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InitialsDiceBearhttps://github.com/dicebear/dicebearhttps://creativecommons.org/publicdomain/zero/1.0/„Initials” (https://github.com/dicebear/dicebear) by „DiceBear”, licensed under „CC0 1.0” (https://creativecommons.org/publicdomain/zero/1.0/)GR
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  • Meanwhile a pansexual could have attraction to the exact same people but wouldn’t ascribe it to gender, just individuals

    Thanks, that's the first explanation of the difference that's actually managed to reduce my confusion.

  • The reason "ACAB" is a thing is that "good cops" pretty quickly either cease to be good or cease to be cops (e.g. Frank Serpico).

    That's why the "a few bad apples" analogy applies: because they spoil the bunch. The whole bunch, without exception.

  • ...the new Trump-appointed U.S. attorney offered an unusual plea deal...

    ...

    But when U.S. Attorney Bill Essayli took office a few months later, federal prosecutors offered Kirk a plea deal — a dismissal of the felony if Kirk pleaded guilty to a misdemeanor, and a recommendation of one year of probation. A judge agreed to the lessened charge but sentenced Kirk to four months in prison on Monday.

  • There is an implicit assumption here that models are being 'trained', perhaps because LLMs are a hot topic. By models we are usually talking about things like decision trees or regression models or Markov models that put in risk probabilities of various eventualities based on patient characteristics.

    [Citation needed]

    If these things were being based on traditional AI techniques instead of neural network techniques, why are they getting implemented now (when, as you say, LLMs are the hot topic) instead of a decade or so ago when that other stuff was in vogue?

    I think the assumption that they're using training data is a very good one in the absence of evidence to the contrary.

  • I'd like to know what specific steps are being taken to remove the bias from the training data, then. You cannot just feed the model a big spreadsheet of human decisions up to this point because the current system is itself biased; all you'll get if you do that is a tool that's more consistent in applying the same systemic skew.

  • the suffragette movements in the West

    You mean this suffragette movement?

    When by 1903 women in Britain had not been enfranchised, Pankhurst decided that women had to "do the work ourselves"; the WSPU motto became "deeds, not words". The suffragettes heckled politicians, tried to storm parliament, were attacked and sexually assaulted during battles with the police, chained themselves to railings, smashed windows, carried out a nationwide bombing and arson campaign...


    the Salt Marches in India,

    Why is India partitioned, then? (Hint: take a look at the Muslim League's tactics and goals.)


    the Singing revolution in the Baltics

    Kind of a special case; the USSR collapsed all by itself and didn't try to oppose them.

  • Cities in pretty much the entire country are running with an understaffed police force.

    Counterpoint: Americans are already overpoliced and the "understaffing" is only relative to the authoritarians' desire to make us even more so.

  • Nuclear weapons can exterminate a populace, but they cannot occupy, pacify, and rehabilitate one. For that, you need actual boots on the ground, which requires logistics. China could have the biggest military in the world, but that doesn't mean jack-shit unless they've got a blue-water navy to ship them over here, and they don't.

    Remember, this discussion is not about China defeating the US. This discussion is about China liberating the US from the MAGAs the way the US liberated Germany from the Nazis. Nukes and drones are categorically useless for that purpose.