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Joined 10 months ago
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Cake day: November 22nd, 2023

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  • It’s not about what humans “like,” it’s about the human bodies’ internal operating temperature and using that as a reference point, the same way that Celsius is about the states of matter of water . Fahrenheit is useful in medicine for that reason, while Celsius is useful anytime a comparison to water is helpful, and beyond that, it’s really just whatever you grew up with. Using a system based on what water “likes” is equally as useless unless you grew up using it as your reference point for temperature in your daily life. Neither 75 Fahrenheit or 23.8889 Celsius tell me whether or not I’m going to need a jacket today unless I’ve already experienced said temperature and use that scale in my daily life.


  • Another Millennial here, so take that how you will, but I agree. I think that Gen Z is very tech literate, but only in specific areas that may not translate to other areas of competency that are what we think of when we say “tech savvy” - especially when you start talking about job skills.

    I think Boomers especially see anybody who can work a smartphone as some sort of computer wizard, while the truth is that Gen Z grew up with it and were immersed in the tech, so of course they’re good with it. What they didn’t grow up with was having to type on a physical keyboard and monkey around with the finer points of how a computer works just to get it to do the thing, so of course they’re not as skilled at it.



  • Because we’re talking pattern recognition levels of learning. At best, they’re the equivalent of parrots mimicking human speech. They take inputs and output data based on the statistical averages from their training sets - collaging pieces of their training into what they think is the right answer. And I use the word think here loosely, as this is the exact same process that the Gaussian blur tool in Photoshop uses.

    This matters in the context of the fact that these companies are trying to profit off of the output of these programs. If somebody with an eidetic memory is trying to sell pieces of works that they’ve consumed as their own - or even somebody copy-pasting bits from Clif Notes - then they should get in trouble; the same as these companies.

    Given A and B, we can understand C. But an LLM will only be able to give you AB, A(b), and B(a). And they’ve even been just spitting out A and B wholesale, proving that they retain their training data and will regurgitate the entirety of copyrighted material.



  • The argument that these models learn in a way that’s similar to how humans do is absolutely false, and the idea that they discard their training data and produce new content is demonstrably incorrect. These models can and do regurgitate their training data, including copyrighted characters.

    And these things don’t learn styles, techniques, or concepts. They effectively learn statistical averages and patterns and collage them together. I’ve gotten to the point where I can guess what model of image generator was used based on the same repeated mistakes that they make every time. Take a look at any generated image, and you won’t be able to identify where a light source is because the shadows come from all different directions. These things don’t understand the concept of a shadow or lighting, they just know that statistically lighter pixels are followed by darker pixels of the same hue and that some places have collections of lighter pixels. I recently heard about an ai that scientists had trained to identify pictures of wolves that was working with incredible accuracy. When they went in to figure out how it was identifying wolves from dogs like huskies so well, they found that it wasn’t even looking at the wolves at all. 100% of the images of wolves in its training data had snowy backgrounds, so it was simply searching for concentrations of white pixels (and therefore snow) in the image to determine whether or not a picture was of wolves or not.






  • Except that’s not at all what’s happening here. We’re not talking about somebody we know personally with their permission or anything, we’re talking about an actress who got into pornography after having an emotional video go viral many years ago. Her dead name has nothing to do with that, and if you had even left out the fact that she’s trans, most people probably could’ve figured it out if they even bothered to go check out the original video. Abd if they didn’t? It wouldn’t make a difference in their knowledge of the subject. They’d still know that a woman who had an emotional video go viral years ago later became a porn actress. All her dead name adds to this is a possibly paparazzi style invasion of her privacy.


  • Does it add any useful context, though? I don’t know either name but I do remember the “Leave Britney alone” video being a thing (and the fact that the person in the video turned out to be right all along when the truth about Britney’s situation came out years later), so the added context that she’s trans and what her dead name was is meaningless to me other than to say, “She used to be a man. She’s a woman now, but she was a man before. Did you know that? That she was once a man? Because she was. Here’s what her name was.”

    As a trans woman, whose safety is so dependent on being able to go stealth in society, if I found out people were going around talking about me like this, I’d take a rusty icepick and make sure that they never think in words ever again. Lack of malicious intent doesn’t mean that no harm was caused. Your threat index is not universal.

    This could have very easily been left at “Trans woman X got into porn after her viral video Y” and there would be all the context needed to figure out who they were and what video they were in without using their dead name. Hell, you probably wouldn’t even have to point out that she’s trans for people to figure it out. Cis people treat the privacy of trans people the same way that the paparazzi treats the privacy of celebrities.