[asgard1024]

I am no expert, but it seems to me that intuition is what the machine learning algorithm develops as a model.

The model itself cannot explain how it arrived to its conclusions; that could be done with the training data which are long gone.

Similarly to machine learning, humans can develop intuition about things by learning and training in the subject (that's why I believe rote learning is actually quite useful).

Just like with intuition, it crucially depends on (and varies with) the input data (experience) and there can be different models, but successful models (those that give good results on training data) are quite similar in appearance.

Of course, the big disadvantage of intuition is that you cannot explain it to others, even if it works. They have to believe that you are expert and made correct judgements (that you have correct model). That's why science (and especially mathematics) has tried to formalize the process, so that people could double check the reasoning and wouldn't have to rely on expert authority. That's why the two processes are called differently, I think.

[crimsonalucard]

I agree. This is a good theory.