I've been spending a lot of time lately digging into "outdated" 80's era approaches to AI. Some people think I'm wasting my time and ask "why aren't you doing Deep Learning?" This is the reason why: "You can think about deep learning as it currently is today as the equivalent in the brain to our sensory cortices: our visual cortex or auditory cortex. But, of course, true intelligence is a lot more than just that, you…
I'd say that's a good idea. A 30-year cycle (say one human generation) seems to occur in many fields, for various reasons.
For instance, some of the first transistors were field-effect (FETs), then bipolar junction (BJTs) ruled the first integrated circuits, then MOS technology made FETs again the standard technology. Some of the design approaches were recycled ...
Sometimes things seem to go in a spiral, and the new level amplifies the previous. I'd just caution that knowing why the previous approach went out of fashion would be useful, but that's often hard to determine - acknowledging failure is not a popular activity. Lack of power may not be the only problem ...