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Cake day: October 17th, 2023

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  • exactly: It’s “open source” like android. The core android is open source (in many cases because they are required to), but that does not include anything that makes the actual system work for normal users. The core android is open source (“Android Open Source Project”), but that includes practically nothing: Essentially the stuff that is in there are things that have to be open source (like the linux kernel they use). However, if you want to have the system “practically useable” you need a lot more, which is usually the “Google Mobile Services”, which are proprietary. You are also generally required to install all items in the GMS, i.e. even if you only need the play store, you still have to install google chrome.

    Further, the android name and logo are trademarked by google, so even if you want to roll your own android, you would not be allowed to call it android. WearOS is essentially the same thing: The android subsystem is open, the actual thing you call WearOS (plus trademarks, etc.) are not.



  • train one with all the Nintendo leaks

    This is fine

    generate some Zelda art and a new Mario title

    This is copyright infringement.

    The ruling in japan (and as I predict also in other countries) is that the act of training a model (which is just a statistical estimator) is not copyrightable, so cannot be copyright infringement. This is already standard practice for everything else: You cannot copyright a mathematical function, regardless of how much data you use to fit to it (that is sensible: CERN has fit physics models to petabytes worth of data, that doesn’t mean they hold a copyright on laws of nature, they just hold the copyright on the data itself). However, if you generate something that is copyrighted, that item is still copyrighted: It doesn’t matter whether you used an AI image generator, photoshop, or a tattoo gun.


  • The problem is that the model is actually doing exactly what it’s supposed to, it’s just not what openai wants it to do. The reason the prompt extraction method works is because the underlying statistical model gets shifted far outside the domain of “real” language. In that case the correct maximizing posterior becomes a sample from the prior (here that would be a sample from the dataset, this is combined with things like repetition penalties).

    This is the correct way a statistical estimator is supposed to work, but not the way you want it to work. That’s also why they can’t really fix this: there’s nothing broken to begin with (and “unbreaking” it would almost surely blow something take up)