Human learning vs. machine learning.

For example, we have set of rules describing the deterministic operation ( transforming input to output), and task to teach a human and a computer to perform the operation.

To teach the human we need:
1) Describe the rules of the operation performing in the language.
2) Test knowledge (pass human through the tests), and check.
3) If human not having practice, for a time, we need to repeat the steps 1) and 2).

To teach the computer we need:
1) Describe the rules of the operation performing in the language.
It is all, if the 1) done perfectly. (We can test 1), but in fact it will be the test of our capabilities to describe operation, not the capabilities of the computer).

As the result, we would have the computer, that works most probably much better than the human, for the operation, with less efforts for teaching (If the labor intensity of the description in the computer language is equal to the description in the human language) .

It seems very simple, but there is conflict with the reality. Why neural networks are still popular concept? Why such a big amount of operations are still done by humans?