Need to let loose a primal scream without collecting footnotes first? 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.

Last week’s thread

(Semi-obligatory thanks to @dgerard for starting this)

  • @hrrrnghEnglish
    11•3 days ago

    I know this shouldn’t be surprising, but I still cannot believe people really bounce questions off LLMs like they’re talking to a real person. https://ai.stackexchange.com/questions/47183/are-llms-unlikely-to-be-useful-to-generate-any-scientific-discovery

    I have just read this paper: Ziwei Xu, Sanjay Jain, Mohan Kankanhalli, “Hallucination is Inevitable: An Innate Limitation of Large Language Models”, submitted on 22 Jan 2024.

    It says there is a ground truth ideal function that gives every possible true output/fact to any given input/question, and no matter how you train your model, there is always space for misapproximations coming from missing data to formulate, and the more complex the data, the larger the space for the model to hallucinate.

    Then he immediately follows up with:

    Then I started to discuss with o1. [ . . . ] It says yes.

    Then I asked o1 [ . . . ], to which o1 says yes [ . . . ]. Then it says [ . . . ].

    Then I asked o1 [ . . . ], to which it says yes too.

    I’m not a teacher but I feel like my brain would explode if a student asked me to answer a question they arrived at after an LLM misled them on like 10 of their previous questions.

    • @swlabrEnglish
      12•3 days ago

      I’m not a teacher but I feel like my brain would explode if a student asked me to answer a question they arrived at after an LLM misled them on like 10 of their previous questions.

      When I was a lab demonstrator in university, one of the lab exercises was somewhat hard to get right, and our team noticed a huge raft of plagiarism. Not only were multiple students using the exact same code, but it was code that didn’t even work! The course had a policy of instant failure with evidence of plagiarism, which was made clear at the start of the course, and yet this just kept happening. To top it all off, the lab exercises didn’t change from year to year, so the same code kept showing up! This plus a number of other incidents broke me and made me realise I didn’t have it in me to be this kind of educator.

      It would be great if this was all a big psyop by big tech to gaslight educators into ending their careers for some kind of mass brainwashing conspiracy, because at least there would be some intention behind it. Instead, we’re poisoning collective consciousness with cyberslop because we think one day the virtual dumbass will reach enlightenment. We don’t actually have a way for that to happen, so we’re just going to metaphorically bash our own heads against rocks in hopes we glitch ourselves into the singularity.

      So yeah in short my head would explode too.