So that should make it easy to attribute, yes?
But boilerplate functions don't deserve copyright protection as they are not creative. Can I copyright print('hello world!') if I post it in my repo? Do I deserve a citation from now on?
But there is a desire to understand why an AI provided the output it did (to increase trust in AI generated output), and so there's a lot of study and work going into adding that observability. Once that's in place, it becomes pretty straightforward to identify which inputs to a model provided what outputs.
It's a non-starter for no other reason than potential copyright infringement means the government becomes involved, and they will stomp on the AI mouse with the force of an elephant - the opinions of amateurs and the anti-copyright movement notwithstanding.
As such, AI Observability is a problem that's both under active research, and the basis for B2B companies.
https://censius.ai/wiki/ai-observability
https://towardsdatascience.com/what-is-ml-observability-29e8...
Given a black box you can do two things: watch the black box for a while to see what it does, or take it apart to see how it works.
Observability is the former. Useful in many cases, just not here.
If you want to know what LLMs are actually doing, you’ll need the latter. Looking at weight activations for example, although with billions of parameters that’s infeasible.
Have a great new years!
> In an attempt to address the issues with open-source licensing, GitHub plans to introduce a new Copilot feature that will “provide a reference for suggestions that resemble public code on GitHub so that you can make a more informed decision about whether and how to use that code,” including “providing attribution where appropriate.” GitHub also has a configurable filter to block suggestions matching public code.