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Joined 9 months ago
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Cake day: November 19th, 2023

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  • Modern operating systems have made it take very little knowledge to connect to WiFi and browse the internet. If you want to use your computer for more than that, it can still take a longer learning process. I download 3D models for printing, and wanted an image for each model so I could find things more easily. In Linux, I can make such images with only about a hundred characters in the terminal. In Windows, I would either need to learn powershell, or make an image from each file by hand.

    The way I understand “learning Linux” these days is reimagining what a computer can do for you to include the rich powers of open source software, so that when you have a problem that computers are very good at, you recognize that there’s an obvious solution on Linux that Windows doesn’t have.













  • 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.



  • 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.