• 1 Post
  • 33 Comments
Joined 1 year ago
cake
Cake day: October 4th, 2023

help-circle
  • locallynonlinear@awful.systemsOPtoNotAwfulTech@awful.systemsWe can, protect artists
    link
    fedilink
    English
    arrow-up
    1
    arrow-down
    4
    ·
    edit-2
    11 months ago

    Ha! Nope, not buying it.

    nasty license Ironic, considering that their work directly builds upon Stable Diffusion.

    Funny you mention licenses, since stable diffusion and leading AI models were built on labor exploitation. When this issue is finally settled by law, history will not look back well on you.

    So I’m not allowed to have the discussion I’m currently having

    Doesn’t seem to prevent you from doing it anyways. Does any license slow you down? Nope.

    nor to include it in any Linux distro

    Not sure that’s true, but also unnecessary. Artists don’t care about this or need it to be. I think it’s a disengenous argument, made in the astronaut suit you wear on the high horse drawn from work you stole from other people.

    This is not only an admission of failure but a roadmap for anybody who wants to work around Nightshade.

    Sounds like an admission of success given that you have to step out of the shadows to tell artists on mastodon not to use it because, ahem, license issues???

    No. Listen. The point is to alter the economics, to make training on image from the internet actively dangerous. It doesn’t even take much. A small amount of internet data actively poisoned requires future models to use alignment to bypass it, increasing the marginal (thin) costs of training and cheating people out of their work.

    Shame on you dude.

    If you want to hurt the capitalists, consider exfiltrating weights directly, as was done with LLaMa, to ruin their moats.

    Good luck on competing in the arms race to use other people’s stuff.

    @self@awful.systems can we ban the grifter?









  • Adversarial attacks on training data for LLMs is in fact a real issue. You can very very effectively punch up with regards to the proportion of effect on trained system with even small samples of carefully crafter adversarial inputs. There are things that can counter act this, but all of those things increase costs, and LLMs are very sensitive to economics.

    Think of it this way. One, reason why humans don’t just learn everything is because we spend as much time filtering and refocusing our attention in order to preserve our sense of self in the face of adversarial inputs. It’s not perfect, again it changes economics, and at some point being wrong but consistent with our environment is still more important.

    I have no skepticism that LLMs learn or understand. They do. But crucially, like everything else we know of, they are in a critically dependent, asymmetrical relationship with their environment. The environment of their existence being our digital waste, so long as that waste contains the correct shapes.

    Long term I see regulation plus new economic realities wrt to digital data, not just to be nice or ethical, but because it’s the only way future systems can reach reliable and economical online learning. Maybe the right things happen for the wrong reasons.

    It’s funny to me just how much AI ends up demonstrating non equilibrium ecology at scale. Maybe we’ll have that self introspective moment and see our own relationship with our ecosystems reflect back on us. Or maybe we’ll ignore that and focus on reductive world views again.


  • It’s hilarious to me how unnecessarily complicated invoking moore’s law is to say anything…

    With Moore’s Law: “Ok ok ok, so like, imagine that this highly abstract, broad process over huge time period, is actually the same as manufacturing this very specific thing over a small time period. Hmm, it doesn’t fit. ok, let’s normalize the timelines with this number. Why? Uhhh because you know, this metric doubles as well. Ok. Now let’s just put these things together into our machine and LOOK it doesn’t match our empirical observations, obviously I’ve discovered something!”

    Without Moore’s Law: “When you reduce the dimensions of any system in nature, flattening their interactions, you find exponential processes everywhere. QED.”



  • Also meta but while I am big on slamming AI enshitification, I am still bullish on using machine learning tools to actually make products better. There are examples of this. Notice how artists react enthusiastically to the AI features of Procreate Dreams (workflow primarily built around human hand assisted by AI tools, ala what photoshop used to be) vs Midjourney (a slap in the face).

    The future will involve more AI products. It’s worthy to be skeptical. It’s also worthy to vote with your money to send the signal: there is an alternative to enshitification.


  • You can read their blog about the AI-crap, in terms of their approach and philosophy. In general, it is optional and not part of the major experience.

    The main reason I use kagi is immediately obvious from doing seaches. I convinced my wife to switch to it when she ask, “ok but what results does it show when I search sailor moon?” and she saw the first page (fan sites, official merch, fun shit she had forgotten about for years).

    What you need to know is that you pay money, and they have to give you results that you like. It’s a whole different world.




  • I use nix to manage all my personal infrastructure. I enjoy it and it has many benefits.

    But, I still have trouble recommending it openly or advocating its usage in any of my workplaces. There are so many gotchas that run against the grain, in practice. There are so many different patterns for using nix (like a big sore point is that nix flakes aren’t the default way to manage dependencies, instead it’s an experimental feature alternative to the default, which is fragmented tooling (pinned channels? fetchUrl? overlays? NIX_PATH? oh lord), (or even just the fact that minor version changes in nix completely deprecates certain core build utilities. See how nix docker images are still in major flux) that in practice a newbie who wants to go beyond playing with the simple compile a C project with make to… a nodejs development environment (shudder), is gonna have some struggles with unobvious decisions they make early on.

    I totally understand that they have greatly improved documentation, examples, tutorials, and community. These are all high quality. But the offense remains the fact that you really should read the whole manual before you get started, because the --defaults-- of solving the small problems with nix, and the deep baggage of historical packages and tooling, means that you can dig yourself into a corner that one day will require rethinking how you organized your work. That to me isn’t super great.

    But yes, I do love nix and am happy to see them continue to work through these issues.


  • Maybe unpopular take here, but I love discord as an excellent fit for specific use cases. I think plenty of groups that should be web forums use discord wrong, but for several of my favorite communities:

    1. They are better smaller, I don’t necessarily want or need them to be discoverable aside from word of mouth.
    2. They are better without search history, because the discussion is more ephemeral and personal instead of assuming that anyone is digging history in after hours
    3. Ad hoc voice chat rooms is a useful boon because of exactly 1 and 2.
    4. No ads. Yes I understand the privacy issues, but I would still prefer to have opt in subscriptions, no ads, and my chats are harvested than many alternatives for small communities that need to subsidize costs. (Again fediverse, if not ads, requires a buy in in terms of technical operational costs)
    5. Trivial to build specialized addons in the case your community has a need.

    Good examples for me are: Friend of Friend Groups for organizing dinners or parties Online gaming communities Book clubs Co-worker chat alternative to slack