>> As it is, I only see you making demands for proof of things that you are well aware that no human has yet completely modeled, even after decades of studying such phenomena.
What you gain from this incessant nay-saying, I do not understand.
I just wanted to comment on this. I've just finished a PhD (in AI, no less) and I've learned a few things about "nay-sayers", which in research are more formally critics and reviewers. What I've learned is that the "nay-sayers" are your friends. They're your friends even if they don't know it, even if their incessant nagging is driving you mad. They're your friends, because they give you the opportunity to see your work as it really is, not as the product of an unrecognised genious, but as the clumsy fumblings in the dark of a clueless and confused limited human mind. And they're your friends because they help you figure out where it is that you're wrong, and, ultimately, to improve your work.
It took me a long time to see things that way, it took many "dark tea-times of the soul" to figure out where I was wrong, when my critics were right, and how to improve my work to address the criticisms. And, you know the funny thing? Once I did that, all the frustration I felt at the "nay-saying" evaporated. The critics continued "nay-saying", but I just don't care, because I have proved to myself, and to others besides, that the criticisms have been addressed. The critics now have to find another angle of attack, and I'm disappointed as long as they can't, because that means my own understanding will stagnate and my work will not progress. Critics are worth their weight in gold.
But all that works much better in an academic environment where the critics and that "nay-sayers" are experts in their field. On the internet anyone can say anything and everybody pretends to know everything all the time, so all that we're left with is frustration. So I apologise for the frustration I discern in your use of "nay-sayers". This kind of conversation doesn't work well on the internet.
Part of why I comment is specifically to attract naysayers. I can learn from them. I've never been one to mind being wrong. I will argue vehemently for a position both because it seems correct to me, but also because it exposes my ideas to be torn down, so when I rebuild them they can be better.
I looked into things a number of people just in this thread brought up. It was a very rewarding discussion for me. I feel like my understanding is better than before it.
I became frustrated with you because your stance seemed a combination of "you can't prove it" and "it works like it does because it is what it is". You weren't expanding on your positions. It is possible that you are assuming context others in the thread do not share. It seems likely you think many here are reading into or seeing things that aren't actually present. Being tricked by the model, essentially. It is possible. But it also seemed possible that you were simply enjoying telling everyone they are wrong for fun.
With your refusal to expand on anything, it all looks much the same from this side.
I would like to understand what perspective you were arguing from, because even if I disagree with it in the end, knowing your perspective will allow me to understand where my own may be weak or flawed, and either shore it up or discard and rebuild it.
It is very frustrating to have a discussion with someone who seems to only contradict, but never explain their contradictions. They may be right. You may be right. But it was frustrating to talk to you because you didn't seem to want to say where or how people were wrong.
"It's not a brain". "It's works because it's a language model".
Okay, it's not a brain and it's just a language model. How does it perform what appears to be, whether it is or not, obvious translation of subtext in sentences between unrelated literal domains?
When I specifically asked if your position was simply that you would not attribute "meaning" to its representations, you declined to respond.
That's not an unfair position to take. An LLM has no experiential data in it. It has only the words. Philosophically, one could argue that even if it were "proven intelligent", it would still have no knowledge or contain actual meaning because it has only the words to go by. That would be a reasonable position, but you wouldn't confirm if that was what perspective you were taking.
I genuinely would enjoy knowing how you think translations on the level of the one I presented happened. Another poster discussed "latent spaces" where terms can grow closer even if not literally related. Articles I've read indicate any number of surprising results from LLms, assertions that LLMs can or cannot reason, various levels of assertion on whether LLMs can "understand" things. It appears like there are many perspectives active in this field all at once, and that they are quickly out of date as better and better LLMs are trained.
If you would be so kind, please add context to when you correct others. Pointers we can follow in order to understand your perspective.
For much of the thread, it felt like you were arguing that deep blue can't actually play chess because it has no concept of games or people or chess pieces or winning or losing, rather it only performs statistical analyses of numerically represented move sets and just outputs a move based on that statistical analysis. How can it play chess, after all, if it doesn't know what chess is?
