NLP is indeed at its top, NLP professors aren't :)
Imagine if you had stayed in academia and kept working in MT for the last two decades. First of all, now you would see how LLMs render all your work pretty much obsolete. That's already hard for many people. Not so much for me, as I consider myself to be rather adaptable, and maybe not for you either - you can embrace the new thing and start working on it yourself, right?
But the problem is that you can't. The main issue is not lack of explainability, but the sheer resources needed. In academia we can't run, let alone train, anything within even one or two orders of magnitude of ChatGPT. We can work with toy models, knowing that we won't get even remotely near the state of the art (many are now doing this, with the excuse of "green AI", sustainability and such, but that won't even hold much longer). Or we can work with the likes of ChatGPT as plain users, but then we are studying the responses of an "oracle" that we don't even have control of, and it starts looking more like religion than science.
Ten years ago an academic could beat the state of the art in MT and many other NLP tasks, now for many tasks that's just impossible (unless we count coming up with some clever prompt engineering, but again, religion). For those of us who were in this field because we liked it, not only to make a living, research has become quite unfulfilling.
Ah, okay. The scale is just too big for academia, makes sense.
I remember that in my study days (just masters, not PhD) we have been unable to beat Google Translate already for general tasks, but we could beat it on weird language pairs, just because we could get more training data (mostly by downloading some ebooks online) and tinker with the algorithm a bit.
But the scale argument - more data will be better - was true even back then... it was just easy to get better training data than Google for weird subtasks.
(Actually one of my professors is now Big Guy of that "European Commission Approved LLM" research project that was in news few months ago. I am ... interested how that turns out. https://openeurollm.eu/ )
Imagine if you had stayed in academia and kept working in MT for the last two decades. First of all, now you would see how LLMs render all your work pretty much obsolete. That's already hard for many people. Not so much for me, as I consider myself to be rather adaptable, and maybe not for you either - you can embrace the new thing and start working on it yourself, right?
But the problem is that you can't. The main issue is not lack of explainability, but the sheer resources needed. In academia we can't run, let alone train, anything within even one or two orders of magnitude of ChatGPT. We can work with toy models, knowing that we won't get even remotely near the state of the art (many are now doing this, with the excuse of "green AI", sustainability and such, but that won't even hold much longer). Or we can work with the likes of ChatGPT as plain users, but then we are studying the responses of an "oracle" that we don't even have control of, and it starts looking more like religion than science.
Ten years ago an academic could beat the state of the art in MT and many other NLP tasks, now for many tasks that's just impossible (unless we count coming up with some clever prompt engineering, but again, religion). For those of us who were in this field because we liked it, not only to make a living, research has become quite unfulfilling.