In reading the r/machinelearning posts I often feel that there are two communities here:

1. People who are interested in getting the last 2% of performance on their big data sets, using methods they can use *today*,
2. Researchers who are interested in discussing new ideas and algorithms, that may be only only demonstrated on MNIST or may not be practical for years to come.

I waste time skipping past comments from the other community (the one I’m not interested in), and sometimes find that I cannot tell even from the title of a thread which community it appeals to. Open the thread, scan, close, a minute wasted.

I suspect that explicitly recognizing these two communities would both save everyone a bit of time, but also might encourage some deeper conversations (at least on the research side – discussions won’t be buried with all the “doesn’t work as well as existing methods my real-world big dataset” comments).

There are already several ML related subreddits (LearnML, Deeplearning, ?). But this one appears to be the only one that attracts real researchers (I know that some of you are here), and if so is the only one that has this particular dichotomy.

What do you think?