Hard to prove a negative. On the other hand, relying on a poorly defined idea of "emergent behavior" when there is no architectural rational is rather poor form IMO. It's more wordy but safer to qualify these terms appropriately. That is, to test the "appearance" or "simulation" of human-like abilities.
That's all I'm hoping for, an ML model that appears to be as good as any human in existence, for all human skills.
To be fair, this is what we do with humans as well, we test their skills. We (rarely!) take humans apart, inspect their brains, and declare that they have reasoning or understanding capabilities.
Sounds like you are looking for AGI, along with many other folks. Although, be careful of the usage of "as good as", which is hard to quantify (think of artistic endeavors).
The absence of an architectural rational does not preclude it's formation either. Not understanding how something works doesn't mean that something isn't there.
It simply means we don't yet understand, so you should be agnostic in your position. That's why these studies are trying to find answers to these questions, not pretending to know the answer like you are.
Strange you don’t recognize you’re making the very mistake you criticize others for.
So I'll just say it: you lack the knowledge foundations for an informed opinion on this topic.
We know that DNA is "just" a sequence of nucleotides, yet life emerges. How does that happen, going from the world of nonliving to the living? We don't know. There are theories, but abiogenesis remains one of the most profound mysteries of science. But at the core of these theories, it is that the process of abiogenesis is informational.
Computer code is information. So from an information theoretic perspective, it's quite incoherent to say something can't happen because "computer code". As in fact information may well be the entire reason we can reason, or have the complexities of life.
Consider:
1) we know LLMs are universal function approximators
2) suppose the ability to reason, and the degree of reasoning ability, is a functional process that can be represented algorithmically, and can be learned
3) then might LLMs have learned some degree of reasoning ability?
If you insist 3) is impossible, you must also insist 2) is impossible. And since we really don't have a definitive answer for 2) much like we don't have one for abiogenesis, your position is not really defensible. At least not to the degree of conviction you seem to hold.
This is just silly. You can trace precisely how an LLM produces its output. You have access to all of the weights. We know precisely how the model is trained. There is no mystery here.
Two things, first you are thinking about proving as a negative, it means you think something think or do not, understand or do not, imagine or do not.
It's wrong, it's all a spectrum.
Pigeons are stupid, relatively to a human, still, a pigeon have some intelligence.
That's the same thing for worms. Do you think that a worm have more intelligence than an LLM ?
The second thing, you says "hard to prove a negative", that's a fallacy that hide the main point: you don't know how to prove that something can understand.
Prove that you understood a concept, any of your choosing, or prove that you are not showing the appearance of intelligence or ability. Good Luck.
You avoided the questions as a whole.
LLMs theorically can emulate any functions, so the mechanism is also here.
If I understand correctly, you are saying that worms are smarter than LLMs ?
Yeah, exactly. A network of NN functions is a model for AGI.
My point is that to say that a NN can approximate a mathematical function is correct. But to now equate such a function to a generalized procedure (like a simulation of a brain) is a mistake.
Yeah at this point the guy is either trolling or very confused.
It's some crazy stuff framing your personal opinion on a controversial subject like that as if it's an established fact when the subject itself is under active studied and hardly "solved".
There are some big issues with the ML community today. One is brazen assumptions. If the subject is under study, why write your title and abstract in such a fashion that you assume that LLMs can "understand". That is the source of my original (fairly mild) objection. If the problem isn't "solved", why assume it is?
> Can Large Language Models Understand Symbolic Graphics Programs?
The title is framed as a question. And even throughout the study, the authors are careful to refer to this apparent reasoning ability with the appropriate qualifiers.
> To understand symbolic programs, LLMs may need to possess the ability to imagine how the corresponding graphics content would look without directly accessing the rendered visual content
> We find that this task distinguishes existing LLMs and models considered good at reasoning perform better
In short no such assumption is made.
You are so overwhelmed by your personal biases you're not even comprehending what you're reading.
