Hundreds of developers are using it every day internally, and the most common reaction has been the head exploding emoji. If the technical preview goes well, we'll plan to scale this up as a paid product at some point in the future.
- 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 code from other projects and use that excerpt code?
- if yes, *WHY* ?!
- who is going to deal with legalese for something he or she was not responsible in the first place?
- what about conflicts of interest?
- can GitHub guarantee that Copilot won't use proprietary code excerpts in FOSS-ed projects that could lead to new "Google vs Oracle" API cases?> You can use the code anywhere, but you do so at your own risk.
Something more explicit than this would be nice. Is there a specific license?
EDIT: also, there’s multiple sections to a FAQ, notice the drop down... under “Do I need to credit GitHub Copilot for helping me write code?”, the answer is also no.
Until a specific license (or explicit lack there-of) is provided, I can’t use this except to mess around.
Edit: you have to click the things on the left, I didn't realize they were tabs.
Read it all, and the questions still stand. Could you, or any on your team, point me on where the questions are answered?
In particular, the FAQ doesn't assure that the "training set from publicly available data" doesn't contain license or patent violations, nor if that code is considered tainted for a particular use.
> GitHub Copilot is a code synthesizer, not a search engine: the vast majority of the code that it suggests is uniquely generated and has never been seen before. We found that about 0.1% of the time, the suggestion may contain some snippets that are verbatim from the training set.
I'm guessing this covers it. I'm not sure if someone posting their code online, but explicitly saying you're not allowed to look at it, getting ingested into this system with billions of other inputs could somehow make you liable in court for some kind of infringement.
Does everyone in this thread contact their lawyers after cutting and pasting a mergesort example from Stackoverflow that they've modified to fit their needs? Seems folks are reaching a bit.
You are expanding the discussion, which is great, but that doesn't apply in answer to that specific question.
There are answers in response to your question, however. For example, many companies use software for scanning and composition analysis that determines the provenance and licensing requirements of software. Then, remediation steps are taken.
> How is it possible to determine if you've violated a random patent from somewhere on the internet via a small snippet of customized auto-generated code?
The only real answer is a patent search.
My answer described literally what many companies do today. It was not a theoretical pie in the sky answer or a discussion about patent IP.
To restate, the real-world answer I gave for, "How is it possible to determine if you've violated a random patent from somewhere on the internet via a small snippet of customized auto-generated code?" is often "Do not take code from the Internet."
0.1% is a lot when you use 100 suggestions a day.
Is it even copyrighted? Generally my understand is that to be copyrightable it has to be the output of a human creative process, this doesn't seem to qualify (I am not a lawyer).
See also, monkeys can't hold copyright: https://en.wikipedia.org/wiki/Monkey_selfie_copyright_disput...
Isn't it subject to the licenses the model was created from, as the learning is basically just an automated transformation of the code, which would be still the original license - as else I could just run some minifier, or some other, more elaborate, code transformation, on some FOSS project, for example the Linux kernel, and relicense it under whatever?
Does not sound right to me, but IANAL and I also did not really look at how this specific model/s is/are generated.
If I did some AI on existing code I'd be quite cautious and group by compatible licences classes, asking the user what their projects licence is and then only use the compatible parts of the models.-Anything else seems not really ethical and rather uncharted territory in law to me, which may not mean much as IANAL and just some random voice on the internet, but FWIW at least I tried to understand quite a few FOSS licences to decide what I can use in projects and what not.
Anybody knows of some relevant cases of AI and their input data the model was from, ideally in jurisdictions being the US or any European Country ones?
No, I don't believe it is, at least to the extent that the model isn't just copy and pasting code directly.
Creating the model implicates copyright law, that's creating a derivative work. It's probably fair use (transformative, not competing in the market place, etc), but whether or not it is fair use is github's problem and liability, and only if they didn't have a valid license (which they should have for any open source inputs, since they're not distributing the model).
I think the output of the model is just straight up not copyrighted though. A license is a grant of rights, you don't need to be granted rights to use code that is not copyrighted. Remember you don't sue for a license violation (that's not illegal), you sue for copyright infringement. You can't violate a copyright that doesn't exist in the first place.
Sometimes a "license" is interpreted as a contract rather than a license, in which you agreed to terms and conditions to use the code. But that didn't happen here, you didn't agree to terms and conditions, you weren't even told them, there was no meeting of minds, so that can't be held against you. The "worst case" here (which I doubt is the case - since I doubt this AI implicates any contract-like licenses), is that github violated a contract they agreed to, but I don't think that implicates you, you aren't a party to the contract, there was no meeting of minds, you have a code snippet free of copyright received from github...
Wouldn't GitHub potentially be responsible for the infringement by distributing the copyrighted material knowing that it would be published?
The functional parts of code are not copyrightable, only the non functional creative elements.
(Not a lawyer...)
