| > Your argument would have some merit if something were created instead of assembled, but there is no new algorithm that is being created. That is not what is happening here. If you're going to set such a high standard for ML tools like this, I think you need to justify why it shouldn't apply to humans too. When a human programmer who has read copyrighted code at some point in their life writes new code that is not a "new algorithm", are they in violation of the copyrights of every piece of code they've ever read that was remotely similar in any respect to the new work? I mean, I hope not! > On the one hand, you call this copying in fair use. On the other hand, you say this is creating new code. You can't have it both ways. I'm not a lawyer, but this actually sounds very close to the "transformative" criterion under fair use. Elements of existing code in the training set are being synthesized into new code for a new application. I assume there's no off-the-shelf precedent for this, but given the similarity with how human programmers learn and apply knowledge, it doesn't seem crazy to think this might be ruled as legitimate fair use. I'd guess it would come down to how willing the ML system is to suggest snippets that are both verbatim and highly non-generic. |
On the same page is an image showing copilot in real-time adding the text of the famous python poem, The Zen of Python. See https://docs.github.com/assets/images/help/copilot/resources... for a link directly to copilot doing this.
You are making arguments about what you read instead of objectively observing how copilot operates. Just because GH wrote that copilot synthesizes new code doesn't mean that it writes new code in the way that a human writes code. That is not what is happening here. It is replicating code. Even in the best case copilot is creating derivative works from code where GH is not the copyright owner.