Earlier quoted context omitted.
I did a bit of research in program synthesis, which is probably the closest academic field to this sort of thing (albeit in the realm of programming languages rather than AI/ML). I have two main conclusions: • Current methods to generate programs fundamentally do not scale . Most practical research is about how to extend small-scale synthesis systems to large-scale problems, rather than synthesizing large programs ou…
Current program synthesis isn't that advanced. At least not compared to state of the art AI. Recently AI has proved it can beat expert humans at all perfect information games. Go has a ridiculously huge search space, but by using neural networks AIs can learn to narrow it down to a manageable size. Program synthesis is just like a perfect information game. You have a search space, a state, a set of goals, etc. You co…
But not actually. Program synthesis is hard even if you have perfect formal requirements. In practice, gathering formal requirements is by far the most difficult task. You're probably better off building a system using modern development practices than trying to collect formal requirements Let alone translating those requirements into code, which is easier than coming up with the requirements but still difficult.
> Recently AI has proved...
FYI, it's not as if the program synthesis folks haven't heard of neural nets... you're giving researchers who actually do this stuff all day surprisingly little credit.