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Gitlab’s AI-assisted code suggestions

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Re: Gitlab’s AI-assisted code suggestions

#21

Post by GordonS »

colordrops wrote: post #11
RadiozRadioz wrote: post #3

Crucial: do they train on GPL code? If I am to use this tool, I must abide by the license terms of the training data. Even if it is found that the GPL does not cover LLM responses as derivative works, I would prefer to be on the safe side and refuse to use models trained on software with non-permissive licenses unless I am building Free Software.

Unless the LLM is cutting and pasting GPL code, I don't see how this differs from an engineer learning to code by referencing open source projects then creating their own project with its own licensing. I'd imagine these models could be tuned or at least a check could be added that no licensed code is being returned.

Huh, I hadn't thought of it that way, interesting.

Re: Gitlab’s AI-assisted code suggestions

#22

Post by blibble »

Chiron1991 wrote: post #18
ases wrote: post #4

All the code examples on this page are doubling any brackets ([], {}, ()). How have they managed that? Not a fantastic first impression of its capabilities...

Not only that, but the Golang example is full of errors. Parameter definitions don't have colons between name and type. The map for seen elements is declared as a, but later referenced to as m. The append instruction references uniques, which is undefined. The return statement also references uniques.

ah yes, so a typical example of generative AI output

Re: Gitlab’s AI-assisted code suggestions

#23

Post by michaelmior »

cassianoleal wrote: post #8
dgrin91 wrote: post #2

I wish them luck. While I'm not a huge fan of copilot coding AIs, I understand how they are going to be a killer feature for many and I worry that it would create a big moat for Github. I hope that Gitlab is able to close that gap because I really like Gitlab.

Codeium [0] seems like a worthy competitor to Copilot already. [0] https://codeium.com/

I've been using Codeium with Vim for a few months and it works great :)

Re: Gitlab’s AI-assisted code suggestions

#24

Post by wizzwizz4 »

colordrops wrote: post #11
RadiozRadioz wrote: post #3

Crucial: do they train on GPL code? If I am to use this tool, I must abide by the license terms of the training data. Even if it is found that the GPL does not cover LLM responses as derivative works, I would prefer to be on the safe side and refuse to use models trained on software with non-permissive licenses unless I am building Free Software.

Unless the LLM is cutting and pasting GPL code, I don't see how this differs from an engineer learning to code by referencing open source projects then creating their own project with its own licensing. I'd imagine these models could be tuned or at least a check could be added that no licensed code is being returned.

If someone learns to program by memorising and then re-using blocks of code, they're absolutely doing it wrong – but, also, they should be able to attribute their code.

Most of the techniques I used, I've invented myself (or learnt from the standard documentation). When I use a technique that I haven't invented myself, I look up where it came from. Half the time, my version is actually radically different (and my attribution is mistaken); the other half, I've remembered an inferior version, so I steal the better version and then attribute it appropriately.

That's one way it's different. There are others. Really, though, we should be asking the question “in what way is this the same as humans learning?”, expecting answers that would convince an education specialist.

Re: Gitlab’s AI-assisted code suggestions

#25

Post by jraph »

RadiozRadioz wrote: post #3

Crucial: do they train on GPL code? If I am to use this tool, I must abide by the license terms of the training data. Even if it is found that the GPL does not cover LLM responses as derivative works, I would prefer to be on the safe side and refuse to use models trained on software with non-permissive licenses unless I am building Free Software.

Even with permissive license, you'd need to respect attribution.

Re: Gitlab’s AI-assisted code suggestions

#26

Post by hospitalJail »

RadiozRadioz wrote: post #3

Crucial: do they train on GPL code? If I am to use this tool, I must abide by the license terms of the training data. Even if it is found that the GPL does not cover LLM responses as derivative works, I would prefer to be on the safe side and refuse to use models trained on software with non-permissive licenses unless I am building Free Software.

I'm so torn on GPL...

It prevents bad actors like Apple from ripping off people's philanthropic labor, but it also prevents me from ripping off people's labor. It also focuses effort onto the FOSS project.

I like PyQts solution of having GPL or buy a commercial license.

I suppose I still like MIT/Apache style the best. Even if someone rips them off, we didn't lose progress.

Re: Gitlab’s AI-assisted code suggestions

#27

Post by andrewl-hn »

RadiozRadioz wrote: post #3

Crucial: do they train on GPL code? If I am to use this tool, I must abide by the license terms of the training data. Even if it is found that the GPL does not cover LLM responses as derivative works, I would prefer to be on the safe side and refuse to use models trained on software with non-permissive licenses unless I am building Free Software.

Most non-GPL licenses require attribution. So, there are two real choices: either we consider that AÍ generated code doesn’t infringe copyright and we are free to use any code: proprietary, GPL, AGPL, or we have to attribute the generated code to all sources, and thus in practical terms your program has to carry a few thousand copies of MIT, BSD, Apache, etc licenses with different copyright headers.

Re: Gitlab’s AI-assisted code suggestions

#28

Post by meowface »

colordrops wrote: post #11
RadiozRadioz wrote: post #3

Crucial: do they train on GPL code? If I am to use this tool, I must abide by the license terms of the training data. Even if it is found that the GPL does not cover LLM responses as derivative works, I would prefer to be on the safe side and refuse to use models trained on software with non-permissive licenses unless I am building Free Software.

Unless the LLM is cutting and pasting GPL code, I don't see how this differs from an engineer learning to code by referencing open source projects then creating their own project with its own licensing. I'd imagine these models could be tuned or at least a check could be added that no licensed code is being returned.

The same argument could be used to defend image generation models. (And, personally, I'm favorable to that argument for both.)

Re: Gitlab’s AI-assisted code suggestions

#29

Post by jedbrown »

RadiozRadioz wrote: post #3

Crucial: do they train on GPL code? If I am to use this tool, I must abide by the license terms of the training data. Even if it is found that the GPL does not cover LLM responses as derivative works, I would prefer to be on the safe side and refuse to use models trained on software with non-permissive licenses unless I am building Free Software.

Even MIT licensed code requires you to preserve the copyright and permission notice.

If a human did what these language models are doing (output derivative works with the copyright and license stripped), it would be a license violation. When humans want to create a new implementation with clean IP, they have one team study the IP-encumbered code and write a spec, then a different team writes a new implementation according to the spec. LM developers could have similar practices, with separately-trained components that create an auditable intermediate representation and independently create new code based on that representation. The tech isn't up to that task and the LM authors think they're going to get away with laundering what would be plagiarism if a human did it.

Re: Gitlab’s AI-assisted code suggestions

#30

Post by mattl »

hospitalJail wrote: post #26
RadiozRadioz wrote: post #3

Crucial: do they train on GPL code? If I am to use this tool, I must abide by the license terms of the training data. Even if it is found that the GPL does not cover LLM responses as derivative works, I would prefer to be on the safe side and refuse to use models trained on software with non-permissive licenses unless I am building Free Software.

I'm so torn on GPL... It prevents bad actors like Apple from ripping off people's philanthropic labor, but it also prevents me from ripping off people's labor. It also focuses effort onto the FOSS project. I like PyQts solution of having GPL or buy a commercial license. I suppose I still like MIT/Apache style the best. Even if someone rips them off, we didn't lose progress.

Why do you want to rip off people's labor? What's stopping you from incorporating it into your own work?
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