A comment about the original topic...Machine Learning.
This is the field I work in. However, I don't recall, after scanning through the 34 pages of this thread where anyone tried to define what they mean by Machine Learning.
Academically, it is an extremely broad field. I have taken classes at the graduate school level at both the University of Washington and MIT. It covers everything from nonlinear optimization based techniques such as support vector machines, and neural networks, genetic and evolutionary search algorithms, rule base systems, decision trees, etc.
Commercially, my company is paid hundreds of millions of dollars each year to develop and configure this stuff, primarily in the financial industry. We have hedge fund and investment banking clients. I have met with them and worked with them.
So, I can only figure that these clients think these methods have some merit, although because of their secrecy, I have no idea whether trading systems using these techniques are actually deployed. One of the areas I can guarantee where they deploy our software is in the area of portfolio selection and risk analysis.
All that said, I do believe these techniques are useful in developing trading systems. But, not by novices. You have to ***understand*** what's going on.
AmazingIndustry said something about 5 pages back that was particularly relevant:
"but you still know in the back of your mind that the dynamics of the strategy were not derived from logical reasoning that you derived from experience about market dynamics but that it instead originated from a mathematical optimization tool".
The problem with non-interpretable strategies like those typically produced by neural networks, for instance, is if you can't understand why something is happening, you don't know when the logic is breaking down. Because people are at least subconsciously aware of this, they can't continue to trade these systems when they go through the normal periods of drawdown.