Technology
This is the official technology community of Lemmy.ml for all news related to creation and use of technology, and to facilitate civil, meaningful discussion around it.
Ask in DM before posting product reviews or ads. All such posts otherwise are subject to removal.
Rules:
1: All Lemmy rules apply
2: Do not post low effort posts
3: NEVER post naziped*gore stuff
4: Always post article URLs or their archived version URLs as sources, NOT screenshots. Help the blind users.
5: personal rants of Big Tech CEOs like Elon Musk are unwelcome (does not include posts about their companies affecting wide range of people)
6: no advertisement posts unless verified as legitimate and non-exploitative/non-consumerist
7: crypto related posts, unless essential, are disallowed
view the rest of the comments
GPT-4 cannot alter its weights once it has been trained so this is just factually wrong.
LLMs are really cool and very useful, don't get me wrong. But people get excited by what they seem to do and lose sight of what they actually can do. They are not intelligent. They create text based on inputs. That is not what intelligence is, unless you have an extremely dismal view of intelligence that humans are text creation machines with no thoughts, no feelings, no desires, no ability to plan... basically, no internal world at all.
An LLM is an algorithm, not an intelligence.
Adam Something uploaded a video starting with the definition of intelligence itself, and then explains how something that “acts” intelligent doesn’t mean it “is” intelligent.
I think even "intelligence" here is a stretch. In a very narrow sense, it is intelligent: it creates text, simulates conversations, answers questions. But that is not what intelligence is (and it is all LLMs can do).
The author is an imbecile if they haven't been able to break GPT. It took me less than one day of tooling around with it before I got it to say something which outed it as having no understanding of what we were discussing.
The bit you quoted is referring to training.
Recent papers say otherwise.
The conclusion the author of that article comes to (LLMs can understand animal language) is.. problematic at the very least. I don't know how they expect that to happen.
In what sense does your link say otherwise? Is a world model the same thing as intelligence?
In the end of the bit I quoted you say: "basically no world at all." 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 guess having a world model is more akin to understanding than intelligence, but I don't think we have a great definition of either.
Edit to add: More... papers...
From the Encyclopedia Britannica:
In no sense do LLMs do any of these except, perhaps, "understand and handle abstract concepts." But since they themselves have no understanding of the concepts, and merely generate text that can simulate understanding, I would call that a stretch.
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.
I'm not really interested in papers that either don't understand LLMs or play word games with intelligence (shockingly, solipsism is an easy point of view to believe if you just ignore all evidence). For every one of these, you can find a dozen that correctly describe ChatGPT and its limitations. Again, including ChatGPT itself. Why not believe those instead of cherry-pick articles that gratify your ego?
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.
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.
We understand how they work? From the Wikipedia page on LLMs:
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:
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.
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. :)
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.
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.
Yes, that's exactly what I'm saying.
I mean. Not in the way we do, and not with any agency, but I hadn't argued either way on thoughts because I don't know the answer to that.
Huh? We know what they are doing but we don't? Yes, we know the math, people wrote it. I coded my first neural network 35 years ago. I understand the math. We don't understand how the math is able to do what LLMs do. If that's what you're saying then we agree on this.
"The neurons are cells. When neurotransmitters are sent through the synapses, they become words, but they were never concepts at any point."
What do you mean by "they were never concepts"? Concepts of things are abstract. Nothing physical can "be" an abstract concept. If you think about a chair, there isn't suddenly a physical chair in your head. There's some sort of abstract representation. That's what word vectors are. Different from how it works in a human brain, but performing a similar function.
From this page. Or better still, this article explaining how they are used to represent concepts. Like this is the whole reason vector embeddings were invented.
Obviously intelligence is an emergent feature. What you're arguing is as ridiculous as saying humans can't be intelligent because the behavior of individual neurons is relatively simple and governed by deterministic electrical and chemical processes. Any argument that starts with "it's all just math" is no good.
are you not an algorithm?
perfected over thousands of years?