1. Depends heavily on the jurisdiction (e.g., Software patents are a thing in America but not really in basically all European ones)
2. A change to a copyrightable work, creative or not, would still mean that you created a derived work where you'd hold some additional rights, depending on the original license, but not that it would now be only in your creative possession. E.g., check §5 of https://www.gnu.org/licenses/gpl-3.0.en.html
3. What do you think of when saying "functional parts"? Some basic code structure like an `if () {} else {}` -> sure, but anything algorithmic like can be seen as copyrightable, and whatever (creative or not) transformation you apply, in its basics it is a derived work, that's just a fact and the definition of derived work.
Now, would that matter in courts? That depends not only on 1., but additionally to that also very much on the specific case, and for most trivial like it probably would be ruled out, but if an org would invest enough lawyer power, or suing in a for its case favourable court (OLG Hamburg anyone). Most small stuff would be thrown out as not substantial enough, or die even before reaching any court.
But, that actually scares me a bit when thinking about that in this context, as for me, it seems like when assuming you'd be right, this all would significantly erodes the power of copyleft licenses like (A)GPL.
Especially if a non-transparent (e.g., AI), lets call it, code laundry would be deemed as a lawful way to strip out copyright. As it is non-transparent it wouldn't be immediately clear if creative change or not, to use the criteria for copyright you used. This would break basically the whole FOSS community, and with its all major projects (Linux, coreutils, ansible, git, word press, just to name a few) basically 80% of core infrastructure.
https://www.law.cornell.edu/uscode/text/17/101
(Not a lawyer...)
Is this not subject to the same concern, but at a much greater scale? What happens when a large entity with a legal department discovers an instance of Copilot-generated copyright infringement? Is the project owner liable, is GitHub/Microsoft liable, or would a court ultimately tell the infringee to deal with it and eat whatever losses occur as a result?
In any case, I hope that GitHub is at least limiting any training data to a sensible whitelist of licenses (MIT, BSD, Apache, and similar). Otherwise, I think it would probably be too much risk to use this for anything important/revenue-generating.
I'm going to assume that there is no sensible whitelist of licenses until someone at GitHub is willing to go on the record that this is the case.
Yes, and even those licences require preservation of the original copyright attribution and licence. MIT gives some wiggle room with the phrase "substantial portions", so it might just be MIT and WTFPL
https://en.wikipedia.org/wiki/Monkey_selfie_copyright_disput...
This is not one lone developer with a passion promoting their cool side-project. It's GitHub, which is an established brand and therefore already has a leg up, promoting their new project for active use.
I think in this case, it's very relevant to post these kinds of questions here, since other people will very probably have similar questions.
The commenter isn't interrogating some indy programmer. This is a product of a subsidiary of Microsoft, who I guarantee has already had a lawyer, or several, consider these questions.
Can't companies write code that runs on customer's premises these days? Are they too afraid somebody will extract their deep learning model? I have no other explanation.
And the irony is that these companies are effectively transferring their own fears to their customers.
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 participate!
EU doesn't enforce it on the states, yes. But some (maybe all) countries that are in EU do have it.
Uh, I very much doubt that. Is there any actual precedent on this?
> We expect that IP and AI will be an interesting policy discussion around the world in the coming years, and we're eager to participate!
But apparently not eager enough to have this discussion with the community before deciding to train your proprietary for-profit system on billions of lines of code that undoubtedly are not all under CC0 or similar no-attribution-required licenses.
I don't see attribution anywhere. To me, this just looks like yet another case of appropriating the public commons.
(1) training ML systems on public data is fair use
> [...] The GitHub Copilot editor extension sends your comments and code to the GitHub Copilot service, which then uses OpenAI Codex to synthesize and suggest individual lines and whole functions.
Edit: Github mentions the issue here: https://docs.github.com/en/github/copilot/research-recitatio... and here: https://copilot.github.com/#faq-does-github-copilot-recite-c... though they neatly ignore the issue of licensing :)
Hash each function, store the hashes as a blacklist. Then you can ask the model to regenerate the function until it is copyright safe.
> We found that about 0.1% of the time, the suggestion may contain some snippets that are verbatim from the training set
That's kind of a useless stat when you consider that the code it generates makes use of your existing variable/class/function names when adapting the code it finds.
I'm not a lawyer, but I'm pretty sure I can't just bypass GPL by renaming some variables.
In the AI world, unless all GPL (etc.) code is excluded from the training data, it's inevitable that some will be "copied" into other code.
Where lawyers decide what "copy" means.
Then you have the issue of attribution with more permissive licenses.
I don't, but then I didn't go first look at the GPL code, memorize it completely, do some brain math, and then write it out character by character.
I for one wouldn't touch this with a 10000' pole until I know the answers to these (very reasonable) questions.
Another question is this: let's hypothesize I work solo on a project; I have decided to enable Copilot and have reached a 50%-50% development with it after a period of time. One day the "hit by a bus" factor takes place; who owns the project after this incident?
https://breckyunits.com/the-intellectual-freedom-amendment.h...
