Title, or at least the inverse be encouraged. This has been talked about before, but with how bad things are getting, and how realistic goods ai generated videos are getting, anything feels better than nothing, AI generated watermarks, or metadata can be removed, but thats not the point, the point is deterrence. Immediately all big tech will comply (atleast on the surface for consumer-facing products), and then we will probably see a massive decrease in malicious use of it, people will bypass it, remove watermarks, fix metadata, but the situation should be quite a bit better? I dont see many downsides/

    • queermunist she/her@lemmy.ml
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      7 days ago

      If they can find cannabis grow ops from power usage, they certainly can find people using massive amounts of data and processing power and public water and investor cash to train AI. You expect me to believe this could be done in secret?

      • FourWaveforms@lemm.ee
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        5 days ago

        Yes.

        Using AI models and playing 3D games both use the same hardware and electricity. You can run an LLM or a diffusion model on a home computer, and many do. Is someone going to show up with a clipboard, and demand to see your Steam account?

        • queermunist she/her@lemmy.ml
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          5 days ago

          Training AI models is completely different, though. That requires massive amounts of compute and data and electricity and water, and that’s all very easy for the government to track.

          • FourWaveforms@lemm.ee
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            4 days ago

            If someone trains an open source AI model to fingerprint its output, someone else can use abliteration or other methods to defeat that. It will not require re-training. An example of this is deepseek-r1’s “1776” variant, where someone uncensored it, and now it will talk freely about Tiananmen Square.

            Even without that, it’s not practical for a government to find all instances of model training. Thousands of people can rent the same GPUs in the same data centers. A small organization training one model can have the same power consumption as a large organization running inference. It would take advanced surveillance to get around that.

            It’s also becoming possible to train larger and larger models without needing a data center at all. nVidia is coming out with a 128GB desktop machine that delivers 1 petaflop @ FP4 for 170 watts. FP8 would be on the order of hundreds of teraflops. Ten of them could talk over an InfiniBand switch. You could run that setup in an apartment, or in a LAN closet.