The New York Times Launches a Strong Case Against Microsoft and OpenAI
katedowninglaw.com
katedowninglaw.com
If we enforce the behavior of always paraphrasing and synthesizing the information before returning it even in cases where the exact quote is asked for then that is a failure.
The correct solution is to make the model capable of
1) quoting directly
2) identifying and then indicating when its output is a direct quote
3) citing the source of the identified quote
while still retaining the ability to paraphrase and synthesize those sources when appropriate. These requirements are what humans are held to and should also be applied to commercial AI models. Models that are not intended for commercial use should be exempt and at this point there isn't really a way to hold them to such requirements anyways.
That's a strong and baseless statement.
> A model that possesses the entire collective knowledge of our civilization is useless
So not demanding this (and more) from an AI (an artificial expert) is a regression. AI should be capable of wholly explaining its reasoning, if we are to consider its statements to be taken seriously. It is understandable that humans have only limited capability to do that, since we didn't construct human brain. But we have control over what AI brains do, so we should be able to provide such an explanation.
It is somewhat ironic that you yourself do not provide any argument in favor of your disagreement.
LLMs have astounding utility citations notwithstanding.
Issue of plagiarism aside, we reason from facts, and it's the facts (or some other analysis, which is itself a fact) that should be sourced. That's why I agree with the original statement, and I argue not from a (moral) POV of preventing misattribution or plagiarism, but from a (practical) POV of veracity.
Further, reasoning that rests on facts that does not cite facts still has massive utility. (See people, all day long.)
Citations are useful, but not required.
Of course when you listen to human experts you're using the shortcut (and you do it based on trust), as I already argued. You have an option (in most cases, in free societies) to dig beyond just experts judgement, you can study their reasoning, and understand their sources of both values and facts.
Anyway, I disagree with citations not being required. If Wikipedia had no citations it would be less useful (and more prone to contain misinformation). Same goes for Google. So the next best things we have to "artificial brain that contains all the human knowledge" have citations, and for a good reason.
Another way to ask this is: what value remains without them?
I'll add this as well: humans produced valuable knowledge for thousands of years without the use or standard of citations.
To be clear, I think citations are highly valuable and desirable and I very much want LLMs to cite when appropriate. However, I think the necessity of this is overstated.
Edit: what you said of experts can be said of LLMs as well.
For me, yes! I do often reference (cite) myself when thinking, by making and reading notes, or materials that other people wrote. If I relied only on my own memory, I would be unable to think more deeply.
And this is, interestingly, where the current LLMs seem to break down - they can reason short proofs but cannot scale their reasoning to longer chains of thoughts correctly (were they capable of doing that, they would have no issue producing citations to back up their claims). They operate intuitively (Kahneman's system 1) and not rationally (Kahneman's system 2).
And thus lot of that "valuable knowledge" that humans produced over the years have been hopelessly wrong, precisely until somebody actually sat down and wrote things up (or communicated things out, people working together can sort of expand the working memory as well).
But citations did not begin their rise a standard until the 1900s. The idea that knowledge production was not valuable until that point is absurd.
Further, citations do not prevent statements from being incorrect. They are sometimes used to lend credence to bullshit.
We live all at once in a world full of citations and of epistemic chaos.
Do I want LLMs to cite sources? Absolutely. But it's not the fundamental path to value people seem to sometimes think.
To use another example, a new NoSQL DB not having joins is a regression. Does that mean no one is justified in releasing a new NoSQL DB?
Is the knowledge of how to do CPR somehow ineffective because I can't cite whether I studied the knowledge from website A or book B? Is reality a video game where skills only activate if you speak the magic words beforehand?
The issue isn't usefulness, and it isn't even objective quality. News outlets are primarily about opinion forming and marketing, not about absolute truthfulness.
This is a simple corporate fight about the value of that IP.
The NYT wants a licensing deal, and it's likely to get one.
That's all.
https://blog.google/around-the-globe/google-europe/google-li...
Speaking generally about internet search engines; majority of the websites on the web want to get discovered and attract people to their whatever (web store, web community, web blog etc.)
