Earlier quoted context omitted.
> that so far isn't identified ... then I don't see what would be left except metaphysic So out of all "that so far isn't identified", there are just two classes of things: (i) biological and psychological processes; and (ii) metaphysics. This is some magic voodoo hand-waving here. Metaphysics seems to be just your way of saying "everything we don't know or understand".
I think "physical process" already covers a lot of ground besides metaphysics. My main distinction was that anything physical is governed by the laws of physics that we know and that are generally deterministic. In contrast, metaphysics would be something entirely out of our realm of reality - which was how consciousness has been generally seen for a long time. But what other possibility would you see? Edit: > (i) bi…
Talking About Large Language Models
121–130 of 158 posts
Re: Talking About Large Language Models
#122Re: Talking About Large Language Models
#123Earlier quoted context omitted.
How can these things not know what words mean? Did you not see how they created a virtual machine under chatGPT? They told it to imitate bash and they typed ls, and cat jokes.txt and it outputted things completely identical to what you'd expect. Look it up. https://www.engraved.blog/building-a-virtual-machine-inside/ I don't see how you can explain this as not knowing what words mean. It KNOWS.
LLMs are trained exclusively on text, which means they lack crucial context behind the meaning of sentences. The universe of information outside of pure text - vision, sound, etc is completely unknown to it. LLMs are basically the aliens in blindsight. They have a superhuman ability to memorize the context of words it has seen and generalize to new contexts, but it can never be perfect because it's working on incompl…
Unlike you?
Re: Talking About Large Language Models
#124Earlier quoted context omitted.
LLMs may be overhyped, but transformers in general are under hyped. LLMs make a lot of mistakes because they don't actually know what words mean. The key thing is though - it's much harder to generate coherent text when you don't know what the words mean. In a similar vein it's completely unreasonable to expect an LLM to perform visual tasks when it literally has no sense of sight. The fact that it can kind of sort o…
How can these things not know what words mean? Did you not see how they created a virtual machine under chatGPT? They told it to imitate bash and they typed ls, and cat jokes.txt and it outputted things completely identical to what you'd expect. Look it up. https://www.engraved.blog/building-a-virtual-machine-inside/ I don't see how you can explain this as not knowing what words mean. It KNOWS.
Re: Talking About Large Language Models
#125Earlier quoted context omitted.
Well, I agree that it's amazing - it almost always produces grammatical output, for instance. But it's not a reliable way of obtaining knowledge. One should not, in particular, try to learn about biology by asking ChatGPT questions. It often produces made-up stuff that is just wrong. And it's very confidently wrong, with the output often coming across like someone barely concealing their contempt that you might doubt…
My wife (a physician) asked it multiple medical questions and the majority of the time they were dangerously wrong, but looked perfectly fine to me. I asked it a series of questions about my area of expertise and they were wrong but looked perfectly fine to my wife. It even confidently “solved” the 2 generals problem with a solution that looks completely plausible if you don’t already know that it won’t work.
All the answers looked good, used several of the correct terms, and one even referenced the project I worked on, but they just contained flat out wrong information.
You reach a point, where when you ask a ML model to generate text given the internet as a corpus, where there just isn't enough text to make something that is both true and convincing. In niche fields, this is just where we are at.
Re: Talking About Large Language Models
#126Earlier quoted context omitted.
bias infests research as well as seen by the replication crisis. So you being a researcher doesn't give more credence to your words especially given that the state of current research cannot fully comprehend what these ML models are doing internally. I do agree that we can't ascribe cognition to machine learning. But I also believe that we can't ascribe that it's NOT cognition. Why? Because we don't even truly unders…
In science, if you don't know, you don't make the claim, that is basic positivism and the scientific method. So basic in fact, I was thought this in elementary school. So far ad-hominem attributions of naivety. Anyone that humanises computation is not only committing an A.I. faux-pas but are going against the basic scientific method.
Re: Talking About Large Language Models
#127The section on emergence makes a very convincing point about how such systems might, at least in theory, be doing absolutely anything, including "real" cognition, internally and then goes right ahead and dismisses this entirely on the basis of the system not having conversational intent. who cares if it has conversational intent? If it was shown to be doing "the real thing" (how ever you might want to define that) internally that would still be a big deal wether the part you interact with gives you direct access to that or not.
Then it goes on to argue that these systems can't possibly actually believe anything because they can't update believes. Frankly I'm neither convinced that the general use of the word "believe" matches the narrow definition they seem to be using here nor that even their narrow definition could not in principle still be taking place internally for the reasons laid out in the emergence section.
I agree people should probably be mindful of overly anthropomorphic language but at the same time we really shouldn't be so sure that a thing is definitely not doing certain things that we can't even really define beyond "I know it when I see it" and that it sure looks like it's doing.
beyond that I'm not even really sure there is a good philosophical grounding for insisting that "what's really going on inside" matters, like, at all. The core thing with the turing test isn't the silly and outdated test protocol but the notion that, if something is indistinguishable by observation from a conscious system, there is simply no meaningful basis to claim it isn't one.
all that said the current state of the art probably doesn't warrant a lot of anthropomorphizing but that might well change in the future without any change to the kinda of systems used that would be relevant to the arguments made in the paper
Re: Talking About Large Language Models
#128Earlier quoted context omitted.
