I mean it in the education and learning sense, that for knowledge to be understood it must be tailored to the person asking the question. Answering a question a

I mean it in the education and learning sense, that for knowledge to be understood it must be tailored to the person asking the question. Answering a question about physics ideally should look different for someone with a high school education vs someone who's in the field, or from someone who only has passing interest vs one that expresses interest through continued engagement. All info is filtered in some fashion, else a simple query would lead to an impossibly dense tome ('baking an apple pie from scratch'). As an edtech person that started a news company to try to solve this, one of my pet peeves about news is that it doesn't adopt an education mindset / approach despite it's mission being an educational one at its core. Part of that is due to search being what drives traffic, and that means there's a lack of relationship to the reader. But the root is internal as news and journalism is still too stuck in newspaper-mind of bespoke, reverse pyramid one-off articles that act as islands of information, delivered to an audience they have no ongoing relationship with and who have no means of getting follow-up. It's a cultural holdover that's bound up in relationship to it's old medium, not what optimally teaches. Generated results need not be in the form of articles, that's one of the constraints that ML lifts. You could give people some generated text but also give people drill-down tools, letting people expand on a simple summary, or read in dialectic or debate style in areas where facts are not settled or multiple viewpoints exist. Just gotta expand your POV a bit and ML generated results represent an opportunity for incredible leverage educating the world. Anyway if someone in ML at OpenAI/Google/etc wants to get deeper into this, please reach out!