[ganfortran]

I guess computer is much more deterministic than what is required for ML to be useful.

ML, in a very inaccurate way, can be seen as:

1.We have observations and conclusions.

2.We don't know exact those observations leads to the conclusions.

3.The assumed procedure that leads the observations to conclusions is called model.

4.With enough pairs of (observation, conclusion), we can train a good model that is good enough to make good decision on future observations.

Problem for traditional computer science is that, the system is so deterministic that we know EXACTLY how it works on instruction level, while ML is good at dealing problem that is inherently probabilistic.