As far as I know, none of these 3 work specifically in NLP, most of their work is in image processing and to the best of my knowledge none of them have any background in linguistics.
Well, they are absolutely top AI researchers, so their opinion should count for a lot. If you specifically ask for people working on LLMs: Paul Christiano invented RLHF when he worked at OpenAI, and I'm pretty sure he also rejects the stochastic parrot analysis.
Their pricing page says that it costs them around 1$ to serve 80 searches. I really wonder how they arrive at that number. That seems shockingly expensive somehow.
> This seems like such a distant yet specific relationship type
I don't know much about it (never stepped foot outside EU/NA) but apparently some Asian countries have a pretty intricate system of kinship terms. For example Diagram IV in [1] shows different (Mandarin?) Chinese terms for what we would just call a "cousin" in English.
I also found the video by NativLang [2] on the topic pretty interesting.
scikit-learn (next to numpy) is the one library I use in every single project at work. Every time I consider switching away from python I am faced with the fact that I'd lose access to this workhorse of a library.
Of course it's not all sunshine and rainbows - I had my fair share of rummaging through its internals - but its API design is a de-facto standard for a reason.
My only recurring gripe is that the serialization story (basically just pickling everything) is not optimal.
I recently ran into this issue as well. Serialization of sklearn random forests results in absolutely massive files. I had to switch to lightgbm, which is 100x faster to load from a save file and about 20x smaller.
There is so much wrong with the api design of sklearn (how can one think "predict_proba" is a good function name?). I can understand this, since most of it was probably written by PhD students without the time and expertise to come up with a proper api; many of them without a CS background.[1]
These seem like minor gripes (reading your link) - and I don't even agree with them, seems like an ok use of mutable state (otherwise a separate object would be needed for hyperparameter state?). Maybe my expectations are low, but they way sklearn unifies the API across different estimators all across the library - that's already way above what you can expect - especially if you consider it to be "written by a bunch of phd students".
I didn't want to bag on sklearn (I've already bagged on pandas enough here), but for what it's worth I agree with you. It's, ahh, not the API I would've come up with. It's what everybody has standardized on, though, and maybe there's some value in that.
Currently on course 2/4 in the series and it's great. Every week starts with a reading assignment from the RL book followed by a series of videos (re-)explaining stuff. The videos themselves are very nicely structured, with clear outlook and summary at the start and end of them.
Sutton himself appears in a couple of videos and there are other great guest lectures with interesting insights about applications.
I live in Switzerland too and especially Youtube never seems to know whether to serve me ads in German, French or Italian (even though Google should know I almost exclusively consume English content).
So I can sometimes watch several videos in a row, where I get served the same ad in a different language.
But don't ads on YouTube follow the more classic advertisement 'demographics', where location is more relevant than language? And it may just be cheaper and easier to just target all of Switzerland?
This is where I feel a bit better about targeted advertising, if they're not able (as in at least not cheaply enough to be worth it) to serve me an ad in a language I understand, they still have a long way to go.
And that's in Switzerland where people have a lot of disposable income and thus must be more attractive to advertisers than most places, despite being relatively small.
