[fc417fc802]

> you almost never actually interact with people more than a half standard deviation away

I wasn't talking about the average person there but rather those who could also craft the high undergrad to low grad level explanations I referred to.

> This has not been a remotely credible claim for at least the past six months

Well it's happened to me within the past six months (actually within the past month) so I don't know what you want from me. I wasn't claiming that they never exhibit evidence of a mental model (can't prove a negative anyhow). There are cases where they have rendered a detailed explanation to me yet there were issues with it that you simply could not make if you had a working mental model of the subject that matched the level of the explanation provided (IMO obviously). Imagine a toddler spewing a quantum mechanics textbook at you but then uttering something completely absurd that reveals an inherent lack of understanding; not a minor slip up but a fundamental lack of comprehension. Like I said it's really weird and I'm not sure what to make of it nor how to properly articulate the details.

I'm aware it's not a rigorous claim. I have no idea how you'd go about characterizing the phenomenon.

[winwang]

How much of this is expectations setting by the heights models reach? i.e. of we could assess a consistent floor of model performance in a vacuum, would we say it's better at "AGI" than the bottom 0.1% of humans?

[fc417fc802]

Not sure how to answer because we were off on a tangent there about mental models.

I think AGI is two things. Intelligence at a given task, which can be scored versus humans or otherwise. And generalization which is entirely separate. We already have superhuman non-general models in a few domains.

So I don't think that "better than AGI at % of humans" is a sensible statement, at least not initially.

Right now humans generalize to all integers while AI companies keep manually adding additional integers to a finite list and bystanders make claims of generality. If you've still got a finite list you aren't general regardless of how long the list is.

If at some point a model shows up that works on all even integers but not odd ones then I guess you could reasonably claim you had AGI that was 50% of what humans achieve. If a model that generalizes to all the reals shows up then it will have exceeded human generality by an infinite degree. We'll cross those bridges when we come to them - I don't think we're there yet.

[winwang]

Interestingly, I find that the models generalize decently well as long as the "training" (more analogous to that for humans) fits in (small enough) context. That's to say, "in-context learning" seems good enough for real use.

But of course, that's not quite "long term"

[fc417fc802]

Given that models don't currently learn as they go isn't that exactly what this benchmark is testing? If the model needs to either have been explicitly trained in a similar environment or else to have a human manually input a carefully crafted prompt then it isn't general. The latter case is a human tuning a powerful tool.

If it can add the necessary bits to its own prompt while working on the benchmark then it's generalizing.