I think it's a little deeper than that. During the '70s and '80s heyday of AI, the focus was on mechanisms that would mimic high-level aspects of human cognitio

I think it's a little deeper than that. During the '70s and '80s heyday of AI, the focus was on mechanisms that would mimic high-level aspects of human cognition: A* search, Prolog, and so on. The overall approach at the time was top-down, and focusing on deterministic algorithms. I think what we have learned in the interim is not just that "machine-learning" style approaches are successful at specific tasks, but also that a statistical, data-driven, "bottom-up" approach is going to need to be an integral part of any eventual general AI solution. Whether we will need to explicitly design the high-level cognitive elements, or whether they will emerge from properly constructed low-level elements, remains an open question. Another very important advance since the 70s/80s is the widespread realization that every major aspect of cognition is essentially probabilistic. People have realized that a search for "exact" or "optimal" solutions to problems with the complexity seen in the real world is a futile task because of the high dimensionality of these problems.