But will it be capable of generating a copyright infringement? Let's imagine I want to implement an algorithm to find the shortest path between nodes in a graph. Well I remember from my MSc that you probably need to implement it with Dijkstra's algorithm, and I've implemented this a few times over the years in kata and leetcode. Now let's say I'm programming Rust, which I don't really know the syntax for so I go read some GPL code while browsing the net for syntax.
Do I need to state that my implementation of Dijkstra's algorithm is 10% GPL, because I spent a few minutes reading syntax of a GPL file? What if someone else's copyrighted code looks 90% similar to mine, because hell there's only so many ways to implement it, can I get sued because there is 90% overlap and I might have looked at this code?
These systems aren't even capable yet of generating more than a a few functions, much less a coherent library. Even then, as they generalize more and gain more expressive power they will be less likely to copy in the training data to the output now, a possibility that I consider quite remote even given a relatively primitive Co-Pilot.
When you write your implementation of Dijkstra's algorithm then you hopefully using human intelligence to write it, not an illusion of intelligence. If its original then its 0% of whatever GPL file you happen to have read at a previous date. If you copy something verbatim without using human intelligence, then its 100% GPL.
True artificial intelligence == magic. if not magic then math.
At least according to Google’s monetization policy when it comes to music.
In particular an algorithmic concept cannot be copyrighted but a description of an algorithm in code can be. I think that many programmers believe that the algorithm is the copyrightable bit because that is the “hard part” compared to say the variable names and comments which are the easy part. But copyright cares much more about the latter than loops and conditionals, which have much less creative value. Mathematical expressions cannot be copyrighted.
But of course this wouldn't catch copies where variable names have been changed, etc. One thing that I think would be really interesting (but hideously computationally expensive) is to compute an embedding of each chunk of the training data and then query for the k nearest neighbors when Copilot generates some code, so you can see what the closest snippets in the training data are and evaluate for yourself if they're too similar.