If the AI generated content is labeled, or has context, or has comments or descriptions created by people, then wouldn’t it just be the same as synthetic training data? Which is shown to still be very useful for training.
Most AI-generated data in the wild won’t have labels because there’s no incentive to label it, and in a lot of cases there are incentives to not label it.
Yes it’s still useful and it’s basically how we made our last couple of jumps. An AI training on AI generated data being graded by another AI. We’ve hit diminishing returns though.
Sorta. This “model collapse” thing is basically an urban legend at this point.
The kernel of truth is this: A model learns stuff. When you use that model to generate training data, it will not output all it has learned. The second generation model will not know as much as the first. If you repeat this process a couple times, you are left with nothing. It’s hard to see how this could become a problem in the real world.
Incest is a good analogy, if you know what the problem with inbreeding is: You lose genetic diversity. Still, breeders use this to get to desired traits and so does nature (genetic bottleneck, founder effect).
If the AI generated content is labeled, or has context, or has comments or descriptions created by people, then wouldn’t it just be the same as synthetic training data? Which is shown to still be very useful for training.
Most AI-generated data in the wild won’t have labels because there’s no incentive to label it, and in a lot of cases there are incentives to not label it.
Yes it’s still useful and it’s basically how we made our last couple of jumps. An AI training on AI generated data being graded by another AI. We’ve hit diminishing returns though.
Exactly what percentage of AI data in the wild is labeled?
Close to zero I’d say.
Sorta. This “model collapse” thing is basically an urban legend at this point.
The kernel of truth is this: A model learns stuff. When you use that model to generate training data, it will not output all it has learned. The second generation model will not know as much as the first. If you repeat this process a couple times, you are left with nothing. It’s hard to see how this could become a problem in the real world.
Incest is a good analogy, if you know what the problem with inbreeding is: You lose genetic diversity. Still, breeders use this to get to desired traits and so does nature (genetic bottleneck, founder effect).