> It will also fail to capture the language and the norms of countries and peoples that have less access to the internet and thus a smaller linguistic footprint

> It will also fail to capture the language and the norms of countries and peoples that have less access to the internet and thus a smaller linguistic footprint online. The result is that AI-generated language will be homogenized, reflecting the practices of the richest countries and communities. It seems amusing to read this when the same person and her followers hounded Yann LeCun for pointing out the same thing with image models. Anyway, this seems interesting. But I am not sure how you solve this. Do we take representative dataset according to population of a place? Also, assuming this is limited to a single language. How can AI generated language account for nuances from regional differences at the same time in a common model. Isn't what the author asking for here is kind of train, en_US, en_GB, en_IN separately here. For things like completion don't language models already account for this?