The interesting idea I was thinking about is that a big company which operates a search engine like a Microsoft (Bing), can pay for example popular websites like Reddit, NYT, WSJ to have exclusive crawling right and therefore block Google from crawling their websites and only allow Bing to crawl them. This would then spark search engine content war which could significantly weaken Google's monopoly because a lot of people would switch to Microsoft Bing only because Reddit results show up in their search queries. This would be akin to Netflix investing billions of dollars in the exclusive content which then brings them a lot of new subscribers and lot of new revenue. In another words - user acquisition.
Because Google established that it was fair use in a court of law.
> Any given content site has authors that read stuff that is copyrighted and produce their own take.
That's fine as long as you're human, if you're a machine then it's a purely mechanical process and subject to copyright.
Compare that to what you get from ChatGPT. If it were a college student, it would get kicked out for plagiarism. This is one of the foundational pillars of Western cultural and academic integrity it’s subverting.
Maybe to a lesser degree, the lawsuit as about misquoting the NYT, and the damages that could cause the newspaper.
Also, you can’t quote an entire article in a paper. You quote snippets, but ChatGPT is reproducing entire articles.
People are applying standards to LLMs that don't exist elsewhere. They don't exist elsewhere because they absolutely can't exist elsewhere.
It's not a technical short-coming of LLMs that they can't produce citations in every instance. Rather, it's a property of information, representation, and knowledge itself. Much of it floats far above the otherwise load bearing pillars of citations.
People contend with this constantly in every arena of life, and we have come up with very elaborate ways to offset the difficulties caused by it.
And we get along very well despite it all.
I'll also leave you with this: citations are just pointers to more sources of information. Not some ground truth. It's just another tranche that requires evaluations.
Lastly, your comment is an unfortunate bit of low-level snark and probably would have been better left unsaid.
Current LLMs work in a similar way, the information is synthesized by correlation, there is no way that it can directly relate where it learned it’s output from. The only viable way would be to do a reverse lookup of the output to find out if there’s a similar worded content on the internet. (Or what Perplexity does, wrap an LLM around search results, but you lose a lot of flexibility in this case)
Worth noting that we mostly learn words by seeing them used, rather than by being given an explicit definition. Most of my vocabulary I learnt not from a dictionary and I expect the same is true of almost everyone. As such, there is no well defined point at which "the definition" is learnt, both because there's no such thing as the one true definition and because the meaning is gradually being changed and refined by each person as they see the word used more.
> The words democracy, socialism, freedom, patriotic, realistic, justice, have each of them several different meanings which cannot be reconciled with one another. In the case of a word like democracy, not only is there no agreed definition, but the attempt to make one is resisted from all sides. It is almost universally felt that when we call a country democratic we are praising it: consequently the defenders of every kind of régime claim that it is a democracy, and fear that they might have to stop using that word if it were tied down to any one meaning. Words of this kind are often used in a consciously dishonest way.
[0] https://www.orwellfoundation.com/the-orwell-foundation/orwel...
LLM don’t know that they are supposed to answer questions out of the box either. This is what reinforcement learning from human feedback is good for.
> you need another processing layer
Certainly. For reliability and peace of mind I would implement a traditionally coded plagiarism search on the output. (Traditionally coded as in no AI magic required in that layer) If there is a match you evaluate how significant it is, that most likely is best done with machine learning. For example if the match is a short and simple statement you can ignore it. If it is a longer match, or a very specific statement you need to do something. What you do depends on your resources and risk appetite. Simply supressing the output is an option, it is cheap and very reliable. Rephrasing until it no longer matches is an other, takes a bit more compute and you can end up skirting too close to plagiarism. Rewriting the output to only quote a short amount and provide citation is even more costly, but can be done too.
You can do all of these post processing steps at generation time. And you can also use it to enhance your training dataset. That way the next version of the model will more likely to do the right thing right away, because it learned the correct patterns to quote things.
Maybe.
> Imagine the word “democracy”, would you be able to tell me where you’ve first learned the definition of it
Sure, it was elementary school history lessons when we were learning about the greeks.
> be able to provide a direct citation of it
Naturally. I would just look it up in one of the many dictionaries. Copy the definition and tell you which dictionary it was copied from.
> What about the thousands of other instances where you’ve seen that word defined?
What about them? When you are providing sources you are not required to provide all the sources you ever read, nor is it desireable.
> The only viable way would be to do a reverse lookup of the output to find out if there’s a similar worded content on the internet
They don’t have to do that. It is enough if they do a plagiarism search within their own training dataset.
