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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/)OH
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  • You don't need to bribe a judge.

    You need enough money to have a team of lawyers grind through the evidence and find what's been hidden.

    Compare this to having a public defender with limited resources. They basically have to trust the DA's office.

    What's depressing about this is the DA's office is so used to getting away with this shady shit, that they can't do their job properly even when they know they're under a higher level of scrutiny. Think of all the average Joes that have been fucked over by these guys.

    Rich persons justice isn't really about bribing your way out of things. It's about having enough resources that you can force the system to behave, for you, in the way that it's meant to.

    This is instead of the usual process that just steamrolls over every poor bastard that ends up in court.

  • It also makes the existing voter suppression much more effective.

    "Oh sorry, none of the black districts have enough staff to run all the polling stations. I guess you'll have to travel an hour and then queue outdoors for four hours if you want to vote.

    Yes, it is illegal for anyone to bring you water, we don't want them to influence your vote, do we?"

  • I'm not describing binary classification, I'm describing multiclass. "Group classification" isn't really a thing. Yes, your ml system probably guesses what kind of plant it is and then looks up the ediblity of components.

    The problem with this is how they will handle rare plants that aren't in the dataset, or that are in the dataset but with insufficient data to be recognised.

    Because multiclass assumes that it's seen representative data on all possible outputs (e.g. plant types) it will tend to be dangerously confident on plant types it hasn't seen before.

    This is because it can rule out other classes. E.g. if you're trying to classify as rose, tulip, or daisy and you get a bramble, your classifier is likely to be very certain it's a rose because tulips and daisies don't have thorns. So your softmax score is likely to show heavy confidence in rose even though it's actually none of them.

    This is exactly what can go wrong when you try to use the softmax/standard multiclass approach and come across an interesting rare mushroom or wild carrot. You don't want it to guess which type of plant in the database it's most like, even if this guess comes with scores, you want it to say that it genuinely doesn't know and you shouldn't eat it.

  • The key issue here is that 'level of certainty' doesn't really mean what you would like it to.

    You get back a number yes, but it can change according to what's visible in the background, the angle that the plants at, how close is it to the camera, and how nice the camera is you're using (professional photographers use expensive cameras and take shots of different things to everyone else).

    Interpreting this score as "how safe is it to eat the plant" is a really bad idea. You will still eat the wrong plant. These scores can lead to very confident random guessing when you show it a plant it's never seen before.

    And no, softmax is a trick for making the scores all sum to one, so you get back a confidence for every possible thing the image could be of.

  • Because there's all these fertilized eggs that don't become people. If you believe life begins at conception, then IVF kills a lot.

    It's all unscientific nonsense, and requires you to ignore how many fertilized eggs don't become a viable fetus, but the anti-abortion stance has never been about science. It's about control.

  • It's worth saying that ml is in a very different position to most of academic publishing.

    All of the serious journals are free to publish and fully open access and a significant amount of publication includes enough code that things are mostly replicable. GitHub has done wonders for our field. Also many tech companies use publications as an indication of prestige and go out of their way to publish stuff.

    We're still drowning in too many papers and 95% of everything is shit, but that's every field really. Talking to musk on twitter is the not right place for a nuanced discussion about publication.

  • That would be fine, if people weren't using LLMs to write code, or to do school work,

    But they are. So it's important to write these articles that say "if you keep using a chainsaw to drive nails, here are the limitations you need to be aware of."

  • There's a reason terf stands for trans exclusionary.

    They don't really believe in some bullshit biological essentialism where trans men and cis women are categorised together. They just want all trans people gone. Excluded from society.

    Biological essentialism is just an excuse to be dicks to trans people.

  • It's super hard to get involved as a UI person. If you're a developer, you can just rock up to a project and fix bugs, and if you follow the coding style they'll probably get accepted.

    If you want to successfully contribute as a UI person you have to convince a bunch of developers that you know what they should be doing better than they do. It basically never happens.

  • It's a consequence of parliamentary sovereignty.

    Parliament can always dissolve itself and call an election, and it's an important mechanism for getting rid of the government.

    The problem is that the prime minister also has a majority in parliament, and that means he can make parliament dissolve itself when he likes.

    This was actually a problem for Johnson. Initially, he didn't have enough of a majority and it wasn't clear he could call an election without Corbyn's support.