Why do you need to ask me, isn't a guess based on past answers good enough?

Or, do you understand that you need to know more, you need to understand the reasoning based on what's missing from that post?

There's _always_ something missing, left unsaid in every example, it's the nature of language.

As for your example, the LLM can be trained to know the underlying reasons (doctor's recommendation, etc.). That knowledge is not fundamentally different from the knowledge that someone tends to eat cereal for breakfast. My question to you, was an attempt to highlight that the dichotomy you were drawing, in your example, doesn't actually exist.

Maybe, maybe one is based on correlation, the other causation.

In either case, the results are the same, he's eating cereal for breakfast. We can know this fact without knowing the underlying cause. Many times, we don't even know the cause of things we choose to do for ourselves, let alone what others do.

On top of which, even if you think the "cause" is that the doctor told him to eat a healthy diet, do you really know the actual cause? Maybe the real cause, is that the girl he fancies, told him he's not in good enough shape. The doctor telling him how to get in shape is only a correlation, the real cause is his desire to win the girl.

These connections are vast and deep, but they're all essentially the same type of knowledge, representable by the same data structures.

Yeah, no.

Understanding the causation allows the system to provide a better answer.

If they "enjoy" cereal, what about it do they enjoy, and what other possible things can be had for breakfast that also satisfy that enjoyment.

You'll never find that by looking only at the fact that they have eaten cereal for breakfast.

And the fact that that's not obvious to you is why I cannot be bothered going into any more depth on the topic any more. It's clear that you don't have any understanding on the topic beyond a superficial glance.

Bye :)