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> Some might argue that a "pure" open-source would require the dataset and the training "recipe" as it would be needed to reproduce the training, but it would b
by andy99 3y ago
> Some might argue that a "pure" open-source would require the dataset and the training "recipe" as it would be needed to reproduce the training, but it would be so expensive that most people wouldn't be able to do much with it.
I've actually argued the opposite. https://www.marble.onl/posts/considerations_for_copyrighting_AI.html https://www.marble.onl/posts/considerations_for_copyrighting... When you look at the freedoms underlying open source, you can exercise them without the training data.
Most of what disqualifies the licenses I mentioned above from being classically open source is use restrictions.
- KRAKRISMOTT 3y agoYes, I think most of the misunderstanding comes from people who are not in ML. A lot of the times you can't release the training pipeline without getting into grey areas of training data copyright. A ML model is a high dimensional distribution that can be sampled from infinitely, given enough time and money you can train another model (possibly even with a different architecture) that closely models the original model in generation capability.