That’s a lot of “could” and “will” from an article a year old, primarily about concerns from two years ago, while image models to-day keep getting smaller and better. They didn’t find a second internet’s worth of JPEGs. Better training on the same data, or even better labels on less data, beats a simple obsession with scale.
Yes, photocopying a photocopy will degrade, but diffusion is a denoising algorithm. Un-degrading an image is its central function. ‘Make it look less AI’ is how you get generative adversarial networks.
Anyway, the grim truth is that the central concern is mistaken. Training data for cancer screening does not require the patient lived.
That’s a lot of “could” and “will” from an article a year old, primarily about concerns from two years ago, while image models to-day keep getting smaller and better. They didn’t find a second internet’s worth of JPEGs. Better training on the same data, or even better labels on less data, beats a simple obsession with scale.
Yes, photocopying a photocopy will degrade, but diffusion is a denoising algorithm. Un-degrading an image is its central function. ‘Make it look less AI’ is how you get generative adversarial networks.
Anyway, the grim truth is that the central concern is mistaken. Training data for cancer screening does not require the patient lived.
The article links a study. What’s your study that collapse isn’t a concern?
For what it’s worth, my worry was never focused on cancer, these doctors were just an example measured for the likely universal unlearning effect.