You might not, but you don't have to look far in these very comments to be met with woo and mysticism on the emergent phenomena side, either.
> The obvious explanation is that, between the limited amount of information that can be extracted from 2D photos that the NN is trained on, and the limit on the complexity of world modeling that NN of a particular size can fit, its model of the world is just not particularly accurate.
I think the more obvious solution is that it's not modelling the world, because... why would it be? It seems obvious to me that this is a significantly more complex way to handle their given task than every other explanation that does not require them create a model of the world.
> Pragmatically, at some point, you have to pick the most likely (usually this means the simplest) explanation to move forward.
I agree completely with this, which is why the idea that this is modelling the world is absolutely bizarre to me, particularly when our understanding of their training is that it's all weighting for pixel nearest neighbors via ranking how well it does at denoising 2D images.
It's not like de-rendering is something new, either. ShaderMap has existed since the mid 2000s and could get similar results from 2D images without any AI/ML, and we have other models that can generate it without anyone suggesting they model the world, e.g. https://arxiv.org/abs/2201.02279
People like LeCun don't think that even models where the stated goal is world simulation are able to do it, e.g. sora, either - https://twitter.com/ylecun/status/1758740106955952191 - so it seems even more unlikely when that isn't the goal that this has happened due to some emergent phenomenon.
But the other side doesn't see it that way, specifically not the "something more" part. It's "just math" all the way down, in our brains as well. The "emergent phenomena" are not undefined in this sense - they're (obviously in LLMs) also math, it's just that we don't understand it yet due to the sheer complexity of the resulting system. But that's not at all unusual - humans build useful things that we don't fully understand all the time (just look at physics of various processes).
> which also meshes closely with the fact that generations routinely have multiple perspectives and other errors that wouldn't exist if they modeled the world some people are suggesting.
This implies that the model of the world those things have either has to be perfect, or else it doesn't exist, which is a premise with no clear logic behind it. The obvious explanation is that, between the limited amount of information that can be extracted from 2D photos that the NN is trained on, and the limit on the complexity of world modeling that NN of a particular size can fit, its model of the world is just not particularly accurate.
> we're going to need some real explanations instead of things that can be explained multiple other ways before I'm going to take it seriously, at least.
If we used this threshold for physics, we'd have to throw out a lot of it, too, since you can always come up with a more complicated alternative explanation; e.g. aether can be viable if you ascribe just enough special properties to it. Pragmatically, at some point, you have to pick the most likely (usually this means the simplest) explanation to move forward.