> Maybe the supporter of the definition could demonstrate practically modifying a ML model without using the original training data, and show that it is just as easy as with the original data and it does not limit what you can do with it (e.g. demonstrate it can unlearn any parts of the original data as if they were not used).<p>I quite like that comment that was left on the article. I know some models you can tweak the weights, without the source data, but it does seem like you are more restricted without the actual dataset.<p>Personally, the data seems to be part of the source to me, in this case. I mean, the code is derived from the data itself, the weights are the artifact of training. If anything, they should provide the data, the training methodology, the model architecture, the code to train and infer, and the weights could be optional. I mean, the weights basically are equivalent to a built artifact, like the compiled software.<p>And that means commercially, people would pay for the cost of training. I might not have the resources to "compile" it myself, aka, run the training, so maybe I pay a subscription to a service that did.