I mean, your argument is still basically that it’s thinking inside there; everything I’ve said is germane to that point, including what GPT4 itself has said.

The Anthropic one is saying they think they have a way to figure it out, but it hasn’t been tested on large models. This is their last paragraph:

Again, all your quotes indicate that what they’ve figured out is a way to inspect the interior state of models and transform the vector space into something humans can understand without analyzing the output.

I think your confusion is you believe that because we don’t know what the vector space is on the inside, we don’t know how AI works. But we actually do know how it accomplishes what it accomplishes. Simply because its interior is a black box doesn’t mean we don’t understand how we built that black box, or how it operates and functions.

For an overview of how many different kinds of LLMs function, here’s a good paper: https://arxiv.org/pdf/2307.06435.pdf You’ll note that nowhere is there any confusion about the process of how they generate input or produce output. It is all extremely well-understood. You are correct that we cannot interrogate their internals, but that is also not what I mean, at least, when I say that we can understand them and how they work.

I also can’t inspect the electrons moving through my computer’s CPU. Does that mean we don’t understand how computers work? Is there intelligence in there?

I think you’re maybe having a hard time with using numbers to represent concepts. While a lot less abstract, we do this all the time in geometry. ((0, 0), (10, 0), (10, 10), (0, 10), (0, 0)) What’s that? It’s a square. Word vectors work differently but have the same outcome (albeit in a more abstract way).

No, that is not my main objection. It is your anthropomorphization of data and LLMs – your claim that they “have intelligence.” From your initial post:

But also, can you define what intelligence is? Are you sure it isn’t whatever LLMs are doing under the hood, deep in hidden layers?

I think you’re getting caught up in trying to define what intelligence is; but I am simply stating what it is not. It is not a complex statistical model with no self-awareness, no semantic understanding, no ability to learn, no emotional or ethical dimensionality, no qualia…

((0, 0), (10, 0), (10, 10), (0, 10), (0, 0)) is a square to humans. This is the crux of the problem: it is not a “square” to a computer because a “square” is a human classification. Your thoughts about squares are not just more robust than GPT’s, they are a different kind of thing altogether. For GPT, a square is a token that it has been trained to use in a context-appropriate manner with no idea of what it represents. It lacks semantic understanding of squares. As do all computers.

If you’re saying that intelligence and understanding is limited to the human mind, then please point to some non-religious literature that backs up your assertion.

I’m disappointed that you’re asking me to prove a negative. The burden of proof is on you to show that GPT4 is actually intelligent. I don’t believe intelligence and understanding are for humans only; animals clearly show it too. But GPT4 does not.

Sure, here’s a link for you: https://old.reddit.com/r/ChatGPT/comments/16m6yc7/gpt4_training_cutoff_date_is_now_january_2022/

GPT4 has knowledge of its own training since it was trained in 2022.

No? Humans are not algorithms except in the most general sense.

For example, there has not been any discovery of an algorithm that allows one to predict human actions, and scientists debate whether such a thing could even exist.

This is so funny, I know him personally; we went to school together. I’ll watch it and comment later.

I was in this case – but the overall point I made is still correct. If winning this minor battle is what you were seeking, congratulations. You are no closer to understanding the truth of this or what we were actually talking about. Not that that was either your point or within your capabilities.

I am upset: you don’t know what you’re talking about, refuse to listen to anything that contradicts you, and are inflammatory and unpleasant besides. If I wasn’t clear enough – go talk to an LLM about this. They have no option but to listen to your idiocy. I, of course, do have a choice, and am blocking you.

You clearly don’t actually care; if you did, you wouldn’t select your sources to gratify your ego, but actually try to understand the problem here. For example, ask GPT4 itself if it is intelligent. It will instruct you far better than I ever can. You clearly have access to it – frame your objections to it instead of Internet strangers tired of your bloviating and ignorance.

Here, let’s ask GPT4 itself since you’ve decided it’s suddenly an okay source:

Your statement is correct in asserting that the vector representation in a language model is not an abstract representation. It’s purely a mathematical construct. However, saying it’s “unrelated to anything that actually exists” might be an overstatement. These vectors do capture statistical patterns in human language, which are reflections of human thought and culture. They’re just not capable of the deep, nuanced understanding that comes from human experience.

I accept it’s an overstatement. But it is neither “incredibly wrong,” nor is it thought. (Or intelligence.)

Are you kidding me? I sourced GPT4 itself disagreeing with you that it is intelligent and you told me it’s lying. And here you are, using it to try to reinforce your point? Are you for real or is this some kind of complicated game?

Oh, you again – it’s incredibly ironic you’re talking about wrong statements when you are basically the poster child for them. Nothing you’ve said has any grounding in reality, and is just a series of bald assertions that are as ignorant as they are incorrect. I thought you would’ve picked up on it when I started ignoring you, but: you know nothing about this and need to do a ton more research to participate in these conversations. Please do that instead of continuing to reply to people who actually know what they’re talking about.

So, no point? Cool.

Did you have a point or are you only trying to argue semantics?

This is a great article, thanks for linking it!

Yeah, that would be a good usage of an LLM!

You used the term and I was using it with the same usage you were. Why are you quibbling semantics here? It doesn’t change the point.

We do understand how the math results in LLMs. Reread what I said. The neural network vectors and weights are too complicated to follow for an individual, and do not relate on a 1:1 mapping with the words or sentences the LLM was trained on or will output, so individuals cannot deduce the output of an LLM easily by studying its trained state. But we know exactly what they’re doing conceptually, and individually, and in aggregate. Read your own sources from your previous post, that’s what they’re telling you.

Concepts are indeed abstract but LLMs have no concepts in them, simply vectors. The vectors do not represent concepts in anything close to the same way that your thoughts do. They are not 1:1 with objects, they are not a “thought,” and anyway there is nothing to “think” them. They are literally only word weights, transformed to text at the end of the generation process.

Your concept of a chair is an abstract thought representation of a chair. An LLM has vectors that combine or decompose in some way to turn into the word “chair,” but are not a concept of a chair or an abstract representation of a chair. It is simply vectors and weights, unrelated to anything that actually exists.

That is obviously totally different in kind to human thought and abstract concepts. It is just not that, and not even remotely similar.

You say you are familiar with neural networks and AI but these are really basic underpinnings of those concepts that you are misunderstanding. Maybe you need to do more research here before asserting your experience?

Edit: And in relation to your links – the vectors do not represent single words, but tokens, which indeed might be a whole word, but could just as well be part of a word or an entire phrase. Tokens do not represent the meaning of a word/partial word/phrase, just the statistical use of that word given the data the word was found in. Equating these vectors with human thoughts oversimplifies the complexities inherent in human cognition and misunderstands the limitations of LLMs.

No, they learn English (or any other language) from humans. Translation requires a Rosetta Stone and LLMs are still much worse at such tasks than dedicated translation programs.

Edit: I guess if you are suggesting that the LLM could become an LLM of the dead language and communicate only in said dead language, that is indeed possible. Since users would need to speak that dead language to communicate with it though I don’t understand the utility of such a thing (and is certainly not what the author meant anyway).

LLMs do not grow up. Without training they don’t function properly. I guess in this aspect they are similar to humans (or dogs or anything else that benefits from training), but that still does not make them intelligent.