RNNs and CNNs are both pretty simple conceptually, and to me they fall into the class of "things I would have invented if I had been working on that problem," so I suspect that the original inventors knew what they were doing. (Random forests were not as intuitive to me, but then I saw a good explanation and realized what was going on, and again suspect that the inventor knew what they were doing.)
There is a lot of "we threw X at the problem, and maybe it worked?" throughout all of science, especially when it comes to ML (and statistics more broadly), because people don't really see why the algorithms work.
I remember once learning that someone had discretized a continuous variable so that they could fit a Hidden Markov Model to it. "Why not use a Kalman filter?" I asked, and got back "well, why not use A, B, or C?". At that point I realized that they didn't know that a Kalman filter is basically the continuous equivalent of a HMM (and thus obviously more appropriate, especially since they didn't have any strong reason to suspect non-Gaussianity), and so ended the conversation.
I find CNNs a lot less intuitive than RNNs. In which context was training many filters and successively apply pooling and again filters to smaller versions of the output an intuitive idea?
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