• 4 Posts
  • 685 Comments
Joined 2 years ago
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Cake day: June 1st, 2023

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    • Cleaner for your penis: sitting down eliminates the last drops remaining that are common when standing up
    • Cleaner for the bathroom: zero risk foreskin messing up your aim and having pee hit areas that don’t get washed by the flush
    • Better urine elimination for men that have prostate issues or lower urinary tract symptoms. I don’t, but it’s a factor
    • More chill to sit down and check your phone while in the bathroom





  • An interesting point - I checked, and as far as I can tell, non-citizens can be charged with treason in the U.K, so long as they are considered under the jurisdiction of the U.K - “alien residents” for example are covered, and probably temporary visitors to the country as well. It would likely be up to judicial interpretation whether attempting the coup virtually would qualify, but I’d assume it might.

    The serious take would be that this comment is too mild to qualify for treason, but one could always hope.










  • No, the old model does not have the training data. It only has “model weights”. You can conceptualize those as the abstract rules that the old model learned when it read the training data. By design, they are not supposed to memorize their training data.

    I expressed myself poorly, this is what I meant - it has the “essence” of the training data, but of course not the verbatim training data.

    To outperform the old model, the new model needs more than what the old model learned. It needs primary sources, ie the training data itself. Which is going to be deleted.

    I wonder how valuable in relative terms the old training data is to the process, compared to just the new training data. I can’t answer it, but it would be interesting to know.



  • I guess it depends on how important old data is when building upon new models, which I fully admit I don’t know the answer to. As I understand it though, new models are not trained fully from scratch, but instead are a continuation of the older model trained with new techniques/new data.

    To speculate, I guess not having the older data present in the new training stages might make the attributes of that data be less pronounced in the new output model.

    Maybe they could cheat the system by trying to distill that data out of the older models and put that into the training data, but I guess the risk of model collapse is not-insignificant there

    Again, limited understanding here, take everything I speculate with a grain of salt