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> The output of a machine simply does not qualify for copyright protection

Good, does the `cp` or `cat` command qualify for the "output of a machine"? Now I can uncopyright everything, hooray. What about converting a video or an image to another format? Again, it's just output of a machine.

Added:

Really, I would've been happy if this was the situation, as I'm, in general, against patents and copyright (in the form that they are now being used).

Wow, that's quite a strawman/bait-and-switch from the article; thanks for highlighting it.

If Copilot is just a machine -- a glorified typewriter -- then the machine's operator is responsible for its output.

Or does the author seriously want to claim that any code added via Copilot to a proprietary codebase would not be proprietary as well? If that were true, Copilot's userbase is going to be...limited.

So then we come full circle on infringement, if the operator is responsible for the code produced by co-pilot then the articles claim that is not infringement because it is machine created fails, as the operator is responsible

The argument in the article is that all code made by co-pilot is not infringement because there is no copyright attached. You seem to imply that the copyright of all code made by co-pilot is copyrighted by the operator of co-pilot thus would then fall under copyright law, and thus would/could be infringing

> The argument in the article is that all code made by co-pilot is not infringement because there is no copyright attached.

Right; I don't see how that's even remotely a tenable argument. I think the article is trying to eat its cake and have it too.

> This line of thought implies that we can legally feed all proprietary code into GitHub Copilot

And lines such as:

    char *base64_pirated_mp4_file = "YW55IGNhcm5hbCBwbGVhc3VyZS4...";

> Copyright law has only ever applied to intellectual creations – where there is no creator, there is no work

This is, at best, an oversimplification. Code compiled from copyrighted source code is derived work inheriting that copyright according to long-established law. This is exactly why the legal issues around machine learning applied to copyrighted corpora have been contentious.

"Patented and secret tricks" are not protected by copyright, if the output was an actual reimplementation of an idea instead of Copilot regurgitating existing code (https://news.ycombinator.com/item?id=27710287).

The specific implementations are protected by copyright, and the ideas may be protected by patents. In the case of "secret" tricks, they may be protected by trade secret laws, but not if it's in a public GitHub repo.

Except Copilot itself is not open source, so your only way to feed that proprietary code into it would be to upload it to github, which would make you an infringer.

> your only way to feed that proprietary code into it would be to upload it to github, which would make you an infringer

Somebody has to do it. Someone ready to take one for the team (or better yet, already skilled in software piracy, so that it's not a big deal for them). Then, if this argument holds, everyone gets the result in public domain.

As I see it, the argument presented in this post essentially makes Copilot to be an universal copyright laundering machine. Not just for code, but for anything that can be represented digitally.

Obviously this won't stand. While I can see Github ending up protected from all liability, the only way for this to not kill copyright is for the users of Copilot to become at risk of copyright infringement. Which kills the whole value proposition of Copilot.

This seems like a good time to ask what the heck is the value proposition of a thing like this. Are people really going to use the output of this blindly? And if they're going to audit every line - is that really easier than just writing the code yourself? Honestly, at best it feels like a machine for introducing pernicious bugs that "look right" but are semantically wrong. (Which reminds me - were any of the Underhanded C Contest entries in the training data?)

That's a very good question. On the surface, the idea seems to be helping people write code faster - but as you observe, properly auditing generated code is more work than actually writing it from scratch.

Best I can think of in terms of real value delivered, is helping people with first drafts, breaking through the "staring at an empty page" problem. But even with this, I feel it's too risky compared to doing a StackOverflow search, where you can at least see some explanations, discussions, and other relevant context.

It's definitely an interesting vision demonstrator - despite not being quite there, it lets us see that a tool like this that actually worked well (in terms of generating correct, explainable, license-respecting code) would be very useful.

Assume GitHub designs a filter to detect similarities to the training set and displays an attribution link with the result, as a comment. It's no different from using a search engine to find the code and putting it in your project, especially that the code is public already and visible to multiple search engines. You are ultimately responsible, just like you are every day with Google.

But the model has on average just 1 regurgitation in 10 weeks per user, so you can just discard all of them.

Almost all the output of Copilot is not an exact copy of any code in the training set. You discard the 0.001% of generations that are similar to the training data and use the rest.

Can they afford it, though? The most important feature of DNN models is that they're orders of magnitude smaller and cheaper to run than training data; checking each output against the training set will make the model orders of magnitude more expensive to run.

As you type you send the code you're writing to Copilot's backend.

From the copilot telemetry docs [0]:

> The GitHub Copilot collects activity from the user’s Visual Studio Code editor, tied to a timestamp, and metadata.

[...]

> This data will only be used by GitHub for:

[...]

> - Improving the underlying code generation models, e.g. by providing positive and negative examples (but always so that your private code is not used as input to suggest code for other users of GitHub Copilot)

I'm inclined to believe this. After all, why would they taint the training data with code from a random guy who is asking for help when they have more than a hundred thousand repos with 100+ stars?

[0] [https://docs.github.com/en/github/copilot/about-github-copil...]

Sure, perhaps they won't use it for training, but the fear is they would use it for corporate espionage, market research, etc. Compared to training, this would be both more useful and much easier to keep deniable / under wraps.

I've had previous employers that were highly concerned about far more innocuous data leaking to competitors, e.g. autocomplete search terms. This means that a Copilot installation at any company that competes (or might compete) with MS should be considered a security breach, IMO. Given Microsoft's presence in so many markets, in general I think it would be foolish to risk this at any company.

You're still sending your intellectual property to Microsoft and hoping that they do only what they say they do with it, and that whatever they do with it will never change.

From context, I thought you were suggesting the strategy "just type pirated proprietary code into the IDE and the Copilot plugin will automatically include it in the training data", since my earlier comment was about the difficulty of training Copilot on such code. I don't believe they won't abuse your work in other ways either.

You're assuming Copilot is the only AI-powered autocomplete?

No, but parent comment did say, "feed all proprietary code into GitHub Copilot".

Patent is not copyright.

Secret tricks are usually a company secret, not really protected by copyright either.

NDAs and similar are also not copyrights.

Even trademark is not copyright.