• 34 Posts
  • 489 Comments
Joined 2 years ago
cake
Cake day: April 27th, 2024

help-circle
  • So I just stumbled into Team Human, because a Youtuber I kind of like (Kraut) published a video for them. They seem to run a campaign targeted at creators, pushing to get videos released on the 10th or 17th. So they must be having a bit of a high point right now. (Idk if cash changed hands.)

    Their manifesto tries to amp up societal harms, but the existential risk is snuck in at the end (AI programmed pandemics, etc.) and they want to regulate AI chips like nukes. CAIS (Center for AI Safety) is listed as the sponsor. Seems a little “AI centrist” to me, as in aligned with the less insane billionaires, but still a typical AI safety astroturfing shop. Shame if any of your favorite creators fell for it.
















  • 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.





  • I bumped heads here before on that take, but I can empathize deeply with these people.

    I expect that interacting with most of them will be infuriating, since they probably accepted the “this is just how things are done from now on” narrative of AI and are some level of infatuated with the fancy demos.

    But this acceptance is also where the inescapable soul-crushing comes from. If you can adopt an internal posture of resistance, the prevalence of AI is still grating but you can adopt a perspective that doesn’t devalue your own skill and makes personal impact conceivable. Many of these folks are pretty far gone, but a pipeline of recovery should be possible from a starting point like this.