Great achievements like this only hammer home the point more about how illogical copyright and patent laws are.
Ideas are always shared creations, by definition. If you have an “original idea”, all you really have is noise! If your idea means anything to anyone, then by definition it is built on other ideas, it is a shared creation.
We need to ditch the term “IP”, it’s a lie.
Hopefully we can do that before it’s too late.
No one will see the light at Microsoft. The "open" source babble is marketing and recruiting oriented, and some OSS projects infiltrated by Microsoft suffer and stagnate.
In general, what is IP and how it's enforced are two separate things. Just because we've used copyright and patents to "protect" an organization's unique advantages, doesn't mean we need to keep using them in the same way. Or maybe it's the best we can do for now. That's why BSD style licences are so great.
Copyright doesn't protect "ideas" it protects "works". If an artist spends a decade of his life painting a masterpiece, and then some asshole sells it on printed T-shirts, then copyright law protects the artist.
Likewise, an engineer who writes code should not have to worry about some asshole (or some for-profit AI) copy and pasting it into other peoples' projects. No copyright protections for code will just disincentivize open source.
Software patents are completely bullshit though, because they monopolize ideas which 99.999% of the time are derived from the ideas other people freely contributed to society (aka "standing on the shoulders of giants"). Those have to go, and I do not feel bad at all about labeling all patent-holders greedy assholes.
But copyright is fine and very important. Nothing is perfect, but it does work very well.
Same goes for an engineer. Binary notation is at the root of all code, and in the intermediate nodes you have Boolean logic and microcode and ISAa and assembly and compilers and high level Lang’s and character sets. The engineer who assembles some leaf nodes that are copy and pasteable is by definition building a shared creation of which they’ve contributed the least.
And they went to the store and paid money for those things.
No it really is not that easy, as with compilers it depends on who owned the source and which license(s) they applied on it.
Or would you say I can compile the Linux kernel and the output belongs to me, as compiler operator, and I can do whatever I want with it without worrying about the GPL at all?
So, to be clear, I am allowed to take leaked Windows source code and train an ML model on it?
So your point (1) is a distraction, and quite an offensive one to thousands of open source developers, who trusted GitHub with their creations.
Looking at the four factors for fair use looks like Copilot will have these issues: - The model developed will be for a proprietary, commercial product - Even if it's a small part of the model, the all training data for that model are fully incorporated into the model - There is a substantial likelihood of money loss ("I can just use Copilot to recreate what a top tier programmer could generate; why should I pay them?")
I have no doubt that Microsoft has enough lawyers to keep any litigation tied up for years, if not decades. But your contention that this is "okay because it's fair use" based on a position paper by an organization supported by your employer... I find that reasoning dubious at best.
You can get past GPL, LGPL and other licenses this way. Microsoft can finally copy the linux kernel and get around GPL :-).
The first example parse_expenses.py uses a float for currency - that seems to be a pretty big error that's being overlooked along with other minor issues around no error handling.
I would say the quality of the generated code in parse_expenses.py is not very high, certainly not for the banner example.
EDIT - I just noticed Github reordered the examples on copilot.github.com in order to bury the issues with parse_expenses.py for now. I guess I got my answer.
The fact that you ask is a very good case in point though: Many programmers are not aware of this issue and would maybe not question the "wisdom" of the AI code generator. In that sense, it could have a similar effect to blindly copy-pasted answers from SO, just with even less friction.
[1] Exceptions may apply to e.g. finance mathematics where you need to work with statistics and you're not going to expect exact results anyway.
Of course, if you are willing to get incorrect results, such as in play money, this may be okay.
Over in payments, we use micros regularly, as documented here: https://developers.google.com/standard-payments/reference/gl...
GCP on there other hand has standardized on unit + nano. They use this for money and time. So unit would 1 second or 1 dollar, then the nano field allows more precision. You can see an example here with the unitPrice field: https://cloud.google.com/billing/v1/how-tos/catalog-api#gett...
> For example, the non-representability of 0.1 and 0.01 (in binary) means that the result of attempting to square 0.1 is neither 0.01 nor the representable number closest to it.
https://en.wikipedia.org/wiki/Floating-point_arithmetic#Accu...
The problem with float is that many people don't understand how they work to handle rounding errors correctly.
Now there are some cases where float don't cut it. And big ones. For example, summing a set of numbers (with decimal parts) will usually be screwed if you don't round it. And not many people expect to round the results of additions because they are "simple" operations. So you get errors in the end.
(I have written applications that handle billions of euros with floats and have found just as many rounding errors there as in any COBOL application)
>>> from decimal import Decimal
>>> Decimal(1000) * Decimal(16) / Decimal(31)
Decimal('516.1290322580645161290322581')
>>> 1000 * 16 / 31
516.1290322580645
https://docs.python.org/3/library/decimal.html
If it were me, I wouldn't go around bragging about how much money my software manages while being willfully ignorant of the fundamentals.
