If all one is doing is giving a model lots of data and fitting curves it's not really 'understanding' but brute forcing it's way (with gradient descent) and then storing the weights and finally approximate the solution when a query is passed in.
This is not the same as understanding. Human intelligence can operate deterministically as well as non-deterministically. We can listen to language, which is by it's nature non-deterministic and convert that into deterministic operations and vice a versa. IE we can operate on some logic and explain it in multiple ways to other people.
Understanding requires much less data than brute forcing your way into pattern recognition.
When you see a simple number like this 2 * 4 you are able to understand that it's equivalent to 2 + 2 + 2 + 2 and that in turn means 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 <- Count that and you've got your answer.
Because you 'understand' this basic concept and all the operations in between you are able to compute more examples. But you only need to understand it once. Once you understand multiplications and additions and all the tricks in between you are able to compute 23 * 10 without being fed 23 * 10 as prior data. Understanding is very different from fitting a curve. You can reach conclusions and understanding through pattern recognition, but it's important to differentiate 'approximation' from 'calculation'. If you understand something in it's entirety you should be able to calculate an outcome deterministically.
Right now LLMs lack 'understanding', and seems to only 'approximate' which may seem like 'understanding' but is actually not.