>This kind of conversation doesn't work well on the internet
Possibly. I appreciate your having it nonetheless, as I have no one I know that cares to discuss any of this. So I have to make due with discussing it in online forums :)
Thanks for your comment! I gave my word I'd let you have the last word so I'm
not going to continue arguing. I'm just happy because I'm getting the feeling
you are not frustrated at our conversation anymore. I hope we can continue it
at some point and that I'll do a better job explaining things to you.
Especially when it comes to your big question about how can language models do
the kind of thing you illustrate with the translation example.
This conversation was interesting. I pasted it into ChatGPT (GPT-4) and asked it some questions. Here's one of its replies:
ChatGPT:
To better explain Knome's point to YeGoblynQueenne, I would provide a more concrete example and focus on the mechanisms behind LLMs that give rise to the appearance of meaning.
Large language models like GPT-3 are trained on vast amounts of text data, learning to predict the next word in a sentence based on context. This process enables the model to capture patterns, relationships, and regularities in the data, which can be seen as a form of "meaning" or understanding of language. The model's neural network, with its numerous layers, neurons, and connections, essentially forms a high-dimensional space that maps the relationships between words and their contexts.
To make Knome's argument more concrete, let's consider a specific example: the model's ability to translate the metaphor "spinning my tires" into "sweeping the ice." While it's true that the model doesn't have a direct understanding of the real-world experiences or objects the words represent, it has learned the contextual relationships between words and their usage in various situations. The model can associate "spinning my tires" with the concept of being stuck or not making progress, and "sweeping the ice" with a similar concept in the context of curling. This association is not a direct result of knowing the literal meanings of the words but rather of having learned the patterns and relationships in the data.
In essence, the "meaning" Knome refers to in LLMs is not the same as the meaning humans typically ascribe to language, which is derived from direct experiences and real-world knowledge. Instead, it is a form of abstract, statistical understanding that emerges from the relationships between words, their contexts, and the patterns in the data. The model essentially learns a high-dimensional representation of these relationships, which can be thought of as an approximation of meaning within the context of the model's training data.
Thus, when discussing meaning in the context of LLMs, it is essential to recognize that the term refers to an approximation of linguistic relationships and patterns rather than a direct understanding of real-world experiences. This approximation allows the model to manipulate language in a way that seems meaningful to humans, even though it lacks direct experiential knowledge of the world.
In summary, Knome's argument can be made more concrete and rigorous by focusing on the mechanisms behind LLMs, such as the high-dimensional representation of linguistic relationships, and by clarifying that the "meaning" in LLMs is an approximation derived from patterns and associations in the data rather than a direct understanding of real-world experiences.
I just wanted to comment on this. I've just finished a PhD (in AI, no less) and I've learned a few things about "nay-sayers", which in research are more formally critics and reviewers. What I've learned is that the "nay-sayers" are your friends. They're your friends even if they don't know it, even if their incessant nagging is driving you mad. They're your friends, because they give you the opportunity to see your work as it really is, not as the product of an unrecognised genious, but as the clumsy fumblings in the dark of a clueless and confused limited human mind. And they're your friends because they help you figure out where it is that you're wrong, and, ultimately, to improve your work.
It took me a long time to see things that way, it took many "dark tea-times of the soul" to figure out where I was wrong, when my critics were right, and how to improve my work to address the criticisms. And, you know the funny thing? Once I did that, all the frustration I felt at the "nay-saying" evaporated. The critics continued "nay-saying", but I just don't care, because I have proved to myself, and to others besides, that the criticisms have been addressed. The critics now have to find another angle of attack, and I'm disappointed as long as they can't, because that means my own understanding will stagnate and my work will not progress. Critics are worth their weight in gold.
But all that works much better in an academic environment where the critics and that "nay-sayers" are experts in their field. On the internet anyone can say anything and everybody pretends to know everything all the time, so all that we're left with is frustration. So I apologise for the frustration I discern in your use of "nay-sayers". This kind of conversation doesn't work well on the internet.