You're mistaking the hot new fad of Transformers for "new science" and the field of NLP for a scientific field. Transformers have only taken over NLP because th

You're mistaking the hot new fad of Transformers for "new science" and the field of NLP for a scientific field. Transformers have only taken over NLP because that entire field of research has run out of new ideas, and has been desperately grasping for straws in the last twenty years. Consequently, NLP researchers have been jumping on this bandwagon or that ever since I can remember. I enquired about a PhD in NLP as soon as I finished my degree, in 2011, but by 2014, when I finished my Master's, I knew that was the last thing I wanted to study: a subject riddled with sloppy practices, flooded with shaky empirical results, without any real theory to guide it, without even any useful benchmarks or credible metrics that could justify the empirical direction. That is why the GPT-3s and the BERTs and all those data- and power-hungry monstrosities reign supreme: because NLP is built like a house of cards, with no scientific foundation whatsoever, so that anybody can claim anything they like and there is almost nobody left to challenge even the most outlandish claims. >> In 20 years a hacker-news poster will likely post the same comment, referencing the 2017 Transformer paper to enforce the idea that all the _good_ science in DL was done years ago. On the contrary, my expectation is that in 20 years from now people will point to Transformers and laugh and joke about how misguided people were "back then" to think that just building larger neural nets would lead to AGI, as they do now for earlier AI approaches. What I'm not convinced is that you are any different than the "non-expert" in your comment, to whom "it takes some time" for ideas to propagate. You are holding up Transformers as some kind of scientific breakthrough but that is only the most obvious conclusion to draw from the overhyping of the approach on social media and the lay press.