Now for your example, I see that float and decimal just give the same result. Provided I'm doing financial computations of a final number, I'm ok with 2 decimals. And both your computations work fine.
Th decimal module in python gives you number of significant digits, not number of decimals. You'll end up using .quantize() to get to two decimals which is rounding (so, no advantage over floats).
As I said, as soon as you have division/multiplication you'll have to take care of rounding manually. But for addition/subtraction, then decimal doesn't need rounding (which is better).
The fact is that everybody say "floats are bad" because rounding is tricky. But rounding is always possible. And my point is that rounding is tricky even with the decimal module.
And about bragging, I can tell you one more thing : rounding errors were absolutely not the worse of our problems. The worse problem is to be able to explain to the accountant that your computation is right. That's the hard part 'cos some computations imply hundreds of business decisions. When you end up on a rounding error, you're actually happy 'cos it's easy to understand, explain and fix. And don't start me on how laws (yes, the texts) sometimes explain how rounding rules should work.
sum = 0
for i in range(0, 10000000):
sum += 0.1
print(round(sum*1000, 2))
I mean, sure, this is a contrived example. But can you guarantee that your code doesn't do anything similarly bad? Maybe the chance is tiny, but still: wouldn't you like to know for sure?
from fractions import Fraction
# salary is $3210.55
salary = Fraction(321055,100)
monthlyRate = Fraction(16,31)
print(salary*monthlyRate)
The propagation of errors is the main challenge with floating point numbers (regardless of which base you use). The theory is well understood (in the sense that we can analyse an algorithm and predict upper bounds on the relative error), but not necessarily intuitive and easy to get wrong.
Decimal floating-point circumvents the issue by just not introducing errors at all: money can be represented exactly with decimal floating point (barring very exotic currencies), therefore errors also can't propagate. Exact arithmetic takes the other approach where computations are exact no matter what (but this comes at other costs, e.g. speed and the inability to use transcendental functions such as exp).
For binary floating point, that doesn't work. It introduces errors immediately since it can't represent money well and these errors may propagate easily.
Still, I'll take "verbose" over "error-prone and possibly wrong".
(Unclear to me yet whether the design takes advantage of this opportunity)
Well, yes, the wrong code would be used. However - the wrong code would then become more prevelant as an answer from gh, causing more people to blindly use it. It's a self-perpetuating cycle of finding and using bad and wrong code.
And again, I could argue that the opposite could happen too, people who would otherwise have written bad code could be given suggestions of better code that they would've written.
No, not quite. It also makes the cycle more permanent and its results deeply ingrained, which is what is actually relevant.
I have made this mistake without the help of any AI or copy/paste. It's still in the hands of the developer to test and review everything they commit.
But I have a feeling it will end up causing more work. e.g. the `averageRuntimeInSeconds` example, I had to spend a bit of time to see if it was actually correct. It has to be, since it's on the front page, but then I realized I'd need to spend time reviewing the AI's code.
It's cool as a toy, but I'd like to see where it is one year from now when the wow factor has cooled down a bit.
While I think the AI generating your mainline code is interesting, I must certainly agree that generating test code would be the killer feature. I would like to see this showcased a little more on the copilot page.
Interesting point - it reminds me of the idea that it’s harder to debug code than to write it. Is it also harder to interpret code you didn’t write than to write it?
This isn't like skimming through a codebase to get a sense of what the code does. You'd have to thoroughly review each line to make sure it does what you want it to do, that there are no bugs. And even then, you'd feel left behind pretty quickly because your brain didn't create the paths to the solution to the problem you're trying to solve. It is like reading a solution to a problem on leetcode vs coming up with it yourself.
In terms of the permissibility of training on public code, the jurisprudence here – broadly relied upon by the machine learning community – is that training ML models is fair use. We are certain this will be an area of discussion in the US and around the world and we're eager to participate.
How does that apply to countries where Fair Use is not a thing? As in, if you train a model on a fair use basis in the US and I start using the model somewhere else?
To be honest, I doubt that. Maybe I am special, but if I am releasing some code under GPL, I really don't want it to be used in training a closed source model, which will be used in a closed source software generating code for closed source projects.
My understanding of the open problem here is whether the ML model is intelligently recommending entire fragments that are explicitly licensed under the GPL. That would be a licensing violation, if a human did it.
A model can do this as well. Getting the length of a substring match isn’t rocket science.
I fear the coming world of training machine learning models with my face just because it was published by someone somewhere (legally or not).
While I certainly appreciate the difference, is camera observation illegal anywhere where it isn't explicitly outlawed? Meaning, have courts ever decided that the difference of scale matters?
https://en.wikipedia.org/wiki/Cover_version#United_States_co...
The clear difference is that a human's training regimen is to understand how and why code interacts. That is different from an engine that replicates other people's source code.
For example, if I am writing a criticism of an article, I can quote portions of that article in my criticism, or modify images from the article in order to add my own commentary. Fair use protects against authors who try to exert so much control over their works that it harms the public good.