On the contrary, it highlights that the question is generally not well posed and trying to answer it before figuring out how to ask it is silly.
People have been asking grand but poorly-posed questions for millennia, and sometimes this leads to well-posed questions that elicit remarkable answers. What is poorly-posed about the question 'could a machine think'? 'Machine' seems acceptably well-defined, and not in a way that rules out, a priori, the possibility of any machine being able to think, so I'm guessing the problem lies in us not having a good definitio…
But "what is thinking?" isn't poorly posed in the case that "thinking" isn't well defined, because it is about establishing and agreeing on a definition.
The approach some people take to answering "could machines think?" is to try to determine an actual subject in the real world to call "thinking" in a way that aligns with common intuitions, which to me seems like a good approach. But many (most?) approaches I've encountered instead just assume personal intuition about thinking is sufficient, and worse, in common with all other askers of the question.
Re: Talking About Large Language Models
#129Earlier quoted context omitted.
Quoted post unavailable.
You fundamentally misunderstand the scientific method which includes falsifiability but is not limited to it, moved the goalpost away from peer review for journals into general bar stool philosophy all while continuously insulting the GP. What is ironic is you hiding behind throwaway accounts and asserting superior knowledge while fundamentally misunderstanding the issue of positive claims in science.
Read the conversation. You are accusing me of something vile which the GP started. GP Literally said that science was something elementary school level shit implying that I was so damn stupid I was uneducated. You're only taking his side because You agree with him, you are not seeing who drew first blood and you are burning the witch because it serves your own agenda. He insulted me DELIBERATELY. I'm also not technically insulting him. I don't think he knew what I told him.
>You fundamentally misunderstand the scientific method which includes falsifiability but is not limited to it
This is fundamentally incorrect. You are just like the GP. You lack of knowledge. Did you not see the quote from Einstein? My statements are legitimate.
The highest most definitive statement one can make in science is falsification. The next highest form of statement is causality and the next is correlative and everything after that is just informal qualitative stuff. That's it. After falsification everything becomes dependent on the sample and all statements are open to being proved completely wrong at any point in time. Causative and correlative statements are never definitive. But falsification is definitive.
Let me repeat. The only definitive claims science can make about reality is falsification. That's it.
There is no form of provable claims in science. None. If a scientist makes such a claim in a scientific paper, it is not technically science. It is simply a speculative claim on an academic paper. This is not philosophy this is science in it's most fundamental form.
>What is ironic is you hiding behind throwaway accounts
Hiding behind throw away accounts? Are you joking? What is there to hide that everyone else is hiding here? Are you implying you're not hiding, why don't you tell me your real full name if your not hiding as well? The only reason why I made a new account is because I wanted to comment here. That's it. Are you demanding I put my name here?
>moved the goalpost away from peer review for journals
Let me quote what was said in GP's FIRST sentence and FIRST post.
"I am NLP researcher who volunteers for peer review often and the anthropomorphisms in papers are indeed very common and very wrong. "
There are TWO GOAL POSTS listed here. anthropomorphisms LISTED in papers and anthropomorphisms being "VERY WRONG." I did not move a goal post. I am simply refuting a goal post that the GP listed himself and that YOU missed.
>while fundamentally misunderstanding the issue of positive claims in science.
Positivism in science is largely a philosophy. It's abstract enough such that it's not worth talking about. This is actual philosophy. Falsification is fundamental enough that it is intrinsic to understanding the scientific method. It's not "just" a philosophy any more then logic or math is a "philosophy". Literally. If you don't get that the entire scientific method and reality in itself is ONLY subject to falsification, you then you don't understand science.
I'll repeat Einsteins quote:
"No amount of experimentation can ever prove me right; a single experiment can prove me wrong."
Literally the man is saying that there is no science in the universe that can prove any of his theories right. If you don't understand the meaning behind why he said this, again, you don't understand science. This is fundamental it is basic. Not fundamental in the sense that everyone should understand it, but fundamental in terms of foundational to reality, to science itself.
Re: Talking About Large Language Models
#130Earlier quoted context omitted.
> In science, if you don't know, you don't make the claim, that is basic positivism and the scientific method. Yes you're correct. So you can't make the claim that it's NOT cognition. That is my point. You also can't make the claim that it is cognition which was the OTHER point. Completely agree with your statement here. But it goes further then this, and your statement shows YOU don't understand science. >So basic i…
Let me falsify your claim immediately: the inputs of these models are nothing like the inputs a human receives, subword tokens do not even match up with lexical items (visually, textually and semantically). You seem to agree with me even though your interpretation of falsifiability is inverted: I am not asking that authors make a claim that their models do not mimick human intelligence. Like OP, I ask them that they…
This in itself is a claim made without evidence. Which is my point. The claim as it stands cannot be made either way. We simply don't know.