541–550 of 1001 posts
I suppose this is a whole new argument in favor of good code commenting - to train/share context with your future tooling.
Is anyone here actually using this?
This could be the kind of thing easy to train and run locally on a GPU. - You only need code relevant to your language. - The amount of unique code in your language is going to be relatively small in compared to say, the entire history of internet comment. - Training a model also shouldn't take long. I personally would never use an online version feeding all my code back to a home server somewhere, and leaving breadc…
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If I put a section in my LICENSE.txt prohibiting use as training data in commercial models, would that be sufficient to keep my code out of models like this?
> If I put a section in my LICENSE.txt prohibiting use as training data in commercial models, would that be sufficient to keep my code out of models like this? Neither in practice (because it doesn't look for it) nor legally in the US, if Microsoft’s contention that such use is “fair use” under US copyright law. That “fair use” is an Americanism and not a general feature of copyright law might create some interesting…
> Why was GitHub Copilot trained on data from publicly available sources?
> Training machine learning models on publicly available data is now common practice across the machine learning community. The models gain insight and accuracy from the public collective intelligence. But this is a new space, and we are keen to engage in a discussion with developers on these topics and lead the industry in setting appropriate standards for training AI models.
Personally, I'd prefer this to be like any other software license. If you want to use my IP for training, you need a license. If I use MIT license or something that lets you use my code however you want, then have at it. If I don't, then you can't just use it because it's public.
Then you'd see a lot more open models. Like a GPL model whose code and weights must be shared because the bulk of the easily accessible training data says it has to be open, or something like that.
I realize, however, that I'm in the minority of the ML community feeling this way, and that it certainly is standard practice to just use data wherever you can get it.
I've been using the alpha for the past 2 weeks, and I'm blown away. Copilot guesses the exact code I want to write about one in ten times, and the rest of the time it suggests something rather good, or completely off. But when it guesses right, it feels like it's reading my mind. It's really like pair programming, even though I'm coding alone. I have a better understanding of my own code, and I tend to give better na…
Side note: I recently suffered from a tennis elbow due to sub optimal desktop setup when working from home. Copilot has drastically reduced my keystrokes, and therefore the strain on my tenders. It's good for our health, too!
I'm amazed to see how positive the overall response is to this idea. Almost as if programmers think that writing programs is the worst part of the job and ready to be automated away. As someone more aligned with the Dijkstra perspective, this seems to me like one of the single worst ideas I've ever seen in this domain. We already have IDEs and other tools leading to an increase in boilerplate and the acceptance of it…
I'm amazed to see how positive the overall response is to this idea. Almost as if programmers think that writing programs is the worst part of the job and ready to be automated away. As someone more aligned with the Dijkstra perspective, this seems to me like one of the single worst ideas I've ever seen in this domain. We already have IDEs and other tools leading to an increase in boilerplate and the acceptance of it…
I mean you say this, but you and most likely the majority of programmers rely on dozens of repositories, packages and libraries with likely zero deep understanding of it (and at the very least haven't read the source code of ) so I don't really understand the difference here. The advantage of something like this is that instead of having to go to stack overflow or any number of reference sites and copy pasta it can j…
of course I guess copilot won't be perfectly accurate right now or even maybe for a long time but it is interesting to imagine a future where the programmer can think and get code written without lifting a finger.
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Lots of questions: - the generated code by AI belongs to me or GitHub? - under what license the generated code falls under? - if generated code becomes the reason for infringment, who gets the blame or legal action? - how can anyone prove the code was actually generated by Copilot and not the project owner? - if a project member does not agree with the usage of Copilot, what should we do as a team? - can Copilot copy…
In general: (1) training ML systems on public data is fair use (2) the output belongs to the operator, just like with a compiler. On the training question specifically, you can find OpenAI's position, as submitted to the USPTO here: https://www.uspto.gov/sites/default/files/documents/OpenAI_R... We expect that IP and AI will be an interesting policy discussion around the world in the coming years, and we're eager to…
I for one wouldn't touch this with a 10000' pole until I know the answers to these (very reasonable) questions.