Eventually, yes.

It turns out the full grammar of Python (and almost all real programming languages) is quite large; this is very early and new work in neural program synthesis, and so we chose a pretty limited DSL to make sure that we could at least solve this one before moving on to more general ones that contain state, conditionals, for-loops, etc. In theory however, we can apply the exact architecture to Python programs and see what happens. We haven't tried yet. :)

I'm not too familiar with evolutionary computation methods, but I imagine the approaches may be similar in nature.

Theorem solving is very closely related to program induction (we just change the grammar). Just as with Python, the underlying search space would be incredibly large, and while in theory, we could simply change the DSL and it should work, it'll probably involve a few more iterations of the model or other insights to see this to fruition (but it's definitely not impossible).

It looks like this is a more comprehensive version of FlashFill (can do more tasks), and it is based on deep learning instead of previous rule-based techniques in FlashFill.