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Overlearning/Overfitting is a term from machine learning/statistics that means that a learning system fits its learning data so well that it does not generalize
by jorleif 15y ago
Overlearning/Overfitting is a term from machine learning/statistics that means that a learning system fits its learning data so well that it does not generalize to new data. The examples given in the article seem like perfect examples of this, e.g. elected officials fit the election process very well, but do not "generalize" to leading a country.
- loup-vaillant 15y agoAh. I didn't see it as jargon. I recognize the term is accurate, but my main point still stands: the connotation is still there, and still does suggest we should learn less about those systems if we want to preserve their original purpose. Worse, the end of the post suggest we have to preserve things like schools, markets and elections, even though we learned enough to spoil them. Therefore, the only thing left to sacrifice is learning itself. That would really really sucks: I'm curious, and I always want to learn more. There are many things I would sacrifice before I accept to put hard limits on what I am allowed to learn.