The feedback from compilation tools / linters fed into the training loops is an example of this.
What we end up with however is a model good at coding for example but bad at something else. And without enough general coding, good at one language over another.
And we're back to square one. The problem of being able to achieve true intelligence by distilling the essence of it not just knowing the answers to specific problems.
Given enough time, we'll plug the gaps and maybe get good enough but it's not true intelligence until it can learn in a way that excels at all fields in a cross-disciplinary way - much better than the side-effect way it's doing now where some other knowledge does actually contribute to achieving goals in other domains.
What we end up with however is a model good at coding for example but bad at something else. And without enough general coding, good at one language over another.
And we're back to square one. The problem of being able to achieve true intelligence by distilling the essence of it not just knowing the answers to specific problems.
Given enough time, we'll plug the gaps and maybe get good enough but it's not true intelligence until it can learn in a way that excels at all fields in a cross-disciplinary way - much better than the side-effect way it's doing now where some other knowledge does actually contribute to achieving goals in other domains.