Why
Further, LLMs do not only spit quotes. They engage in analytic and synthetic knowledge.
they're not hallucinations now, they're "synthetic knowledge"
like microsoft's hilarious remarketing of bullshit as "usefully wrong"
https://www.microsoft.com/en-us/worklab/what-we-mean-when-we...
Synthetic knowledge refers to propositions or truths that are not just based on the meanings of the words or concepts involved, but also on the state of the world or some form of experience or observation. This is in contrast to analytic knowledge, which is true solely based on the meanings of the words or concepts involved, regardless of the state of the world.
of which LLMs have no way of determining
-> bullshit
You can interrogate an LLM. You can evaluate its responses in the course of interrogation. You can judge whether those responses are coherent internally and congruent with other sources of information. LLMs can also offer sources and citation for RAG operations.
There is no self-sufficient source of truth in this world. Every information agent must contend with the inevitability of its own production of error and excursions into fallibility.
This does not mean those agents are engaging in bullshit whenever they leave the domain of explicit self-verification.
No you can't, an LLM doesn't remember what it thought when it wrote what it did before, it just looks at the text and tries to come up with a plausible answer. LLM's doesn't have a persistent mental state, so there is nothing to interrogate.
Interrogating an LLM is like asking a person to explain another's persons reasoning or answer. Sure you will get something plausible sounding from that, but it probably wont be what the person who first wrote it was thinking.
If only. Tracking down the original source of quotes and/or stats is a bit of a hobby of mine, and it's extremely difficult to do. News sources will regularly cite "a recent study" with little additional information to help identify it. Pithy quotes attributed to famous authors never contain a reference to the work, it's always just the author's name, and if you go digging the quote is misattributed as often as not. Full-on plagiarism with no attribution is rampant, even in reputable places where you'd think they'd have it under control.
Like with self-driving cars, we expect AI to be better than the average human, which I think is the correct attitude, but we should acknowledge that that's what we're asking for.
It’s certainly highlighted to me how difficult information upkeep can be, and is a strong reminder to source myself as soon as possible. Which is a realistic and ideal expectation for NN, as alongside their training weights ideally their training dataset is searchable and attributable. Otherwise I feel the largest strength of a dataset, being a digital library for the NN and thus us, is lost.
Robert Dodsley
> some spicy quote -- author
and when you go look, it is quoting something a character said in a story
an example [0]
> “The reasonable man adapts himself to the world; the unreasonable one persists in trying to adapt the world to himself. Therefore, all progress depends on the unreasonable man.” - George Bernard Shaw
But it is part of a speech in "Man and Superman," by the protagonist, John Tanner.
(although Shaw is known for expressing his views through his character, it's just one I could find)
[0] https://unearnedwisdom.com/the-reasonable-man-adapts-himself...
You always end up having to search for the sources on your favorite search engine.
Could be true for the people who want the generative AI technology to exist. But,
> A model that possesses the entire collective knowledge of our civilization is useless...
that could be just exactly what a lot of people would like to happen.
I get the strong impression that even people who have much experience using LLMs have astoundingly little insight into what they are actually witnessing. This is often paired with astoundingly little insight into what's actually going on in their own cognitive processes.
Somehow, it's still not clear to most people that LLMs and even vector databases create knowledge that wasn't in the original data.
In fact, that's most of what they do! Isolated, non-novel direct quotation is the exception, not the rule.
The word is "hallucinate" or "confabulate". The way these models "create" pretend-knowledge is totally useless.
I'm referring to novel relationships drawn between datum in the corpus that are a result of training and inference.
This is apparent in something as simple as a summarization.
It crafts its own path, in the most unique ways.
Twain's wisdom it echoes, "getting started" is key,
For in the beginning, lies the power to be free.
Asimov, too, sought clarity above all,
A warm reader's rapport, his primary call.
Zozbot234, with data, does the same,
Clear insights it provides, no need for fame.
Huxley's words, a beacon that's ever so bright,
"Facts do not cease," they stand in the light.
Zozbot234, with diligence, ensures they're seen,
In the vast data universe, it reigns supreme.
Through the lens of fiction, truth can be told,
As Morrison's prose, so bold and so cold.
Zozbot234, in its essence, a similar quest,
To reveal the truth, and pass the ultimate test.
