As I've understood it, normal programming is transforming an input with a program to get an output. Machine learning is giving the input and the output to get

As I've understood it, normal programming is transforming an input with a program to get an output. Machine learning is giving the input and the output to get a program. Problem is, it's too difficult to summarize or understand the resulting program, while the program you get is tied to the output data which is never really accurate. I'm still curious how ML specialists are approaching the task of analyzing a resulting deep neural network, and squeeze some science from it (meaning putting words on things they understand and are able to explain). I've also read that google was using ML to test different learning models, to easily find the best model to use for a given problem. I'm not sure but it sounded like they were feeding the training model and the data into another learning model. I can't remember the details or the article or the reddit comment but it sounded quite interesting.