> There's likely other examples that don't come to mind where AI has been used far longer than LLMs

We started in 1956. https://en.wikipedia.org/wiki/Dartmouth_workshop

I can't speak to 70 years ago, but in my 30ish years I don't think I have raised the bar of what I'd consider AI. I never thought of text prediction or auto complete as AI for example, though out of context I could see many 70 years ago considering it AI.

I've always considered AI as a much more complex concept than simply appearing to be human in speech or text. Extremely complex prediction algorithms that appear on the surface to be creative wouldn't have met my bar either.

An LLM can predict how a human may write a white paper, for example. It will sound impressive, but the model and related algorithms seem to have absolutely no way of logically working through a problem or modelling the world to know if the ideas proposed in the paper might work. Its a really impressive sounding impersonator, but it is nothing like intelligence.

Strangely enough, it is actually solid proof that they have a mental model; they just reject your attempts to amend it. This is where many assert several fundamental decisions of that model are actually more driven by the heart rather than the brain.

A LLM has no such concept. It is not truly capable of accepting your rational arguments; it may nod emphatically in its response after a few sentences, but once your input leaves its context window, all bets are off. We can retrain it with your particular conversation, and we will increase likelihood it will more readily agree with you, but that is likely a drop in the bucket of the larger corpus.

But this is well past technology and into philosophy, of which I can safely say my mental model for the topic would likely be rather frustrating to others.

> AI only needs to be as good as a human and cheaper

Setting aside the current limitations of LLMs for a moment, this is just a really unfortunate view on a new tech in my opinion. Its nothing unique here so I don't mean to single you out. I've seen it plenty of other places. The idea, though, that we effectively imply that most humans are idiots and we just need to invent something that is as good at being a functional idiot but for much less money leaves humanity in a terrible place. What does the world look like if we succeed at that?

To you main question, though, I think differences in human opinions and ideas can be easily misconstrued as a lack of intelligence. Our would also consider environmental factors of the average person.

I'll stick with the US as its what I am most familiar with. The average american has what I'd consider a high level of stress, chronic health conditions, one or more pharmaceutical prescriptions, and our food system is sorely lacking and borderline poisonous. With all those factors in play I can't put too much weight behind my view of the average person's intelligence.

For one thing, people often seem stupid to others not so much because they really are so, but because they don't happen to share another's specific context, views and opinions of certain things. Agreement is a terrible way to measure intelligence but so many of us do it.

Secondly, the very fact that you can't convince some people to "get it" (whatever the hell that thing may be, regardless of whether you're the one who's actually failing to get something at the same time) is evidence of their self directed reasoning. For better or worse, it's there, along with a sense of self that even the ostensibly dumbest person has, excepting maybe those with truly profound mental disabilities.

None of these things apply to LLMs, so why not drop the semantics about similarity or much less equivalence? Sel awareness is a sensation that you yourself feel every day as a conscious human being in your own right, and no evidence whatsoever indicates that any LLM has even a smidge of it.

"Intelligence" is an ability: some have it, and in large amount and complexity, some don't; some use it regularly, intensively and/or extensively, some don't.

AI is a set of engineered tools and they need to be adequate for the job.

When we speak about general Intelligence, it will have to be acute and reliable. Surely the metric will have to be well above low bars for humans, already because for that we already have humans available, and because, as noted, that low bar level is easily not «adequate for the job».

No, that is irrelevant and false. False, because we also access sub-worlds - the world model is made of contexts -, like LLMs (not «AI»).

Irrelevant, because for the intended purpose (reasoning over a reliable representation of the world) there is no need for an interactive stream - the salient examples of interaction, cause and effect, are provided by the dataset.

A large perception model "LPM" will not be superior to a large language model "LLM" if its underlying system is not superior.

The coherence comes from the relationship between the virtual (mapped) data and the real data it represents (territory), and the ability of the map to correctly predict the attributes of the territory.

Human perception is coherent because it tends to accurately model the physical world from which it's constructed, by means of sensory input and memory, and if this were not the case, we would have gone extinct as a species ages ago. While it is true that the model of human perception isn't perfect, it would be incorrect to call it incoherent to the same or similar degree as the incoherence demostrated by LLMs, as jmole's earlier comment implies.

Even if one must consider the model of human perception incoherent, one must also consider it vastly more coherent than whatever LLMs model reality on, if anything.

The argument was that with current word-based methods an LLM can puzzle out mechanistic problems better than a human. Turns out it can't at all, it makes errors that no human would. Anyone who looks at such attempts recognizes them as blather that is not rooted in reality.

The parent poster posted a paper where an AI guesses the Hamiltonian (nice, but with iterative methods you at least get an idea of the error associated with your numbers, not sure how far anyone should trust AI's there), maybe methods that do guesswork on topological networks would help. But I haven't seen those yet.

Emmy Noether, paging Emmy Noether, Emmy Noether to the red courtesy phone please!

You can't say that given <conditions>, 1-phenylpropane rearranges to 1,2-diphenylpropane, you cannot pull whole atoms out of thin air for an indefinite amount of time. No human would make such a mistake, but AI does, and what's worse, it is impossible to talk the AI out of its strange reasoning.

There is soooo much post processing happening in the visual cortex. The image from the eye is crude, only in focus in a tiny area, warped and upside down. It's quite incredible how our brains create this seamless virtual reality out of that input soup.

First of all, let us be clear that a «Perfect transformer architecture», in order to correctly perform induction of the dataset (and «generalize over» it), must also be a "perfect" reasoner. The new architecture will have to have that module working. And you reason over a world model (by definition), hence the problem whether a "proper" world model is there, and how good is its quality.

You seem to be conflating a «self» and «the thing doing the modeling». But there is no need to confuse the "self" as a perceptual object part of the world model, and the processor. The "world model" is the set of the ideas (productive to statements) describing reality. The perceptual self may not be greatly important in the world model (it will not be for a large amount of problems we will want to propose to the reasoner), and the processor will be just a "topic" in the world model, in the descriptive form (the world model contains descriptions of processors).

The expression about a «dataset that [would have] no "world model"» makes no sense. The dataset is the input of protocols ("that fact was witnessed"), the world model is what is reconstructed from the dataset. You have a set of facts (the dataset): you reconstruct an ontology and logos (entities and general rules) that make sense in view of the facts.

Is this argument not also applicable to us and our lives?