• 22 Posts
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Joined 2 years ago
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Cake day: April 27th, 2024

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  • I can tell you the two languages I would learn right now if I had the spare cycles.

    1. Zig is a C-replacement language that took a no-AI stance early on. It doesn’t have compiler-assured memory safety like Rust, but it does try to revamp structures (like alloc or error handling) in a way that makes it more obvious to the programmer what is happening. I’d love to learn more, but cycles…
    2. Gleam is a language that is part of the Erlang/BEAM ecosystem. It’s a functional language, so it’s a bit of a mindfuck if you come from a Python-/C-esque perspective, but if you like formal logic it might well be up your alley. It has a good web tutorial if you’re interested. Neither Gleam nor its runtime Erlang-OTP have taken a stance on AI as far as I know (Elixir has a permissive stance), but at least I’ve seen no red flags as of yet. The people at Gleam just generally give me a vibe of being deliberate and careful with their code.


  • Sorry for being late to the party, but I had to give my two cents on AI detectors. (specifically GPTZero & co., watermarking is different) I think it’s a bad idea to recommend them. These companies are overpromising their accuracy and essentially selling false certainty. This will also not fade over time since the inaccuracy is baked into the tech.

    Currently the performance may be good for basic use cases, but recent papers seem to indicate you can still dazzle them by burning more tokens or explicitly prompting against detectors. But from what I understand about GenAI training* this is an eternal cat & mouse game, since the AI vendors don’t want their output to be detectable.

    what I understand about GenAI training

    A basic technique of training a generative model is training a discriminative model alongside it. The gen model tries to synthesize output and the discriminative model tries to distinguish it from actual training data. The models are then trained alongside each other, building better and better discriminator models to sus out better and better generative ones.

    If these detector companies had some kind of breakthrough (better techniques, better datasets, whatever) to sus out generated output, they would be bought up to improve GenAI training. More likely, they don’t. Maybe they can dial into some typical usecases for specific models and perform highly there, but that will fail to generalize for the edge cases.




  • In my book socialism is about workers controlling the means of production, and historical socialists did not agree how that would work in detail.

    Libertarianism on the other hand is primarily about the rejection of personal restrictions, stuff like: “I should be allowed to freely copy and share media I have legally acquired.” or “I shouldn’t be forced to pay taxes.” This individualist thinking makes it usually unable to adress structural inequality.


  • But he also literally has a book titled “Information Doesn’t Want to Be Free”.

    That sure sounds like a zinger, but going off Wikipedia he does argue in that book for a loosening of copyright, which is pretty much what I meant. I don’t hate those positions though I’ll note they’re more libertarian than socialist. He has a whole personal freedom + monopoly control shtick, which works great for me in a lot of scenarios, but doesn’t quite work on GenAI tech.

    You can call me exceptionalist, but I think LLMs stand out because the adoption of the tech basically requires very capital-intensive and thus centralized operations for model training. This makes whoever runs them (likely capital or a state) a powerful arbiter of information space, maybe best comparable with algorithmic feeds in monopolistic social media.

    I think it is necessary to say: “Hey, we as a society shouldn’t run things like that. There are better ways of distributing information that work just fine.” A tech-neutral attitude can’t achieve that.