You are confusing what PCA now is, and what it might become. I am more interested in the dream than in the present reality.
That is like claiming that multiplication of two numbers is the answer to AGI, and then telling any critics that they're confusing what multiplication is now with what multiplication may become.
You should find an explanation of PCA in any elementary linear algebra or statistics textbook. It has a range of applications (like any transform), but it might be best regarded as an/the elementary algorithm for unsupervised dimension reduction. When PCA works, it is more likely to be interpreted as a comment on the underlying simplicity of the original dataset, rather than the power of PCA itself.
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