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It’s the same problem with those ML models, the other day someone generated a children’s book using GPT3, turned out that there is a real children's book with

It’s the same problem with those ML models, the other day someone generated a children’s book using GPT3, turned out that there is a real children's book with the same name and a very similar content: The Very Lonely Firefly by Eric Carle.

bartq 4y ago
Other thing I'm worried about: how to retract facts from ML model? I guess it's impossible, you need to retrain from scratch with part X removed from training set. Or... people could invent layered ML models similar to docker - each layer would be marked what data it was trained with. Then at least you'd have some cache of trained model to re-use in next training session. Nasty stuff.
- alpaca128 4y ago
  Or instead of inventing complicated layered ML models Github could just use each repo's license information to decide what's okay to use. Detecting licenses is already a feature on that site.
  - afiori 4y ago
    Many licenses requite attribution, which would be hard to track.
icoder 4y ago
Interesting, it's a big question I've had for a while, how 'original' stuff coming from these AI systems is, and also the distribution of uniqueness over many answers. I haven't dived into it yet, but I find it surprising how little this comes up when these systems are discussed (ie here on HN). Does anyone even know? Can we even check? What if 1 in a thousand, or one in a million outputs is (very close to) something existing? I find this especially relevant when generating faces.