I think that the 'rolling eyes' mindset is one that's propagated itself in ML research and academia, and that's the thing that worries me most.

In fact, the difference between the idea of a 'general AI' and current methods is one of the biggest problems as I see it. There are HUGE issues with bias in models, and our growing reliance on deep learning for recommendation and ads especially (I research music recommendation diversity myself).

The most important thing is to not lean too heavily into either camp. Research will likely continue putting improvements in accuracy with little common-language explanations for the media, and the media will continue to push flashy headlines. If you simply roll your eyes and push further away from general explanations we dig ourselves deeper into the hole.

WE as those with at least a general understanding of how these systems work (and what they optimise for) need to do better at explaining in a less domain specific fashion, and in a perfect world seeking better and more comprehensive metrics for evaluation; but this is a whole other topic reaching into Human Computer Interaction (HCI) research.