories—like the Turing Test—then consider psychology conquered once you’ve tricked a human with your chat bot. What's the counterargument? What's a less superficial test that we can use instead, which conclusively shows that actually human minds aren't just like very sophisticated LLMs? There isn't one -- this is nothing but the same Chinese room proble…

> (though in general I think the favored “alignment” frames of the LessWrong community are not even wrong).

The Turing Test doesn’t test humans. So you cannot use it to show any properties about humans.

Next!

> The topmost poster is simply assuming that language models can't possibly understand the same way a human does without relying on any kind of "test" at all, which I think is the real scientific dead end here.

Sounds unfalsifiable. So yes.

Re: Theory of Mind May Have Spontaneously Emerged in Large Language Models

#318

Earlier quoted context omitted.

The problem with this facile view of things is that it seems to be a dead end for scientific theories. What if we just limited the science of birds to explaining how limb-flapping could produce levitation? Hmm yes. Birds are kind of like helicopters, it seems. Who’s to say that they are not basically one and the same? Moving on. If you are only interested in the most superficial tests and theories—like the Turing Tes…

The question being asked was "what it would mean under this definition of knowing things for a machine learning algorithm to ever know something". Aside from your answer being rude, it's also unhelpful in that it doens't address the question asked and instead relies on reductio ad absurdum to pretend to make a point. If you'd like to take a crack at a helpful answer, perhaps educate us all on what it WOULD take for y…

> Aside from your answer being rude, it's also unhelpful in that it doens't address the question asked and instead relies on reductio ad absurdum to pretend to make a point.

That is indeed often the kind of answer that a philosophical question deserves.

> If you'd like to take a crack at a helpful answer, perhaps educate us all on what it WOULD take for you to consider a NN to actually "know" something in the same way that we say a human or other sentient animal does.

How many angels can dance on the head of a pin?

Re: Theory of Mind May Have Spontaneously Emerged in Large Language Models

#319
post #132

Earlier quoted context omitted.

The problem with this facile view of things is that it seems to be a dead end for scientific theories. What if we just limited the science of birds to explaining how limb-flapping could produce levitation? Hmm yes. Birds are kind of like helicopters, it seems. Who’s to say that they are not basically one and the same? Moving on. If you are only interested in the most superficial tests and theories—like the Turing Tes…

How is the request that someone provide a clear set of definitions and some empirically falsifiable hypotheses a "dead end for scientific theories"? It seems more like the foundation of the scientific method.

> and some empirically falsifiable hypotheses

Where?

Re: Theory of Mind May Have Spontaneously Emerged in Large Language Models

#320
post #116

Earlier quoted context omitted.

The problem with this facile view of things is that it seems to be a dead end for scientific theories. What if we just limited the science of birds to explaining how limb-flapping could produce levitation? Hmm yes. Birds are kind of like helicopters, it seems. Who’s to say that they are not basically one and the same? Moving on. If you are only interested in the most superficial tests and theories—like the Turing Tes…

> The problem with this facile view of things is that it seems to be a dead end for scientific theories. You're overreaching quite a bit here, or I think you're misinterpreting what Parent said. I interpreted what they said as: it seems the difference in how we "know" something vs how an LLM "knows" something might actually be closer than some suspect. this certainly is not an "end of science".

Not the end. Just the exact opposite attitude that an inquisitive and humble scientist should have. So unlikely to go anywhere.

A “scientist” looks out at his living room. My Roomba and my cat have their own lifes. Who’s to say that they are not in fact the same in kind (but not degreee)? Good luck with that, professor.

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