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You have some good points, but this naive approach of handcoding "cognitive modules" was tried many times in 20th century, and it didn't work at all.

But look at what Deepmind does: it takes these ideas (and also ideas from systems neuroscience), implements them as differentiable modules and trains them on data in end-to-end fashion. This works really well.

Learning is very important, much more important than architecture. If you have a model that can learn you can add more structure later - again this is what modern deep learning is all about.

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