It's no different from a human programmer reading code, learning from it, and using that experience to write new code. Somewhere in your head there is code that someone else wrote. And it's not infringing anybody's copyright for those memories to exist in your head.
Also, a human being that reproduces licensed code from memory - because they read that code - would constitute a license violation. The line between derivative work, and authentic new original creation is not a well defined one. This is why we still have human arbiters of these decisions and not formal differential definitions of it. This happens in music for example all the time.
I could feed the Linux kernel one function at a time into a ML model, then coerce its output to be exactly the same as the input
this is obviously copyright infringement
whereas in the github case where they've trained it on millions of projects maybe it isn't?
does the training set size become relevant legally?
Your argument would have some merit if something were created instead of assembled, but there is no new algorithm that is being created. That is not what is happening here.
On the one hand, you call this copying in fair use. On the other hand, you say this is creating new code. You can't have it both ways.
If you're going to set such a high standard for ML tools like this, I think you need to justify why it shouldn't apply to humans too.
When a human programmer who has read copyrighted code at some point in their life writes new code that is not a "new algorithm", are they in violation of the copyrights of every piece of code they've ever read that was remotely similar in any respect to the new work?
I mean, I hope not!
> On the one hand, you call this copying in fair use. On the other hand, you say this is creating new code. You can't have it both ways.
I'm not a lawyer, but this actually sounds very close to the "transformative" criterion under fair use. Elements of existing code in the training set are being synthesized into new code for a new application.
I assume there's no off-the-shelf precedent for this, but given the similarity with how human programmers learn and apply knowledge, it doesn't seem crazy to think this might be ruled as legitimate fair use. I'd guess it would come down to how willing the ML system is to suggest snippets that are both verbatim and highly non-generic.
On the same page is an image showing copilot in real-time adding the text of the famous python poem, The Zen of Python. See https://docs.github.com/assets/images/help/copilot/resources... for a link directly to copilot doing this.
You are making arguments about what you read instead of objectively observing how copilot operates. Just because GH wrote that copilot synthesizes new code doesn't mean that it writes new code in the way that a human writes code. That is not what is happening here. It is replicating code. Even in the best case copilot is creating derivative works from code where GH is not the copyright owner.
Of course I am. We are both participating in a speculative discussion of how copyright law should handle ML code synthesis. I think this is really clear from the context, and it seems obvious to me that this product will not be able to move beyond the technical preview stage if it continues to make a habit of copying distinctive code and comments verbatim, so that scenario isn't really interesting to me. Github seems to agree (from the page on recitation that you linked):
> This investigation demonstrates that GitHub Copilot can quote a body of code verbatim, but that it rarely does so, and when it does, it mostly quotes code that everybody quotes, and mostly at the beginning of a file, as if to break the ice.
> But there’s still one big difference between GitHub Copilot reciting code and me reciting a poem: I know when I’m quoting. I would also like to know when Copilot is echoing existing code rather than coming up with its own ideas. That way, I’m able to look up background information about that code, and to include credit where credit is due.
> The answer is obvious: sharing the prefiltering solution we used in this analysis to detect overlap with the training set. When a suggestion contains snippets copied from the training set, the UI should simply tell you where it’s quoted from. You can then either include proper attribution or decide against using that code altogether.
> This duplication search is not yet integrated into the technical preview, but we plan to do so. And we will both continue to work on decreasing rates of recitation, and on making its detection more precise.
The arguments you've made here would seem to apply equally well to a version of Copilot hardened against "recitation", hence my reply.
> Even in the best case copilot is creating derivative works from code where GH is not the copyright owner.
It would be convenient for your argument(s) if it were decided legal fact that ML-synthesized code is derivative work, but it seems far from obvious to me (in fact, I would disagree) and you haven't articulated a real argument to that effect yourself. It has also definitely not been decided by any legal entity capable of establishing precedent.
And, again, if this is what you believe then I'm not sure how the work of human programmers is supposed to be any different in the eyes of copyright law.
No. We both aren't. I am discussing how copilot operates from the perspective of a user concerned about legal ramifications. I backed that concern up with specific factual quotes and animated images from github, where github unequivocally demonstrated how copilot copies code. You are speculating how copyright law should handle ML code synthesis.
You say I'm not ... but then you say, explicitly in so many words, that I am:
> You are speculating how copyright law should handle ML code synthesis.
I don't get it. Am I, or aren't I? Which is it? I mean, not that you get to tell me what I am talking about, but it seems like something we should get cleared up.
edit: Maybe you mean I am, and you aren't?
Beyond that, I skimmed the Github link, and my takeaway was that this is a small problem (statistically, in terms of occurrence rate) that they have concrete approaches to fixing before full launch. I never disputed that "recitation" is currently an issue, but honestly that link seems to back up my position more than it does yours (to the extent that yours is coherent, which (as above) I would dispute).