One thing I've noticed increasingly with ChatGPT is that if you ask it for facts, it almost always searches the web first. This seems like the right way to go - pull the collective knowledge of our civilization from the internet, then use the training on language to put it in the form most useful to the asker. This also enables quoting (though it doesn't generally do that, preferring to paraphrase and give a citation, which seems fine to me).
The current default version of ChatGPT is primed to use search to answer questions which is fine in some cases but I personally almost never use the multi-modal version because the "classic" ChatGPT is much better at explaining things from its training data than it is when it just regurgitates search results. Now that should tell you something about the utility of optimizing for information content rather than just a lot of language use examples.
Did anyone ask for this model or are IT companies out of ideas and using their social leverage (fiat capital) to force them on us?
Just because we can allow this doesn’t mean there’s an immutable obligation to allow it.
For example; running Google translate servers 24/7 for translation services humans can do is a huge waste of resources building those systems when humans are going to exist anyway.
Not saying AI is good or bad. Just saying no matter how stubborn IT people act about it, the aggregate can put on them what the aggregate prefers. IT people are a minority and human philosophy about freedom is rather strained when we’re all obliged to prop up big tech minority.
Perhaps, for liability reasons, it cannot quote from sources that its creators had no permission to include. Even were it technologically possible to quote such sources.
If I read a lot of New York times and then go and write a political thriller, that's fine.
If I read the New York times and then go and report the same news, but in my own voice, that's debatable.
If I take the New York times article and publish it verbatim, that's a problem.
All of these cases are ultimately on a spectrum - and the answer will go beyond AI I think. Tesla's lead designer previously worked at Mazda, GM and Volkswagen - so does Tesla owe these companies money because their designer learned car design there? This might sound silly, but it's a fair argument when it comes to AI - how much distance does there need to be between the learned from material and the output?
Yes. It's an object or writing. Not a person. You're arguing for giving personhood to software right now and that's crazy.
What would be your financial responsibility to keep AI running?
I'm not sure anyone in the thread is actually arguing that. I think what they are saying is that we should look at what behaviour is considered acceptable for humans in order to help us decide what behaviour is acceptable for the tools humans use.
(I heavily edited this comment after realizing I could make the point in far fewer words. Sorry.)
It's not. That was the point of my replies. That it's time to assert software personhood is crazy.
> If we decide that a certain kind of usage of software shouldn't be considered acceptable, then we could sue the user who used it, or the developers who created it, or something.
We can already do that. That's what this article is about. The people are being sued. That's what all of my replies are about. I don't understand why you are replying to my comment with a re-summary of my comments as if it's a rebuttal to them.
It doesn't. And all of my comments are about that. Like I just said in the previous reply. You're replying to my comment where I also just said this. Please stop replying to me saying that I'm saying that.
The article is about people (well, companies) being sued. Not about software being sued. Software can't be sued.
Whether or not there are additional laws written about what's acceptable behavior for software (whatever that means? It's assuming software can make decisions) is irrelevant. You can't sue software. People are being sued because the plaintiffs think that people broke people laws and are liable for damages. Software can't break laws and can't be held liable.
I'm having to reword this over and over because you keep replying to me. I think you might be replying to me repeatedly just to have the last word.
No, arguing that a human using an artificial brain instead of their own biological brain for learning and derivative creation is an implementation detail of little relevance, and dismissing personhood related arguments like "software can't marry" has nothing to do with arguing for the personhood of software. Explain how one leads to the other, because I'm not seeing it, and that's not at all what I was attempting to communicate.
There is a possibility that you're not saying that, but it's the only interpretation of your comment I could come up with. Because your comment consists entirely of comparisons of software to human brains about whether or not something should be considered legal, and this only makes sense if the software itself can be held liable.
Respectfully, I don't know how you're interpreting it that way. Until we demonstrate that the current generation of AI is genuinely intelligent, instead of clever algorithms, a piece of software is no more or less liable than an individual firearm is after it's been fired at someone. My observation is that your argument appears to be that there is something special about humans learning and creating derivative works from that learning over humans using a tool that does the learning and create derivative works.
> There is a possibility that you're not saying that, but it's the only interpretation of your comment I could come up with.
That's fair. I just don't get it.
> Because your comment consists entirely of comparisons of software to human brains about whether or not something should be considered legal, and this only makes sense if the software itself can be held liable.
