Every example like this makes it obvious that you can now use ML-like optimization approaches on well-specified, very-well-tested software problems with a clear optimization goal. Keep if it improves the objective while maintaining correctness, discard if it doesn't. AI-descent strikes again.
Maybe I should learn more about ML to have a better instinct on optimization methods in general, so I can actually build AI optimizers like these.
Maybe I should learn more about ML to have a better instinct on optimization methods in general, so I can actually build AI optimizers like these.