Kahneman and Tversky came up with questions meant to probe that in humans:
> If a baseball bat and a ball cost a total of $1.10, and the bat costs $1 more than the ball, then how much does the ball cost?
Most people arrive at $0.10 quickly (system 1), but you can't arrive at the right answer if you go by your gut, you have to shift into a different mode of cognition (system 2) in order to compute the correct answer.
I don't know why you think this is true. If statistically B follows A to a high degree, then a sufficiently advanced statistical model will represent "A then B" in some manner. In a predictive language model, at some point the best way to model a text corpus that indirectly references the "A then B" causal structure is to just model that structure and reference it as needed.
The statistical model will die to combinatorial explosion between billions of possible combinations of locations, times, and modes of transport. In various literature in 2 hours you might have travelled across town, across continents, or to the moon. Statistical approach to such problems is dumb.
>Statistical approach to such problems is dumb.
Well, expecting your model to extract a spatial representation of the world from text is a dumb approach indeed. We interact with the spatial information much more directly. But our ability to navigate is fundamentally just a process of capturing regularities in our sensory input.
> But our ability to navigate is fundamentally just a process of capturing regularities in our sensory input.
i don't think this is true at all, many animals have dedicated 'hardware' for navigation that can sense magnetic fields, etc. We seem to be born with spatial awareness that is far beyond what GPT will ever be capable of.
Yes, but suppose A implied B only if C were true. And in the training corpus C were always true (hence learned A=>B) but in the test corpus suppose C is not true, then the learned statistical rule is wrong.
The problem is that to cover all these possibilities will require not a billion documents but something like exp(billion) documents (exaggerating, but to give you a feel for combinatorial explosions) as @ClumsyPilot explains.
And problem is the real world always keeps “changing” (Eg: whether C is valid or not) compared to the examples you trained on. This is referred to in polite company as “distribution shift” where your underlying statistical distribution keeps evolving.