You don't need to worry about deluge of AI content if it can only come from a few crazy people with top of the line hardware spending days iterating on prompts. Especially if really good GPUs start requiring a license. At some point it's just faster to learn to write properly.
The cat is out of the bag. While the "AI alignment" jackasses were writing their Terminator fanfiction and wringing their hands about paperclips, they had already destroyed the world wide web as we know it.
These models you talk about are either not good or unbearably slow, the output of a model that today runs "as fast as you can type" on average hardware will never be reliably mistaken for the real thing. If you try to cheat with it it would be more likely to fail you than if you spend $20 on a human freelancer to write stuff
The only factor that breaks this today is cheap availability of chatgpt and such. They are reasonably high quality but unprofitable to run, they are subsidized to hook public up so that later MS can safely jack up prices (ideally after getting an exclusive AI license from the government).
That's not even taking into account local LoRAs and scripts that are possible instead of some company's untweakable crap. The open source around this is healthy, has pushed past DALL-E, and there's no real roadblock to Open Source LLMs except of course, the training cost. Even still, people are getting $200k+ models in their hands for free from various training runs and donated computing and LoRAing them and fine tuning them all to make them comparable to the closed off remote models.
Any "cryptographic" scheme with the generations of these will just catch the lazy. The lazy already include the confabulated sources in their papers, and don't try to normalize the Error Level Analysis in generated images (probably the quickest way to determine whether an image is generated), so I don't think it's actually a net benefit. It's a cat and mouse game, and will push the mice further into the walls.
You can't possibly say that generative images like this are "poor quality"
https://www.reddit.com/r/StableDiffusion/comments/131lpks/my...
The first of your examples was generated on a desktop computer with 2080 Ti and even then still glaring uncanny hands. We don't know how long it took but I think the reason for the hands is that it's too slow to generate a dozen of these in hopes that hands would come out right.
The other one I can see done on any laptop in a few minutes, but it's more primitive and just a monochrome sketch. I skip over obvious issues e.g. with shape of glasses.
For both examples you don't need any specialized tools or watermarking to notice this stuff.
Maybe you see what I mean why indie homegrown AI is not such a big deal ;) Sure there are people who will invest in hardware but those people will are not and for now won't be mainstream enough to matter. Especially if it will be licensed, most people don't like to violate laws. Most people will just use chatgpt or dall-e.
Commercial AI has all of those issues you mentioned, and more, and less. Midjourney is just a bunch of LoRAs layered on top and scripting to generate the images. But since they do that, midjourney images have a specific "feel" that it can't seem to get rid of. It's nothing really out of the reach for someone sufficiently motivated to reproduce.
DALL-E is laughable now, and it's only been a year. Certainly has been surpassed by open source, and outside competitors. I'm not sure what your motivation is to discount open source. People are already running LLM inference on their phones.
Your point?
Why do you need to watermark them, again? The error level analysis is off the charts with generative images. They light up like a Christmas tree. Just because you and uninformed legislators and journalists don't know how to check the ELA of an image, doesn't mean they're undetectable. And the cheaters include the bogus sources spit out by ChatGPT already. The cryptographic qualities will be lost as soon as an editor gets their hands on it, automated editing or not. It's a cat and mouse game.
And also, I find it telling that you think if someone doesn't have high end hardware, they're going to pay $20/mo to OpenAI. For $20/mo, you can buy a mid-range video card and write it off. For an extra $10/mo, you can deprecate the cost and buy a high end laptop for that price, if you're a professional, and you're not locked into OpenAI. You're also assuming that 1. hardware doesn't get better and 2. techniques don't improve to run them on limited hardware.
Again, either too slow, requiring outrageous hardware, or obviously noticeable. So far no examples to the contrary.
Don't forget, the topic is using special measures to detect undetectable with naked eye. When you can simply see the screwed up hands on a photo it's not even necessary.
> Why do you need to watermark them, again?
Why do you think I need to watermark them again?
> a mid-range video card
and a PC to put it in, a space to put the PC in, etc. With a laptop we're back in wait for an hour to see a result.
> hardware doesn't get better and 2. techniques don't improve to run them on limited hardware
We can revisit this if it consumer hardware gets good enough...
Any laptop within the last five years with decent memory can run stable diffusion on the cpu in around 12 minutes. My MacBook Pro runs a batch of four on Metal in around 30 seconds.
>We can revisit this if it consumer hardware gets good enough...
I mean, I just showed you a quantized llama running on a Pixel 5 and 6. And, I wouldn't discount most of the next generation of hardware having ML co processing like MacBooks and iPhones and Pixels do with all of this hype.
Majority of output is bad so you need to try dozens of takes to get a result that is reasonably realistic. Multipy 12 accordingly
> quantized llama
I don't know what that means but if it's better than chatgpt/gpt4 then sure.
Average consumer hardware is not showing any signs of being capable of this in near term. Hype is hype, but use your own head.
Don't forget that these homegrown models also require training data, scraped and cleaned, an individual or nonprofit can't do that.
Well, yeah. It's been around for years and chatgpt is new. My years old GPU doesn't run the latest games either, and chatgpt is novel technology so the hardware will of course lag behind a bit. But it will come.
https://www.mosaicml.com/blog/mpt-7b
There's dozens if not hundreds more individuals and nonprofits doing these things.
It may even be the case that all of that RLHF training that OpenAI does simply lessens the quality of generations, as suggested by one of their own papers and the paper above.
Quantized, LLaMa can run on an Pixel 6:
Who is going to pay $20/mo other than professionals? You'd assume professionals have professional hardware. A mid-range GPU or a video editing laptop is not exactly breaking the bank.
>no tool required to see that it is generated
But again, that also applies to commercial generative images. They're easily discernible, if you just look. Midjourney is stable diffusion with a bunch of LoRA stacked on top. And it can't shed the "midjourney look" because of that. That's not in dispute by anyone.
100%, although dall-e is getting better with hands specifically.
Anyway, Microsoft can keep throwing compute on it until a point where it becomes impossible to distinguish fakes by sight and a mechanism like suggested will make sense.
But with homegrown ones I don't see it happening soon. Only those who spend a lot of money on top of the line GPUs and keep desktop PCs may get to that point. Those GPUs will jump in price like they did at crypto mining peak or become impossible to buy if Microsoft gets the government to require "AI license" for them.
> Who is going to pay $20/mo other than professionals
Apple Music costs $10 and people easily spend ten times that on Patreon...