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Large models of what? Mistaking engineering achievements for linguistic agency

arxiv.org

91–100 of 162 posts

Re: Large models of what? Mistaking engineering achievements for linguistic agency

#91
There is a lot of frustration here over what appears to be essentially this claim:

> ...we argue that it is possible to offer generous interpretations of some aspects of LLM engineering to find parallels with human language learning. However, in the majority of key aspects of language learning and use, most specifically in the various kinds of linguistic agency exhibited by human beings, these small apparent comparisons do little to balance what are much more deep-rooted contrasts.

Now, why is this so hard to stomach? This is the argument of this paper. To feel like this extremely general claim is something you have to argue against means you believe in a fundamental similarity between what our linguistic agency and the model. But is embodied human agency something that you really need the LLMs to have right now? Why? What are the stakes here? The ones actually related to the argument at hand?

This ultimately not that strong of a claim! To the point that its almost vacuous... Of course the LLM will never learn the stove is "hot" like you did when you were a curious child. How can this still be too much to admit for someone? What is lost?

It makes me feel little crazy here that people constantly jump over the text at hand whenever something gets a little too philosophical, and the arguments become long pseudo-theories that aren't relevant to argument.

Re: Large models of what? Mistaking engineering achievements for linguistic agency

#92
Why assume you "know" what language is? Like there is a study backed insight on the ultimate definition of language? it's the same as saying "oh, it's not 'a,b,c' its 'x,y,z'", which makes you as dogmatic as the one you critique. This is absurd.

Re: Large models of what? Mistaking engineering achievements for linguistic agency

#93

I'm more or less a layperson when it comes to LLMs and this nascent concept of AI, but there's one argument that I keep seeing that I feel like I understand, even without a thorough fluency with the underlying technology. I know that neural nets, and the mechanisms LLMs employ to train and form relational connections, can plausibly be compared to how synapses form signal paths between neurons. I can see how that make…

There isn’t really any reason biological neurons should relate to their modelled reality, what does a single cell care about poetry or even simple things like a chair?

A chair isn't only a chair, it can be a table, a bookshelf, and many others things. The real world is hard.

Re: Large models of what? Mistaking engineering achievements for linguistic agency

#94

Earlier quoted context omitted.

>> Again, that conclusion feels wrong to me... but if I'm being honest with myself, I can't point to why, other than to point at some form of dualism or spirituality as the escape hatch. I like how Chomsky deals with it who doesn't have any spirituality at all, the big degenerate materialist: As far as I can see all of this [he's speaking about the Loebner Prize and the Turing test in general] is entirely pointless.…

I can't disagree more. Or maybe I actually agree. Because it's not easy to tell whether something is flying. Definitions like that fall apart every time we encounter something out of the ordinary. If you take the criterion of "there's no discussion about it", then you're limiting the definition to that which is familiar, not that which is interesting. Is an ekranoplan flying? Is an orbiting spaceship flying? Is a hov…

"It's not flying, it's falling... with style"

Re: Large models of what? Mistaking engineering achievements for linguistic agency

#95
post #38

I am highly skeptical of LLMs as a mechanism to achieve AGI, but I also find this paper fairly unconvincing, bordering on tautological. I feel similarly about this as to what I've read of Chalmers - I agree with pretty much all of the conclusions, but I don't feel like the text would convince me of those conclusions if I disagreed; it's more like it's showing me ways of explaining or illustrating what I already belie…

> sufficiently advanced mimicry is not only indistinguishable from the real thing, but at the limit in fact is the real thing I am continually surprised at how relevant and pervasive one of Kurt Vonnegut’s major insights is: “we are what we pretend to be, so we must be very careful about what we pretend to be”

This ideas is older than him by a lot

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

Everyone in the "life imitates art, not the other way around" camp (and also neo-platonists/gnostics i.e. https://en.wikipedia.org/wiki/Demiurge ) is getting massively validated by the modern advances in AI right now.

Re: Large models of what? Mistaking engineering achievements for linguistic agency

#96
post #7

That's a lot of thinking they've done about LLMs, but how much did they actually try LLMs? I have long threads where ChatGPT refine solutions to coding problems. Their example of losing the thread after printing a tiny list of 10 philosophers seems really outdated. Also it seems LLMs utilize nested contexts as well, for example when it can break it' own rules while telling a story or speaking hypothetically.