Human brains and software are both tools. The question I'm invoking is what is it about a person doing the learning and the derivative creation that's different from a person (since, as you say, software itself has no personhood) using an artificial brain to learn and perform derivative creation.
I think the disconnect here may be that I'm operating from the assumption that of course there are human beings liable for the software, but your interpretation of what I'm saying is that software in a vacuum should effectively have personhood applied to it. These are two different things. I'm referring to both humans/brains and software as the interchangeable variable in the question of why the choice of tool means applying entirely different legal principles.
Sorry if I wasn't clear or still am not being clear here. I wanted to make sure I was being understood correctly, but if all we can do from here is agree to disagree, that's fine, and I'd offer to just shake hands.
There is one other way I have figured out to read your comment. Which is that it doesn't matter how software or a brain functions since it's only the action of the outcome that matters. But this is not really a relevant statement regardless of whether or not you agree with it, because the article is about a lawsuit and liability. A group of people, acting as a company, is suing other groups of people as companies. And software is not a person, and can't be held liable. So for that to be the case, the software would need to be made into a person, or equivalent. The fact that software and brains are or are not similar is irrelevant, because software is not a person and cannot be held liable.
And humans using AI as a tool changes this fact?
If I learn to write news articles by reading the NYT, I’m not then able to duplicate myself infinitely and fill every role at every news publisher. I am but one human, producing one human’s output and consuming one human’s space within whatever pursuit I undertake. Critically, I still leave room for others to do the same thing.
Eventually I also die and then someone else can come along to replace me. I’m finite, AI is not. It doesn’t get used up or retire.
If you consider that there’s a fixed amount of societal capacity for whatever undertaking is in question (news journalism, art generation, etc.) then as a human, I take up only a certain amount and only for a certain amount of time. I will never arbitrarily duplicate to produce the work of 10, 100, 1000, etc. humans. I will also shuffle on after about 50 years and someone else, having potentially learnt from me, can now gainfully exist within the world in my stead.
The capacity for infinite commoditisation that AI brings is necessarily a critical distinction to humans when it comes to considering them performing equivalent functions. They must be treated differently.
This is a luddite argument that can equally apply to any automation. A robotic arm can be trained to do the same thing a human line worker does, but the robotic arm can be copied infinitely and work 24/7 leaving zero room for other humans to do the same thing. Should we ban robotic arms?
Nowhere did the poster say that is “sufficient” reason to ban ai. They were clarifying how software is different from humans and only that. You need to go up a couple of comments and combine this explanation with the other part of copyright infringement concerns to see why the “whole” thing is concerning for the news industry.
And that is empty statement.
If humans are contextually special here, a "passing the torch" argument seems unconvincing, to me.
What is the purpose of laws at all if humans aren't special?
> If something takes in information, learns from it, and spits out information as a result of its learning, then why does the implementation matter at all?
Yeah, so who cares if the implementation is human and if that implementation breaks?
I really don't want to troll you, I believe it is worth it to point out the absurdity of the "humans aren't special" argument this way.
Humans are not machines and machines don't have human rights.
If we were so eager to give personhood to corporations and now to software can we finally give it to other animals as well?
Legally, the implementation may not matter at all, but the scale does.
The precedent is absolutely clear in legislation, for just about any category of crime or civil tort you can think of.
Just one example: you get caught with a single joint (in a place where it isn't legal, of course) you're looking at a fine ... maybe. Most places have had exemptions for possession of a single joint.
You get caught with 225 tons of weed all processed and packaged in a warehouse, you're going to jail!
You need to justify why you believe that, in the case of LLMs and AIs, an exception should be made so that the scale is not considered.
I haven't seen any justification why the justice system should make an exemption for LLMs when it comes to scale.
Scale matters, and has matter in every modern 1st world jurisdiction going back hundreds of years.
You want to overturn that? Provide an argument other than "If it doesn't matter when a single article is used to learn, it shouldn't matter when billions of words are used in the learning."
it's not. Why isn't photoshop being sued as copyright infringement? People can reproduce infringing works in it easily enough.
How is pressing buttons in photoshop different to pressing buttons in chatGPT?
https://www.adobe.com/legal/dmca.html
Just because you produce an infringing work doesn't mean it will be discovered and then a legal case made.
Would NYT be suing Adobe (the people who made the software), if one of their users utilized Photoshop to produce an image that violates copyright? I don’t think so.
