Presumably the argument is that training a neural net from a basis of complete ignorance is inefficient because we have facts with which we can initialize the model.
So far as applicability to TFA, we can and probably should initialize or bias models that select candidates so their inferences reflect our values.
If the idea were to write an AI to win at a harder game, it would make more sense to add whatever biases you can. You might get better performance that way. Or maybe that's what they thought back when that story was written? Game AI was nothing like we think about it now.
Except that in nearly all nontrivial topics we only see a small sample of reality.
So even if we are lucky enough to be starting out with a set of only verifiable, reproducible, true facts, we are still biased in their selection.