Want to wade into the sandy 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.)

  • Dan Lyke@researchbuzz.masto.host
    7·
    23 days ago

    @BioMan @BurgersMcSlopshot I’ve recently had the chance to look at someone who was really proud that they used a neural net to create a forward/backward mapping through a space of 3 controls to ~50 controls that actually drove the system.

    I took their files, loaded them into Onnx, and … they would have been way better off using PCA, because the neural net is approximating a simple linear system.

    I think this is relevant, and the sort of “don’t understand” we’re talking about.

    • BioMan@awful.systemsEnglish
      5·
      22 days ago

      That’s an indication that the problem is a problem that is not well-served by a neural network. They are useful for approximating highly nonlinear functions with lots of inputs (and will not work well outside the range of inputs that you approximate within), not simple linear systems. The goal of recent ML has been to reduce as many problems to high dimensional highly nonlinear curve fitting as possible, with some great successes (machine translation, image recognition) and some not so great (shhhhh don’t tell the investors!)

      • Dan Lyke@researchbuzz.masto.host
        4·
        22 days ago

        @BioMan exactly. And yet here we are hammering square pegs into round holes.

        If this product makes it to market in its current shape that’s gonna increase hardware costs, all because the blindly throw ML at everything bandwagon.

        • rook@awful.systemsEnglish
          4·
          22 days ago

          I’m reminded of people back in the day using map/reduce via hadoop to solve issues that could just as well be done with postgres or even sqlite and a sprinkling of sql, because that’s how google did it and no-one has any idea what “big data” really is.

          Similarly, turning simple network applications into a hideous armada of microservices on a distributed kubernetes cluster, because that’s how google did it and people outside of giant tech companies don’t really know what that sort of scalability is for.

          And here we are in the age of readily accessible neural network software. This too will pass, and we’ll get a new sledgehammer for walnut-opening in due course.