Most LLM critics (and singularity-is-near influencers) don't actually use the systems enough to have relevant opinions about them. The only really good sources of truth is the chatbot-arena from lmsys and the comment section of r/localllama (I'm quoting Karpathy), both are "wisdom of the crowd" and often the crowd on r/localllama is getting that wisdom by spending hours with one hand on the keyboard and another under their clothes.

Re: Large models of what? Mistaking engineering achievements for linguistic agency

#97

Earlier quoted context omitted.

That is not an example of a LLM being capable of abstract reasoning. Changing the question from "What is the capital of United States?" which is easily answerable to something completely abstract and "not in the training model" doesn't change that LLM's are just very advanced text prediction, and always will be. The nature of their design means they are incapable of AGI.

The question I gave is a literal textbook example of abstract reasoning. LLMs are just very advanced text prediction, but they are also provably capable of abstract reasoning. If you think that those statements are contradictory, I would encourage you to read up on the Bayesian hypotheses in cognitive science - it is highly plausible that our brains are also just very advanced prediction models.

Pleasure and pain, along with subtler emotions that regulate our behavior, aren't things that arise from word prediction, or even from understanding the world, I don't think. So to say human brains are just prediction models seems like a mischaracterization.

Re: Large models of what? Mistaking engineering achievements for linguistic agency

#98
post #86

Earlier quoted context omitted.

I don't think anyone in research actually believes this. Note that the whole idea behind claiming "scaling laws" will infinitely improve these models is a funding strategy rather than a research one. None of these folks think human-like consciousness will "rise" from this effort, even though they veil it to continue the hype-cycle. I guarantee all these firms are desperately looking for architectural breakthroughs, e…

Put aside consciousness or hype or investment. Look at the results; LLMs are well beyond old-school search in many ways. Sure, they are flawed in someways. Previous paradigms for search, were also flawed in their own ways. Look at the arc of NLP. Large language models fit the pattern. One could even say that their development (next token prediction with a powerful function approximator) is obvious in hindsight.

Honestly I don't disagree, I just think that humans tend to anthropomorphize to such a high extent that there is a fair bit of hyperbole promoting LLMs as more than they are. It's my opinion that the big flaws LLMs currently present aren't going to be overcome by scaling alone.

Re: Large models of what? Mistaking engineering achievements for linguistic agency

#99

Earlier quoted context omitted.

If you really want to phrase it that way, organisms like us are "just" distributions of genes that have been pushed this way and that by natural selection until they converged to something we consider intelligent (humans). It's pretty clear that these optimisation processes lead to emergent behaviour, both in ML and in the natural sciences. Computability theory isn't really relevant here.

I don't even know where to begin to address your confusion. Without computability theory there are no computers, no operating systems, no networks, no compilers, and no high level frameworks for "AI".

Well, if you want to address my "confusion" then pick something and start there =)

That is patently false - most of those things are firmly in the realm of engineering, especially these days. Mathematics is good for grounding intuition though. But why is this relevant to the OP?

Re: Large models of what? Mistaking engineering achievements for linguistic agency

#100

Earlier quoted context omitted.

>> Again, that conclusion feels wrong to me... but if I'm being honest with myself, I can't point to why, other than to point at some form of dualism or spirituality as the escape hatch. I like how Chomsky deals with it who doesn't have any spirituality at all, the big degenerate materialist: As far as I can see all of this [he's speaking about the Loebner Prize and the Turing test in general] is entirely pointless.…

I can't disagree more. Or maybe I actually agree. Because it's not easy to tell whether something is flying. Definitions like that fall apart every time we encounter something out of the ordinary. If you take the criterion of "there's no discussion about it", then you're limiting the definition to that which is familiar, not that which is interesting. Is an ekranoplan flying? Is an orbiting spaceship flying? Is a hov…

There are going to be gray areas of course, but the point I'm making is that if it's hard to argue something isn't flying (respectively, intelligent) then it's probably flying (resp. intelligent). If it's hard to tell then it's probably not. I'm suggesting that intelligence, like flying, should be very immediately obvious.

For example, you can't miss the fact that a five-year old child is intelligent and you can't miss the fact that a stone is not. There may be all sorts of things in between for which we can't be sure, or whose intelligence depends on definition, or point of view, etc. but when something is intelligent then it should leave us no doubt that it is. Or, if you want to see it this way: if something is as intelligent as a five-year old child then it should leave us no doubt that it is.

I'm basically arguing for placing the bar high enough that when it is passed, we can be fairly certain we're not mistaken.

>> I can't disagree more. Or maybe I actually agree.

I find myself in that disposition often :)

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