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An example of what you are proposing as evidence: we have an indestructible radio, you can’t open it. It does radio things. You are proposing that empirically since a voice comes out of this radio then it must have a tiny man inside of it. There is no other “evidence”. And the proof? Well its empirically observable, what do you mean there is no tiny man inside the box??
It’s a bad argument and bad science
EDIT: What are you actually using as a definition of internal model? It is imprecise in casual conversation but very specific in technical ones.
This has no relationship to what I wrote, as far as I can tell. Could you explain the connection?
I would say, logically, because a voice comes out of this radio, then it must have some ability to vibrate air. And depending on the nature of the voice and the words, I could draw some conclusions - e.g. if it reported on news that happened after the radio entered my presence, that it must have some ability to take in information from faraway. I'm not sure where you're getting this idea that my logic would conclude that there's a tiny man. Again, I can't figure out how that has any relationship at all with what I wrote, and I'm curious what your explanation of that relationship is.
EDIT:
Something that is simpler than the actual thing being modeled, but which can be used to help make predictions about the actual thing.
You see a black box, you observe inputs and outputs empirically and you derive an explanation for what is inside the box without actually being able to check.
I’m understanding your definition as:
Does that track?
If so we have wildly different definitions. I would say your definition is very very broad, something like logistic regression or a kalman filter would have an internal model.
My definition is is very RL/latent space/World Model-esque: an internal model is a learned or encoded internal structure that represents the state and transition dynamics of an external system sufficiently well to support counterfactuals, simulation, planning, or action prediction.
Which is why your ball throwing example confuses me, under my definition, yes kids clearly have an internal model for catching a baseball but it is very controversial/not settled that an LLM has an internal model for chess. I think saying kids can catch a baseball using essentially a compressed predictive statistics process is cognitively incorrect.
I would agree with this. I see no reason why a "model" would have to have any features more than that to qualify as a model. Of course, having features that are more than that can make it a better model or one that's more useful in certain contexts - in fact, having features more than that are required for being sufficiently useful in most contexts that are worth discussing. But that's a question of degree - if the model allows sufficiently accurate and precise predictions about what it's modeling, then it could be useful for the purposes of someone who wants to generate counterfactuals, simulation, planning, or action prediction.
The actual mechanism of the model that the kid is using doesn't matter. Again, the kid could be a robot, and we'd still know that it had a model of physics. The model of physics might be as simple as "push ball in direction X -> ball moves towards direction X" but that doesn't make it not a model - just a really really simple model. One that is wrong, much like every model, and one that is useful enough for the purposes of throwing a ball towards direction X.
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