Now that five days have passed, there have been a number of examples of copilot doing just that, replicating code. Quake source code that even included comments, the famous python poem, etc. There are many examples of code that has been replicated - not synthesized but duplicated byte for byte from the originals.
Think what you will, but your lies about the GPLv3 can easily be tested. Can you point me to some GPLv3 software in the Apple tech stack?
We actually already know the answer.
Apple had to drop Samba (they were a MAJOR end user use of Samba) because of GPLv3
I think they also moved away from GCC for LLVM.
In fact - they've probably purged at least 15 packages I'm aware of and I'm aware of NO GPLv3 packages being included.
Not sure what their App Store story is - but I wouldn't be surprised if they were careful there too.
Oh - this is all lies and apple's lawyers are wrong? Come one - I'm aware of many other companies that absolutely will not ship GPLv3 software for this reason.
In fact, by 2011 even it was clear that GPLv3 is not really workable in a lot of contexts and alternatives like MIT became more popular.
https://trends.google.com/trends/explore?date=all&geo=US&q=%...
Apple geared up to fight DOJ over maintaining root control of devices (San Bernadino case).
Even Ubuntu has had to deal with this - SFLC made it clear that if some distributor messed things up ubuntu would have to release their keys, which is why they ended up with a MICROSOFT (!) solution.
"Ubuntu wishes to ensure that users can boot any operating system they like and run any software they want. Their concern is that the GPLv3 makes provisions by which the FSF could, in this case as the owner of GRUB2, deem that a machine that won't let them replace GRUB2 with something else is in violation of the GPLv3. At that point, they can demand that Ubuntu surrender its encryption keys used to provide secure bootloader verification--which then allows anyone to sign any bootloader they want, thus negating any security features you could leverage out of the bootloader (for example, intentionally instructing it to boot only signed code--keeping the chain trusted, rather than booting a foreign OS as is the option)." - commentator on this topic.
It's just interesting to me that rather than any substance the folks arguing for GPLv3 reach for name calling type responses.
Apple used to also interoperate wonderfully if you were using Samba SERVER side too because - well, they were using Samba client side. Those days were fantastic frankly. You would run Samba server side (on Linux), then Mac client side - and still have your windows machines kind of on -network (for accounting etc) too.
But the Samba folks are (or were) VERY hard core GPLv3 folks - so writing was on the wall.
GPLv3 shifted things really from preserving developer freedom for OTHERs to do what they wanted with the code, to requiring YOU to do stuff in various ways which was a big shift. I'd assumed that (under GPLv2) there would be natural convergences, but GPLv3 really blew that apart and we've had a bit of a license fracturing relatively.
AGPLv3 has also been a bit weaponized to do a sort of fake open source where you can only really use the software if you pay for a commercial license.
I can't dig it up right now but someone can probably find it.
But the BSD's used samba for a while as well.
Everything else you write is just anecdotes about how certain companies have chosen to do things.
If I sell an open source radio with firmware limiting broadcast power / bands etc to regulated limits and ranges - under GPLv3 I can lock down this device to prevent the buyer from modifying it? I'm not talking about making the software available (happy to do that, GPLv2 requires that). I'm talking about the actual devices I build and sell (physical ones).
I can build a Roku or Tivo and lock it down? Have you even read the GPLv3? It has what is commonly called the ANTI-tivoisation clause PRECISELY to block developers from locking devices down for products they sell / ship.
If I rent a device and build in a monthly activation check - I can use my keys to lock device and prevent buyer from bypassing my monthly activation check or other restrictions?
The problem I have with GPLv3 folks is they basically endlessly lie about what you can do with GPLv3 - when there is plenty of VERY CLEAR evidence that everyone from Ubuntu to Apple to many others who've looked at this (yes, with attorney's) says that no - GPLv3 can blow up in your face on this.
So no, I don't believe you. These aren't "just anecdotes" These care companies incurring VERY significant costs to move away / avoid GPLv3 products. AGPLv3 is even more poisonous - I'm not aware of any major players using it (other than those doing the fake open source game).
Now we can debate whether or not it's a good thing that the user gets full control of his device if he wants it. I think it is. You?
This is why SaaS/Cloud companies dislike them and fuel FUD campaigns.
1a) commercial
1b) non-transformative: in order to be useful, the produced code must have the same semantics as some code in the training set, so this does not add "a different character or purpose". Note that this is very different from a "clean room" implementation, where a high-level design is reproduced, because the AI is looking directly at the original code!
2) possibly creative?
3) probably not literally reproducing input code
4) competitive/displacing for the code that was used in the input set
So failing at least 3 out of 5 of the guidelines. https://www.copyright.gov/fair-use/index.html
1b) This is false. This is not literally taking snippets it has found and suggesting it to the user. That would be an intelligent search algorithm. This is writing novel code automatically based on what it has learned.
2) Definitely creative. It's creating novel code. At least it's creative if you consider a human programming to be a creative endeavor as well.
