every couple decades, there is a new approach that promises to do solve more of the language understanding problems with less of humans actually thinking about

every couple decades, there is a new approach that promises to do solve more of the language understanding problems with less of humans actually thinking about and understanding the inner workings of language. Each time we get a phenomenon which is best described with the hype cycle analogy, namely consistent progress of what it can do, along with even faster progress of what people expect from the technique, until the actual progress cannot keep up with expectations and we settle at a new (much progressed but much disillusioned) level. BERT, GPT-2 and now GPT-3 have brought us to the tail end of such a hype cycle, with the most enthusiastic DNN enthusiasts still claiming that it's understanding meaning and general AI isn't far off, whereas the folks who give out ACL Best Paper awards (all long-time experts in their field) probably sigh and are thankful for a paper that breaks with the hype and tells the overenthusiasts that there's still stuff left to understand and discover. Do we need mediocre philosophical papers when we can have mediocre technical papers (or even fairly decent technical papers) instead? Hell no. But do we occasionally need a good and thoughtful philosophical paper to sober ourselves up? Admittedly yes.