this post was submitted on 16 Oct 2023
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[–] [email protected] 3 points 1 year ago* (last edited 1 year ago) (25 children)

I’m not really interested in papers that either don’t understand LLMs or play word games with intelligence

I mean, my first paper was from Max Tegmark. My second paper was from Microsoft. You are discounting a well known expert in the field and one of the leading companies working on AI as not understanding LLMs.

Human intelligence is a mental quality that consists of the abilities to learn from experience, adapt to new situations, understand and handle abstract concepts, and use knowledge to manipulate one’s environment.

I note that's the definition for "human intelligence." But either way, sure, LLMs alone can't learn from experience (after training and between multiple separate contexts), and they can't manipulate their environment. BabyAGI, AgentGPT, and similar things can certainly manipulate their environment using LLMs and learn from experience. LLMs by themselves can totally adapt to new situations. The paper from Microsoft discusses that. However, for sure, they don't learn the way people do, and we aren't currently able to modify their weights after they've been trained (well without a lot of hardware). They can certainly do in-context learning.

Yes. LLMs are not magic, they are math, and we understand how they work. Deep under the hood, they are manipulating mathematical vectors that in no way are connected representationally to words. In the end, the result of that math is reapplied to a linguistic model and the result is speech. It is an algorithm, not an intelligence.

We understand how they work? From the Wikipedia page on LLMs:

Large language models by themselves are "black boxes", and it is not clear how they can perform linguistic tasks. There are several methods for understanding how LLM work.

It goes on to mention a couple things people are trying to do, but only with small LLMs so far.

Here's a quote from Anthropic, another leader in AI:

We understand the math of the trained network exactly – each neuron in a neural network performs simple arithmetic – but we don't understand why those mathematical operations result in the behaviors we see.

They're working on trying to understand LLMs, but aren't there yet. So, if you understand how they do what they do, then please let us know! It'd be really helpful to make sure we can better align them.

they are manipulating mathematical vectors that in no way are connected representationally to words

Is this not what word/sentence vectors are? Mathematical vectors that represent concepts that can then be linked to words/sentences?

Anyway, I think time will tell here. Let's see where we are in a couple years. :)

I’m not really interested in papers that either don’t understand LLMs or play word games with intelligence

[–] [email protected] 0 points 1 year ago* (last edited 1 year ago) (24 children)

Large language models by themselves are “black boxes”, and it is not clear how they can perform linguistic tasks. There are several methods for understanding how LLM work.

You are misunderstanding both this and the quote from Anthropic. They are saying the internal vector space that LLMs use is too complicated and too unrelated to the output to be understandable to humans. That doesn't mean they're having thoughts in there: we know exactly what they're doing inside that vector space -- performing very difficult math that seems totally meaningless to us.

Is this not what word/sentence vectors are? Mathematical vectors that represent concepts that can then be linked to words/sentences?

The vectors do not represent concepts. The vectors are math. When the vectors are sent through language decomposition they become words, but they were never concepts at any point.

[–] [email protected] 0 points 1 year ago* (last edited 1 year ago) (5 children)

You really, truly don't understand what you're talking about.

The vectors do not represent concepts. The vectors are math

If this community values good discussion, it should probably just ban statements that manage to be this wrong. It's like when creationists say things like "if we came from monkeys why are they still around???". The person has just demonstrated such a fundamental lack of understanding that it's better to not engage.

[–] [email protected] 0 points 1 year ago* (last edited 1 year ago) (1 children)

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.

[–] [email protected] 1 points 1 year ago (1 children)
[–] [email protected] -1 points 1 year ago (1 children)

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.

[–] [email protected] 1 points 1 year ago (1 children)

I understand you're upset, but my sources have been quite clear and straightforward. You should actually read them, they're quite nicely written.

[–] [email protected] -1 points 1 year ago

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.

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