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Unless you are strictly talking about predictive modeling, I would disagree with this. PCA just tries to represent N-dimensional observations in a k < N dimensional subspace (for a given k) such that it captures the most variation. This does not mean that any obtained component loadings refer to anything real or meaningful.

Haha, we used to call it Wikipedia Racing.

I never claimed that everyone wants to claim AI for themselves.

Artificial intelligence is ofcourse a scientific field on it's own right, even before machine learning was a thing. I'm just saying that AI scientists have used concepts from statistics to create an approach to AI called machine learning. I'm not saying that ML is a subset of statistics, mind you, but the statistical underpinnings of it definitely are. ML is not _just_ statistics too.

Moreover, why would statisticians be pissed about the efficacy of a model?

Firstly, many problems/questions that I work on are not concerned with prediction.

Secondly, even if I did, I would love to use DNNs. It's just that I never have a use for it considering I'm only looking at tabular data. Why bother with DNNs when, say, a random forest will do?

The subject matter in the book is more general than focussing on a specific models; the book is about the statistical underpinnings of ML as a whole.

I hear the complaint that R is a terrible language mainly from professional programmers.

I'm sure it is poorly designed from a computer science perspective, but there is no programming language out there which makes it so easy to run any model under the sun as does R.

I feel somewhat embarassed to talk about this, but does anyone else feel a sense of dread and depression when reading news like this? I can't seem to shake it and it's quite strongly affecting my worldview.