[varelaseb]

Just a random thought:

Understanding the limitations is a complicated thing in tech. You can finnangle most systems into doing mostly anything, as inefficient as that may prove to be.

The question then becomes up to what point is it "a reasonably better than most others" solution. And that's a question of an understanding of a field, not a space in the field.

[godelski]

  > is a complicated thing in tech

That's the point. Understanding complex things is what experts are supposed to do.

  > You can finnangle most systems into doing mostly anything

"most" is doing a lot of heavy lifting here and I think the point you're making isn't discrediting my point. Sure you can hamfist a lot of things into working but an expert should know when to use better tools. Being able to identify what would end up as a very hacky solution from one paradigm but could be efficient and/or elegant in another is what an expert should be able to identify. Essentially, are they able to reduce technical debt even before that debt is taken on?

  > an understanding of a field, not a space in the field.

Would you mind clarifying the difference? I agree these are different things but I'm not sure why understanding the limitations would imply not having narrower domain knowledge. Sure, in ML knowing the advantages of convolutions over transformers and vise versa is good. But if you're working on LLMs, ViTs, or anything else it is still good to know what the limitations of transformer models are, and specifically what attention can and cannot do. We should be able to get more and more narrow too. An expert will be able to understand the nuances of specific evaluation methods: metrics, measures, datasets, and other forms of analysis. Being able to discuss nuance and detail is how you determine if someone has expertise or not. IME it tends to be pretty easy to identify experts (even in other fields) due to their ability and frequency of discussing nuances.