Important subject, so-so blog post. This idea deserves further development.
The author seems to be discussing optimizing for the wrong metric. That's not a problem of too much efficiency.
Excessive efficiency problems are different. They come from optimizing real output at the expense of robustness. Just-in-time systems have that flaw. Price/performance is great until there's some disruption, then it's terrible for a while.
Overfitting is another real problem, but again, a different one. Overfitting is when you try to model something with too complex a model and and up just encoding the original data in the model, which then has no predictive power.
Optimizing for the wrong metric, and what do about it, is an important issue. This note calls out that problem but then goes off in another direction.
Very good characterisation of close, but distinct concepts. (a map of a ___domain)
If we squint a little, focus on close/far-away instead of same/distinct and s/metric/model/g (because usage of a metric implies a model), we can see how close these things can be.
Optimizing for the wrong metric - becomes “using a wrong model”.
Excessive efficiency - is partially “using a wrong model”, or maybe “good model != perfect model”. We start with good enough model, but after certain threshold we get to experience the difference between “good enough” and “perfect” (aparantly we care about redundancy, but it was not part of our model; so we were using a wrong model)
Overfitting is “finding the wrong model” (I wanted a model for the whole population, got a model only for a sample)
..or if we squint even more and go meta..
overfitting is part of “good model != perfect (meta)model” of modeling.
(using sample data is good enough, but not perfect)
P.S. I liked the article. Choice of the title - not so much.
P.P.S. Simplicity of a model is part of meta-model.
The author seems to be discussing optimizing for the wrong metric. That's not a problem of too much efficiency.
Excessive efficiency problems are different. They come from optimizing real output at the expense of robustness. Just-in-time systems have that flaw. Price/performance is great until there's some disruption, then it's terrible for a while.
Overfitting is another real problem, but again, a different one. Overfitting is when you try to model something with too complex a model and and up just encoding the original data in the model, which then has no predictive power.
Optimizing for the wrong metric, and what do about it, is an important issue. This note calls out that problem but then goes off in another direction.