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Cake day: June 27th, 2023

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  • I know little of the ins-and-outs of scientific publishing, but that didn’t stop me from having a dumb thought: could the fediverse be a potential solution? Each university or research group could host their own instance of some software specifically for publishing papers, papers can cross-link citations to papers on other instances, people can make comments across instances that are tied to their own identities from their home instance, paper revisions can be tracked easily and bad citations spotted when a paper is updated or retracted, that kind of thing. The currency then becomes the reputation of the organizations and individuals, and this opens up a ton of data for automated analysis. I just don’t know enough to know what problems would arise.




  • I’m not saying planned obsolescence isn’t a thing (because it is), but that’s not the only reason. Making phones smaller, lighter, faster, and more feature-dense all mean that the phone has to be built with tighter manufacturing and operating tolerances. Faster chips are more prone to heat and vibration damage. Higher power requirements means the battery has a larger charge/discharge cycle. And unfortunately, tighter operating tolerances mean that they can fall out of those tolerances much more easily.

    They get dropped, shaken, exposed to large environmental temperature swings, charged in wonky ways, exposed to hand oils and other kinds of dirt, and a slew of other evils. Older phones that didn’t have such tight tolerances could handle all that better. Old Nokia phones weren’t built to be indestructible, they are just such simple phones that there isn’t much to break; but there’s a reason people don’t use them much anymore. You can still get simple feature phones, but the fact remains that they don’t sell well, so not many are made, and the ones that are made don’t have a lot of time and money invested in them.

    Now Voyager is an extremely simple computer, made with technology that has huge tolerances, in an environment that is mostly consistent and known ahead of time so the design can deliberately account for it, had lots of testing, didn’t have to take mass production into its design consideration, didn’t have to make cost trade-offs, and has a dedicated engineering team to keep it going. It is still impressive that it has lasted this long, but that is more a testament to the incredible work that was and is being put into it than to the technology behind it.


  • If I interpret your question correctly, you are basically asking what the practical difference is between interpreting a model as a reflection of reality and interpreting a model as merely a mathematical tool.

    A mathematical model, at its core, is used to allow us to make testable predictions about our observations. Interpretations of that model into some kind of explanation about the fundamental nature of reality is more the realm of philosophy. That philosophy can loop back into producing more mathematical models, but the models themselves only describe behavior, not nature.

    A model by nature is an analogy, and analogies are always reductionist. Like any analogy, if you poke it hard enough, it starts to fall apart. They make assumptions, they do their best to plug holes, they try to come as close as they can to mirroring the behavior of our observations, but they always fall short somewhere. Relativity and Quantum Chromodynamics are both good examples. Both are very, very good at describing behavior within certain boundaries, but fall completely apart when you step outside of them. (Both, to expand on the example, use constants that are impericaly determined, but we have no idea where they come from.)

    The danger is in when you start to assume that a model of reality is reality itself, and you forget that it’s just a best guess of behaviors. Then you get statements like you first made. “Relativity assumes time is a dimension. The model for that works. Therefore time must be a dimension in reality. That must mean that not treating time as a dimension anywhere must be wrong.” That line of thinking, though, forgets that a model is only correct within the scope of the model itself. As soon as you introduce a new model, any assumptions made by other models are no longer relevant. That will pigeonhole your thinking and lead you to incorrect conclusions due to mixed analogies.

    That is how you get statements like your first one. “Model A treats time like an illusion, but model B treats time like a dimension. Ergo, all dimensions are illusions .” That is mixing analogies.