If similar code is open in your VS Code project, Copilot can draw context from those adjacent files. This can make it appear that the public model was trained on your private code, when in fact the context is drawn from local files. For example, this is how Copilot includes variable and method names relevant to your project in suggestions.
It’s also possible that your code – or very similar code – appears many times over in public repositories. While Copilot doesn’t suggest code from specific repositories, it does repeat patterns. The OpenAI codex model (from which Copilot is derived) works a lot like a translation tool. When you use Google to translate from English to Spanish, it’s not like the service has ever seen that particular sentence before. Instead, the translation service understands language patterns (i.e. syntax, semantics, common phrases). In the same way, Copilot translates from English to Python, Rust, JavaScript, etc. The model learns language patterns based on vast amounts of public data. Especially when a code fragment appears hundreds or thousands of times, the model can interpret it as a pattern. We’ve found this happens in <1% of suggestions. To ensure every suggestion is unique, Copilot offers a filter to block suggestions >150 characters that match public data. If you’re not already using the filter, I recommend turning it on by visiting the Copilot tab in user settings.
This is a new area of development, and we’re all learning. I’m personally spending a lot of time chatting with developers, copyright experts, and community stakeholders to understand the most responsible way to leverage LLMs. My biggest take-away: LLM maintainers (like GitHub) must transparently discuss the way models are built and implemented. There’s a lot of reverse-engineering happening in the community which leads to skepticism and the occasional misunderstanding. We’ll be working to improve on that front with more blog posts from our engineers and data scientists over the coming months.
Is it a valid defense against copyright infringement to say “we don’t know where we got it, maybe someone else copied it from you first?”
If someone violated the copyright of a song by sampling too much of it and released it in the public domain (or failed to claim it at all), and you take the entire sample from them, would that hold up in a legal setting? I doubt it.
I mean, in humans it's just referred to as 'experience', 'training', or 'creativity'. Unless your experience is job-only, all the code you write is based on some source you can't attribute combined with your own mental routine of "i've been given this problem and need to emit code to solve it". In fact, you might regularly violate copyright every time you write the same 3 lines of code that solve some common language workaround or problem. Maybe the solution is CoPilot accompanying each generation with a URL containing all of the run's weights and traces so that a court can unlock the URL upon court order to investigate copyright infringement.
> If someone violated the copyright of a song by sampling too much of it and released it in the public domain (or failed to claim it at all), and you take the entire sample from them, would that hold up in a legal setting? I doubt it.
In general you're not liable for this. While you still will likely have to go to court with the original copyright holder's work, all the damages you pay can be attributed to whoever defrauded or misrepresented ownership over that work. (I am not your lawyer)
The first C developers wrote C code despite lacking a training set of C code.
AI can't do that. It needs C code to write C code.
See the difference here?
AI could be used to create languages based on design criteria and constraints like C was, but it does bring up the question of why one of the constraints should be character encodings from human languages if the final generated language would never be used by humans...
I mainly think it's funny watching all of these Rand'ian objectivists reusing ever excuse used by every craftsman that was excised from working life...machines need a machinist, they don't have souls or creativity, etc.
Industry always saw open source as a way to cut cost. ML trained from open source has the capability to eliminate a giant sink of labor cost. They will use it to do so. Then they will use all of the arguments that people have parroted on this site for years to excuse it.
I'm a pessimist about the outcomes of this and other trends along with any potential responses to them.
the problem isn't even that this technology will eventually replace programmers: the problem is that it produces parts of the training set VERBATIM, sans copyright.
No, I am pretty optimistic that we will quickly come to a solution when we start using this to void all microsoft/github copyright.