[urthor]

From my work, I've found it's really simple and straightforward to apply.

1. Choose a parameter for your compiler, xxx.

2. Have your ML model "choose compiler config parameter yyy." After the ML model "chooses" the config parameters, work backwards.

3. Determine why yyy is a better config parameter than xxx.

It might not be!

This system works, brilliantly. Cyborg intelligence, a combination of the human being and the ML model, is the future of society.

The key is the ML "suggests." ML must keep "suggesting."

Never have ML choose a parameter autonomously.

That's exactly how you get self driving cars running over children.

[moonchild]

> Choose a parameter for your compiler, xxx

Most interesting cases don't really look like this. The heuristic is applied to the user's code; it's not a one-time knob in the compiler. If it were, then you would likely be able to afford an exhaustive search to pick it & wouldn't need ml.

[urthor]

Depends on the domain.

The analogy I'm making doesn't especially apply to compilers, which have human defined defaults in the first place.

What I've found is that it's important to not run the ML model then use its output as the default state. Have a human, heuristic choice as the default state.