One of these people has a good reason for preferring his terminology (e.g. it's standard, it's what everyone in the field actually uses, etc.) "Scott, can you define what a qubit is?", etc.
it's what everyone in the field actually uses
Yes, but you are talking to people outside of the field.
For example you tend to use the expression "prediction model" as an antonym to "causal model". That may be standard in your field, but that's not what it means outside of it.
Yann LeCun, now of Facebook, was interviewed by The Register. It is interesting that his view of AI is apparently that of a prediction tool:
"In some ways you could say intelligence is all about prediction," he explained. "What you can identify in intelligence is it can predict what is going to happen in the world with more accuracy and more time horizon than others."
rather than of a world optimizer. This is not very surprising, given his background in handwriting and image recognition. This "AI as intelligence augmentation" view appears to be prevalent among the AI researchers in general.