Everything new is also old. This from my 1984 PhD thesis:
"AI is an experimental science, yet the complexity of its programs and problem domains often makes the interpretation of results very difficult. Programs often contain so many components and parameters that limitations on computer time and the sheer number of possibilities make it impossible to experimentally evaluate how each contributes to performance."
Then I argued, just as I do today, for careful empirical studies in simplified settings that enable better scientific understanding.