Large language models lack deep insights or a theory of mind

It doesn't appear until the early 20th century, in the shadow of compulsory education and the challenges it presented, first as a technical label for attempts to sort students -- and later soldiers -- into the tracks in which they're most likely to succeed, and then being haphazardly asserted (but not scientifically evidenced) as some general measure of mental aptitude.

At that point it shifts from something qualitative (which mental tasks might someone be good at) to something quantitative (how much more might one personal excel at all mental tasks than another), and the burgeoning field of modern American psychology goes "Aha! A quantitative measure! Here's our meal ticket to being recognized as a science instead of those quacks from Vienna", with far too much at stake to either question the many assumptions at play or the inconsistent history of usage.

Momentum takes hold and the public takes the word into its everyday vernacular, even while it's still not a clear and sound concept in its technical domain. [Most of this is history is more academically covered in Danziger's 1987 "Naming the Mind" which is excellent, and critical foundational reading to contextualize recent hot discussions in AI]

The way you're using it when you worry about "super-intelligence" is in the sense of intelligence being some universal, unbounded, quantitative independent variable along the lines of "the more intelligent something is, the more cunningly it can pursue some rationalized goal" -- some master strategist.

That's fine, and you're not alone in that, but there's not really any sound scientific groundwork to establish that there exists some quality of the world that scales like that. You're fear, and what you try to distinguish conceptually from what the paper addresses, is an inductive leap made from highly unstable ground. It's in the same invented, purely abstract idea-space of "omnipotence" or "omniscience" where one takes a practical idea like "power to influence" or "ability to know fact" and inductively draws a line from these practical senses towards some abstract infinite/incomprehensible version of that thing. But that inductive leap a Platonic logician's parlor trick and ends up raising all kinds of abstract paradoxes, as well countless physical impracticalities about how such things could exist.

So a lot of people (academic and lay) just aren't with you in taking that framing of intelligence very seriously. For many, an "super-intelligent" software whose "motives" we don't understand is just a program that produces incorrect outputs and ought to be debugged or retired, and the more interesting questions around machine "intelligence" are practical ones like "what tasks are these programs well-suited for". Here, the authors point out that the current batch of programs are not good at tasks that benefit from a theory of mind.

Knowing the answer to that kind of question reaches back to the earliest and least disputable sense of the word, where we saw that some new students and soldiers excelled at certain tasks and struggled with others, and wanted to understand how best to educated/assign them. And likewise, as we look at these tools, the pressing question for engineers and businesses is "what are they good for and what are they not good for" rather than the fantastical "what if we make a broken program and it wants to kill everyone and we don't notice and forget to shut it off"

If that's how it works, then the "soul" is more like an emergent phenomenon created by the interplay between the various layers of conscious thought and the base layer of nothingness when it's all turned off. That architecture wouldn't necessarily be so difficult to replicate in AI systems.

Goal posts only exist in games.

These systems are engineering products to be leveraged in enginenering processes. We want to understand what they're good at and what they're bad at, and what potential they show for further refinement. There are no goal posts or "happy with" criteria in that context, and when we find ourselves adjusting the language we use to describe them because of how we see them work, we're trying to refine our ability to express their capabilities and suitabilities.

Intelligence, in particular, is a very poor and ambiguous word to be stuck using in technical contexts and so we're likely to just gradually shed it over time to reduce confusion as we hone in on better ways to talk about these systems. We've repeatedly done the same for earlier advances in the field, and for the same reason.

If you assume that "the eyes are the window to the soul", you notice some interesting properties.

1. It is far more observable from the outside (eyes open/lidded/closed, emotion read in eyes)

2. It affects behavior in a diffuse way

3. It pays attention but does not dictate