3) If it's reproducing input code it's just a search algorithm. This doesn't seem to be the case.
4) Most GPLed code doesn't cost any money. As such the market for it is non-existent. Besides copilot does not displace the original even if there were a market for it. As far as I know there is not anything even close to comparable in the world right now.
So from my reading it violates none of the guidelines.
Okay, but that's...not much of a counterargument (to be fair, the original claim was unsupported, though.)
> Maybe I am special, but if I am releasing some code under GPL, I really don't want it to be used in training a closed source model
That's really not a counterargument. “Fair use” is an exception to exclusive rights under copyright, and renders the copyright holder’s preferences moot to the extent it applies. The copyright holder not being likely to want it based on the circumstances is an argument against it being implicitly licensed use, but not against it being fair use.
It seems like some of the chatter around this is implying that the resultant code might still have some GPL still on it. But it seems to me that it's the trained model that Microsoft should have to make available on request.
If you train az ML model on GPL code, and then make it output some code, would that not make the result a derivative of the GPL licensed inputs?
But I guess this could be similar to musical composition. If the output doesn't resemble any of the inputs, or contains significant continous portions of them, then it's not a derivative.
In this particular case, the output resembles the inputs, or there is no reason to use Github Copilot.
This just gives me a flashback to copying homework in school, “make sure you change some of the words around so it’s not obvious”
I’m sure you’re right Re: jurisprudence, but it never sat right with me that AI engineers get to produce these big, impressive models but the people who created the training data will never be compensated, let alone asked. So I posted my face on Flickr, how should I know I’m consenting to benefit someone’s killer robot facial recognition?
In what context? You are planning on commercializing Copilot and in that case the calculus on whether or not using copyright protected material for your own benefit changes drastically.
----> for purposes such as criticism, news reporting, teaching, and research <----, without the need for permission from or payment to the copyright holder.
Copilot is not criticizing, reporting, teaching, or researching anything. So claiming fair use is the result of total ignorance or disregard.
2. While I agree with your stance, it is not by itself sufficient. If you provide the automation but you do not correct the perverse incentives (or you worry about correcting them only later) that you mention, then you are contributing to widening the disparity between a category of workers (who have now lost their leverage) and those with assets and capital (who have a reduced need for workers).
Regardless, programmers would be hypocritical to decry having their jobs automated away.
If you're asking why do people respond the way they do to disparity, then I can only speculate that it has something to do with the meaning of life.
By your reasoning, maybe we don't need backhoes and should just hire a bunch of guys with spoons instead?
historically when has that sort of 'tit-for-tat' style of argument ever been helpful?
the correct approach would be "we've observed first hand the problems that we've cause for society, how can we avoid creating such problems for any person in the future?"
It might seem self-serving, and it is, but 'two wrongs don't make a right'. Let's try to fix such problems rather than serving our sentence as condemned individuals.
It's not tit-for-tat, it's a wake up call. As in, what exactly do you think we've been doing with our skills and time?
> ""we've observed first hand the problems that we've cause for society"...
But not everyone agrees that this is actually a problem. There was a time when being a blacksmith or a weaver was a very highly paid profession, and as technology improved and the workforce became larger, large wages could no longer be commanded. Of course the exact same thing is going to happen to developers, at least to some extent.
How many?
> I don't think it's controversial that we become fair game for that same automation process we've been leading.
This is not correct. A human (developer) displacing another human (business person) is entirely different than a tool (AI bot) replacing a human (developer).
Regardless, this is the Lump of Labour fallacy (https://en.wikipedia.org/wiki/Lump_of_labour_fallacy).
In this case, it is assumed that the global amount of development work is fixed, so that, if AI takes a part of it, the equivalent workforce in terms of developers, will be out of job. Especially in the field of SWE, this is obviously false.
It also needs to be seen what this technology will actually do. SWE is a complex field, way more than typing a few routines. In best case (technologically speaking) this will be an augmentation.
That's not what is happening though, a few developers replace thousands of business and industry people with automated tools. Say, automated route planning for package delivery, would take many thousands of humans if not for the AI bots that do the job instead.
> SWE is a complex field, way more than typing a few routines. In best case (technologically speaking) this will be an augmentation.
Of course there will always be some jobs for humans to do. Just like there are still jobs for humans loading thread into the automated looms and such.
But your arguments against automation displacing programming jobs ring hollow. People said the same thing about chess playing programs, they would never be able to understand the subtlety or complexity like a human could.
Without reading and understanding the lump of labour fallacy, it can't be understood the relation between the fallacy and the displacement of jobs. In short, the fallacy is not incompatible with the displacement argument; the difference is in the implications.
> But your arguments against automation displacing programming jobs ring hollow. People said the same thing about chess playing programs, they would never be able to understand the subtlety or complexity like a human could.
Chess is a finite problem, SWE isn't, so they can't be compared.
