Makes me wonder how many memes are “tainted” with oldschool ML before generative AI was common vernacular, like edge enhancement, translation and such.
A lot? What’s the threshold before it’s considered bad?
What about ‘edge enhancing’ NNs like NNEDI3? Or GANs that absolutely ‘paint in’ inferred details from their training? How big is the model before it becomes ‘generative?’
What about a deinterlacer network that’s been trained on other interlaced footage?
My point is there is an infinitely fine gradient through time between good old MS paint/bilinear upscaling and ChatGPT (or locally runnable txt2img diffusion models). Even now, there’s an array of modern ML-based ‘editors’ that are questionably generative most probably don’t know are working in the background.
Not a great metric either, as models with simpler output (like text embedding models, which output a single number representing ‘similarity’, or machine vision models to recognize objects) are extensively trained.
Welcome to Lemmy (and Reddit).
Makes me wonder how many memes are “tainted” with oldschool ML before generative AI was common vernacular, like edge enhancement, translation and such.
A lot? What’s the threshold before it’s considered bad?
Well those things aren’t generative AI so there isn’t much of an issue with them
What about ‘edge enhancing’ NNs like NNEDI3? Or GANs that absolutely ‘paint in’ inferred details from their training? How big is the model before it becomes ‘generative?’
What about a deinterlacer network that’s been trained on other interlaced footage?
My point is there is an infinitely fine gradient through time between good old MS paint/bilinear upscaling and ChatGPT (or locally runnable txt2img diffusion models). Even now, there’s an array of modern ML-based ‘editors’ that are questionably generative most probably don’t know are working in the background.
Id say if there is training beforehand, then its “generative AI”
Not a great metric either, as models with simpler output (like text embedding models, which output a single number representing ‘similarity’, or machine vision models to recognize objects) are extensively trained.
Another example is NNEDI3, very primitive edge enhancement. Or Languagetool’s tiny ‘word confusion’ model: https://forum.languagetool.org/t/neural-network-rules/2225