If one specific tool / approach seems unreasonably effective, it's usually because the problems you've been trying to solve all belong to the same category. You may not expect the same effectiveness on new kinds problems.
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If all you've been doing so far is repairing rooftops and unclogging roof gutters, you may think ladders are all you need. Until you face a plumbing problem. Deep learning (differentiable parametric functions trained with gradient descent on large datasets) is exactly like that.
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The people trying to fix plumbing issues using ladders are slightly less visionary than they think.
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And sometimes you discover that the ladder can be used in a viking fight : That can be deep learning too (I forget the name of that)
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Hammer syndrome. If the only tool you know how to use is a hammer, all problems look like nails.
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Are you saying attention is not all we need in computer vision?
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