If there is a pathway to improving this AI assist efficiency say by restricting the language, methodology, UI paradigm and design principles, it will happen quick due to market incentives. The main reason SWE is complex is it's done manually in myriad subjectively preferred ways.
Only some software developers seem interested in replacing themselves in order to enrich their corporate masters (mains?) even further.
Just don't use this tool!
And one could argue that this means we all pay more for health and legal services than we otherwise would. You have to calculate both costs and benefits; what price does society pay for those few people having very high paying jobs?
The challenge in software development is understanding the real world processes and constraints and turning them into a design for a functional & resilient system that doesn't collapse as people add every little idea that pops into their head.
If the hard part was "typing in code" then programmers would have been replaced long ago. But most people can't even verbally explain what they do as a series of steps & decision points such that a coherent and robust process can be documented. Once you have that it's easy to turn into code.
The problems we spend our days solving may change. But there will always be problems for humans to solve.
It all boils down to who is capturing the value for the effort and time expended. If a mediocre software engineer can compete against senior engineers with such augmentation, that seems like a win. Less time on learning language incantations, more time spent delivering value to those who will pay for it.
Your own example of the CEO becoming a CTO can be used in every level and part of the business.
Now the receptionist is building office automation tools because they can describe what they want in plain English and have this thing spit out code.
Approximately nothing.
The average knowledge worker somewhat more, but lots of them are at the level of “I can consume a pivot table someone else set up”.
Sure, there are highly-productive, highly-skilled excel users that aren't traditional developers that can build great things, but they aren’t “your average person”.
https://news.ycombinator.com/item?id=26386419 (HN: Excel Never Dies)
https://news.ycombinator.com/item?id=20417967 (HN: I was wrong about spreadsheets)
https://mobile.twitter.com/amitranjan/status/113944938807223... (Excel is every #SAAS company's biggest competitor!)
We may not call them developers or programmers (or we might; I’ve been one of them as a fraction of my job at different times, both as a “fiscal analyst” by working title and as a “programmer analyst” by title), but effectively that's what they are, developers using (and possibly exclusively comfortable with) Excel as a platform.
Is it a coincidence that the same company that makes Excel is trying to… “democratize” and/or de-specialize programming?
I don’t really think so, but shrug.
And sure, we do now have tools like square space which fully automate making a basic business landing page and online store. But the bar has been raised and we now have far more complex websites without developer resources being wasted on making web stores.
I read Mythical Man Month many years ago and enjoyed it. Time for a re-read. Of course it won't cover the third wave very well though. Would love to see a blog post cover that.
Edit:
To expand a little and not sounds so completely negative towards AI, seems like there could be value in training models to predict whether a patch will be accepted, or whether it will cause a full build to fail.
It would be a "cool tool" if it inspected the code statically and dynamically. Testing the code to see if it actually does what the AI thinks it should do. From running small bits of code on unit level to integration and acceptance testing. Suggest corrections or receive them. _That_ will save time and I and companies will pay for.
Also you cannot call this the "third revolution" if it is a paid service.
It... couldn't, in net.
Tools which improve developer productivity increase the number of developers hired and the number of tasks for which it is worthwhile to employ them and the market clearing price for development work.
See, for examples, the whole history of the computing industry as we’ve added more layers of automation between “conceptual design for software” and “bit patterns in hardware implementing that conceptual design as concrete software”.
It might displace or disadvantage some developers in specific (though I doubt a large portion) by shifting the relative value of particular subskills within the set used in development, I suppose.
A tool which increases how rapidly we can output code—correct code—would allow for more time spent on hard tasks.
I can see the quality of some "commodity" software increasing as a result of tools in this realm.
Or, perhaps, lower unit costs of computing lead to far far greater demand for computing since it became practical to apply to more domains.
I love it. So often the code suggestions accurately anticipate what I planned to do next.
It's especially fun to write a comment or doc string and then see Copilot create a block of code perfectly matching your comment.
For a seasoned veteran writing the code was never really the hard part in the first place.
Yes, to most coders this Copilot software is just a fancy keyboard.
Or, I guess a sentinel phrase that the scraper could explicitly check: `github-copilot-optout: true`
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 international wrinkles, though.
> 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 don't see any attribution here.
MIT may say "substantial portions" but BSD just says "must retain".
would MS position then be different?
If its publicly available than its fair game to use it to learn and base ideas on.
I started working at a healthcare company earlier this year, and my whole approach to software has needed to change. It's not about implementing features any more - every change to our embedded code requires significant unit testing, code review, and V&V.
Having a standards-compliant Copilot would be wonderful. If it could catch some of my mistakes before I embarrass myself to code-reviewing colleagues, the codebase would be better off for it and I'd be less discouraged to hear those corrections from a machine than a person.
I mean... why would 95% of developer jobs exist with this tech available?
You just need that 5% of devs who actually write novel code for this thing to learn from.
An Isaac Asimov story about someone who didn't take to the program and, as a result, got picked to create new things because someone has to make them.
If you want to read the whole thing, it's here: