Want to wade into the snowy surf of the abyss? Have a sneer percolating in your system but not enough time/energy to make a whole post about it? Go forth and be mid: Welcome to the Stubsack, your first port of call for learning fresh Awful you’ll near-instantly regret.
Any awful.systems sub may be subsneered in this subthread, techtakes or no.
If your sneer seems higher quality than you thought, feel free to cut’n’paste it into its own post — there’s no quota for posting and the bar really isn’t that high.
The post Xitter web has spawned soo many “esoteric” right wing freaks, but there’s no appropriate sneer-space for them. I’m talking redscare-ish, reality challenged “culture critics” who write about everything but understand nothing. I’m talking about reply-guys who make the same 6 tweets about the same 3 subjects. They’re inescapable at this point, yet I don’t see them mocked (as much as they should be)
Like, there was one dude a while back who insisted that women couldn’t be surgeons because they didn’t believe in the moon or in stars? I think each and every one of these guys is uniquely fucked up and if I can’t escape them, I would love to sneer at them.
(Credit and/or blame to David Gerard for starting this. Merry Christmas, happy Hannukah, and happy holidays in general!)
dafuq?
Yeah, it's not like reviewers can just write "This paper is utter trash. Score: 2" unless ML is somehow an even worse field than I previously thought.
They referenced someone who had a paper get rejected from conferences six times, which to me is an indication that their idea just isn't that good. I don't mean this as a personal attack; everyone has bad ideas. It's just that at some point, you just have to cut your losses with a bad idea and instead use your time to develop better ideas.
So I am suspicious that when they say "constructive feedback", they don't mean "how do I make this idea good" but instead "what are the magic words that will get my paper accepted into a conference". ML has become a cutthroat publish-or-perish field, after all. It certainly won't help that LLMs are effectively trained to glaze the user at all times.