Both in learning environment and practice, from time to time I had to use different algorithms to solve problems. But the more I use them, the more it seems like the AI could be deployed to try finding the optimal solution, especially to the NP-complete problems, since the AI "progression" is easily tracked
If we, for example, never knew how to solve knapsack problem efficiently; I wonder, is applying AI practical and/or ever OK to finding the optimal solution to the given problem?
AI algorithms in general can find an approximation to basically any function. They are so powerful because this is true even for extremely complex functions with many input parameters and/or many output parameters and/or a very complicated internal structure.
On the other hand, there is no known way to solve NP-complete problems "quickly". In practice, you would often have to search through a huge solution space for finding the optimal solution. This is why people use heuristic methods and approximation algorithms to efficiently find a "sufficiently good" solution.
So yes, you can use AI to find a good approximate solution (and possibly even a better one than with traditional heuristics) to a computationally hard problem.
But no, if the problem is NP-complete, you still cannot know that you have found the optimal solution.