That's because this is an evolutionary (in a broad sense) method of learning, i.e. it does not involve analytical understanding of what it's trying to optimize, but rather it uses feedback from previous iterations to achieve the objective.
Evolution works in the same way: there's no analysis from the phenotypical world back to the genes ("oh, we should be taller; let's just tweak this gene", i.e. Lamarckism). Instead, it's just massively scalable trial and error.
In fact, the logical disconnect between effect and cause is probably a strength rather than a weakness.
*In fact, the logical disconnect between effect and cause is probably a strength rather than a weakness.
Absolutely. I remember reading about an artificial life program which had evolving creatures try to survive in a very harsh artificial world. The strategies developed seemed less than optimal on the face of it, but when the writers of the program hand-coded their own supposedly "perfect" strategy, the increased efficiency of their strategy actually led to a lower overall survival rate. Only after that could they see why.
Evolution works in the same way: there's no analysis from the phenotypical world back to the genes ("oh, we should be taller; let's just tweak this gene", i.e. Lamarckism). Instead, it's just massively scalable trial and error.
In fact, the logical disconnect between effect and cause is probably a strength rather than a weakness.