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GitHub Copilot

copilot.github.com

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Re: GitHub Copilot

#801

Post by kopos »

FreeSpeech wrote: post #680
feross wrote: post #306

Earlier quoted context omitted.

I’ve also been using the Alpha for around two weeks. I'm impressed by how GitHub Copilot seems to know exactly what I want to type next. Sometimes it even suggests code I was about to look up, such as a snippet to pick a random hex color or completing an array with all the common image mime-types. Copilot is particularly helpful when working on React components where it makes eerily accurate predictions. I see techno…

Has anyone used Copilot with a more succinct language? It appears to only automate boilerplate and rudimentary patterns, which while useful in repetitive low signal to noise ratio languages like React or Java, sounds less appealing if you're writing Clojure.

Or the converse?

If Copilot is as good as it gets but only for some languages, won’t it influence what languages will be chosen by devs or companies?

Re: GitHub Copilot

#803

Post by celeritascelery »

6gvONxR4sf7o wrote: post #244

Plenty of discussion about the IP issues. It makes me want to start adding a section in my LICENSE.txt that says it's not eligible for use training commercial models. We'll likely end up with a whole set of license choices for that. Although if a license can permit or prohibit use in training commercial models, does that mean that the lack of permission implies a prohibition on it?

My guess is that there is something in the GitHub terms of service that says you consent to this by using them to host your code.

Re: GitHub Copilot

#805

Post by larkinrichards »

toxik wrote: post #681
mitjak wrote: post #639

Earlier quoted context omitted.

the main argument against Copilot for me. it takes longer to grok existing code than just write it from ground up.

This is actually a pretty important thing to understand. It can be better to rewrite than fix an existing mess. It’s similar to construction work in that sense: if you rebuild, you know what’s inside the walls.

If your house doesn’t have fire proofing (Sheetrock / lathe & plaster) then you know what’s inside the walls.

Re: GitHub Copilot

#806

Post by cphoover »

Someone on the reddit thread asked about adversarial ML approaches to introduce either poor performing or vulnerable code into the suggestion. Could be a good vector for exposing tons of applications to attack from malicious actors.

Re: GitHub Copilot

#807

Post by devetec »

leotaku wrote: post #735

> If the technical preview is successful, our plan is to build a commercial version of GitHub Copilot in the future. This may be the first time that a proprietary coding tool offers such a great value preposition that I am actually interested in trying it out and potentially even paying for it. It's also a bit concerning that this will probably be extremely hard, if not impossible, to create an FOSS version of this t…

There is a FOSS version of GPT-3 being worked on by EleutherAI, but they are only at 6 billion parameters so far, while the largest model of GPT-3 is 175 billion (more parameters is better (usually)). EleutherAI is getting more compute from CoreWeave to actually train the GPT-3-like FOSS model, so that's something to look forward to eventually :). Github Copilot uses Codex though, which seems to be a GPT model trained on just code by OpenAI. It wouldn't be too hard to train a FOSS version of Codex on the open-source code of Github and other sources.

Re: GitHub Copilot

#808

Post by devetec »

0-_-0 wrote: post #752
leotaku wrote: post #735

> If the technical preview is successful, our plan is to build a commercial version of GitHub Copilot in the future. This may be the first time that a proprietary coding tool offers such a great value preposition that I am actually interested in trying it out and potentially even paying for it. It's also a bit concerning that this will probably be extremely hard, if not impossible, to create an FOSS version of this t…

Well, it should be possible to crowdsource training a FOSS version, right? There should be a SETI-at-home for training neural networks. I would donate some GPU power for sure. SETI@home achieved 50 times the computing power of the world's largest supercomputer [0], so it might actually be the only way to train the future GPT4 or GPT5. [0]: https://en.wikipedia.org/wiki/SETI@home#Statistics

Distributed GPT training doesn't really work (sadly). Maybe someone will get a good solution to that, though.

Re: GitHub Copilot

#809

Post by devetec »

iwintermute wrote: post #21

So if it was trained using "source code from publicly available sources, including code in public repositories on GitHub." was it also GPLv2? So everything generated also GPLv2?

You can train models on copyrighted materials. https://towardsdatascience.com/the-most-important-supreme-co...

Re: GitHub Copilot

#810

Post by devetec »

qchris wrote: post #110
iwintermute wrote: post #21

So if it was trained using "source code from publicly available sources, including code in public repositories on GitHub." was it also GPLv2? So everything generated also GPLv2?

This is a bit tricky, because at least in the U.S., I don't believe it's settled question in law yet. Some of the other posters on here have said that the resulting model isn't covered by GPL--that's partially true, but provenance of data, and the rights to it, definitely does matter. A good example of this was the Everalbum ruling, where the company was forced to delete both the data and the trained models used they…

It has been cleared up! https://towardsdatascience.com/the-most-important-supreme-co...
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