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Joined 7 months ago
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Cake day: November 19th, 2023

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  • American kettles are significantly worse than British kettles. They run at lower voltage and lower amperage, so they take much longer to boil water.

    Given the choice between using a multipurpose microwave to do one more thing, and buying a separate appliance that is no faster, choosing to use the device you already own is entirely appropriate.





  • In case you aren’t joking, I believe the relevant statement is that acceleration and “a change in velocity over time” are the same thing.

    If you imagine driving a car forward in a straight line, pressing the gas will make you accelerate (velocity becomes more forward). Pressing the brake will also make you accelerate (velocity becomes less forward). Turning the steering wheel will also make you accelerate (velocity points more to the left/more to the right).

    While I’m at it, you can do physics computations in a rotating frame of reference, but it produces some fictious forces, and gets really wacky quickly. An easy example is that anything far enough away from the axis of rotation is moving faster than the speed of light.






  • prime_number_314159@lemmy.worldtoScience Memes@mander.xyzshrimp is bugs
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    2 months ago

    Like today’s computer scientists, early biologists sucked at inventing new words, and simply reused existing ones. “Berry” in common language is a small, usually sweet and edible, fruit. Strawberries, blueberries, blackberries and raspberries are all berries.

    Then biologists came along and decided, actually, strawberries, raspberries and blackberries are out, but watermelon and bananas are in, because the size of the fruit doesn’t matter, only the placement of the seeds decides whether something is a proper, scientific berry.

    A similar thing has happened with “fruit” and “vegetable”, where scientific fruits include cucumbers, eggplants, and pumpkins. Luckily, all three of these are also berries.

    I say we ignore them, and use words to mean sensible things.



  • The (really, really, really) big problem with the internet is that so much of it is garbage data. The number of false and misleading claims spread endlessly on the internet is huge. To rule those beliefs out of the data set, you need something that can grasp the nuances of published, peer-reviewed data that is deliberately misleading propaganda, and fringe conspiracy nuts that believe the Earth is controlled by lizards with planes, and only a spritz bottle full of vinegar can defeat them, and everything in between.

    There is no person, book, journal, website, newspaper, university, or government that has reliably produced good, consistent help on questions of science, religion, popular lies, unpopular truths, programming, human behavior, economic models, and many, many other things that continuously have an influence on our understanding of the world.

    We can’t build an LLM that won’t consistently be wrong until we can stop being consistently wrong.




  • I managed a CentOS system where someone accidentally deleted everything from /usr, so no lib64, and no bin. I didn’t have a way to get proper files at the time, so I hooked the drive up to my Arch system, made sure glibc matched, and copied yum and other tools from Arch.

    Booted the system, reinstalled a whole lot of yum packages, and… the thing still worked.

    That’s almost equivalent to a reinstall, though. As a broke college student, I had a laptop with a loose drive, that would fall out very easily. I set it up to load a few crucial things into a ramdisk at boot, so that I could browse the web and take notes even if the drive was disconnected, and it would still load images and things. I could pull the cover off and push the drive back in place to save files, but doing that every time I had class got really tiring, so I wanted it to run a little like a live system.


  • I tried typing this once before, but kept running into situations were I’m not sure if I’m just being condescending. These are the most obvious reasons this is a selfish and self destructive perspective:

    When you are old, children today will be the only people able to take care of you. Optimizing society so that there are many more old people than young people will create unfair burden on the next generation, and probably lead to horrific suffering for millions of people (probably including you).

    Children are best raised by stable, happy, healthy families, and they are more productive members of society (and happier) when that happens. Because we want the next generation to be happy and productive, aiding today’s parents helps us all tomorrow. Adding financial strain causes many negative effects for families, and therefor for children, and therefor for society at large.

    Unless you are extremely lucky, you probably faced issues in your own childhood that would have been lessened if your parents had more money. Wishing the same, but worse on the next generation is twisted.





  • What we have done is invented massive, automatic, no holds barred pattern recognition machines. LLMs use detected patterns in text to respond to questions. Image recognition is pattern recognition, with some of those patterns named things (like “cat”, or “book”). Image generation is a little different, but basically just flips the image recognition on its head, and edits images to look more like the patterns that it was taught to recognize.

    This can all do some cool stuff. There are some very helpful outcomes. It’s also (automatically, ruthlessly, and unknowingly) internalizing biases, preferences, attitudes and behaviors from the billion plus humans on the internet, and perpetuating them in all sorts of ways, some of which we don’t even know to look for.

    This makes its potential applications in medicine rather terrifying. Do thousands of doctors all think women are lying about their symptoms? Well, now your AI does too. Do thousands of doctors suggest more expensive treatments for some groups, and less expensive for others? AI can find that pattern.

    This is also true in law (I know there’s supposed to be no systemic bias in our court systems, but AI can find those patterns, too), engineering (any guesses how human engineers change their safety practices based on the area a bridge or dam will be installed in? AI will find out for us), etc, etc.

    The thing that makes AI bad for some use cases is that it never knows which patterns it is supposed to find, and which ones it isn’t supposed to find. Until we have better tools to tell it not to notice some of these things, and to scrub away a lot of the randomness that’s left behind inside popular models, there’s severe constraints on what it should be doing.