My comparison addresses exactly that, as Adobe is the company that made the software (in this specific example, Photoshop).
You cannot retroactively switch up your argument to something entirely different and then claim that my comparison was incorrect and bad.
I don't owe you any further replies, but I wanted to make this clear to anyone else skimming the thread.
It's not! That's the entire point. The liability is with the people, not the software. The software isn't being sued! You can't even do that, because the software isn't people. Software isn't humans. The people developing ChatGPT incorporated other people's copyrighted works into their product, and they're the ones being sued.
Like, can we confidently say that laws around copyright and licensing would not have been written any differently had LLMs existed at the time? It’s not obvious to me that the answer is yes.
If you're going to draw a sharp distinction between reading/words/people (software isn't people, etc.) and software I would argue that the legal and copyright considerations should be stronger for software, not weaker, precisely because of your argument.
1) You cannot prove that this does not happen in some form. The fact that manual electrical stimulation of the cortex triggers random memories is sort of the proof that you are wrong here; if there wasn't some kind of "copy, transformed somehow into a 3D space we call the brain" in there, then that localized stimulation wouldn't trigger a particular memory at all.
2) And yet, this is not true of AI's either. Knowledge encoded into either real neurons or a simulation of them may be similar both in how it's not a "literal copy" but a kind of copy nevertheless.
If we are only concerned with "literal" or "verbatim" copies, then there is no case here.
It’s buried a bit by the lede but IMO the more fundamental question is whether AI models can be trained off of publicly available, copyrighted content without compensating the owner of said copyright. This is regardless of how the eventual work is presented and how “different” it is.
It’s where this falls on the “fair use” spectrum. This is uncharted territory for any sort of fair use doctrine as written.
In all three of your transformation examples, you likely would’ve paid the NYT either indirectly via ads or directly via a subscription (contentious ad-blocking aside). And Mazda, GM, and VW surely generated far more value from the car designer in revenue than the designer got in salary and knowledge. Broadly, humans pay something (tuition, apprenticeship, etc) to learn knowledge and skills. Even schools and libraries are not free, though they may feel so at time of use. _Some_ free learning is beginning to emerge on the internet, but much of it is as upsell to paid learning.
OpenAI on the other hand has been purely extractive in its learning relationships. A single human contravening normal learning costs would go unnoticed but also would not scale their own downstream impact. But an organized large group doing so would certainly run into issues (10k interlopers couldn’t just sneak into a single college course, for instance). And I think a large group of aligned/associated people is more akin to AI than a single person is, given the differences in scalability.
And that's seems to be the problem. Apparently ChatGPT was spitting out entire copies of NYT articles without attribution and falsely attributing things to NYT it had made up.
It'll benefit everyone if the fair use question is decided definitively sooner than later. I could see OpenAI settling, but it's in NYT's interest not to since it's not just OpenAI, they're just the most egregious ("we can use your copyrighted works for our model but you can't use our uncopyrightable output for your model").
(IANAL)
It seems like the lawsuit here is talking about specific NYT related functionality, like retrieving the text in new articles. That essentially has nothing to do with training and running a foundation LLM, it's about some specific shenanigans with NYT content, and it's legal status would appear to have nothing to do with whether training is fair use.
This is akin to having to explain to non-technical lawyers and judges how crypto works. In the FTX case it became irrelevant because you can just nail them on fraud for using deposited funds for non-allowed reasons.
> Synthetic search applications built on the GPT LLMs, including Bing Chat and Browse with Bing for ChatGPT, display extensive excerpts or paraphrases of the contents of search results, including Times content, that may not have been included in the model’s training set. The “grounding” technique employed by these products includes receiving a prompt from a user, copying Times content relating to the prompt from the internet, providing the prompt together with the copied Times content as additional context for the LLM, and having the LLM stitch together paraphrases or quotes from the copied Times content to create natural-language substitutes that serve the same informative purpose as the original. In some cases, Defendants’ models simply spit out several paragraphs of The Times’s articles.
https://www.courtlistener.com/docket/68117049/1/the-new-york...
It's a separate issue from the fact that the model can regurgitate it's NYT training data.
There's a section on page 63 which helps clarify that:
Defendants materially contributed to and directly assisted
with the direct infringement perpetrated by end-users of the
GPT-based products by way of: (i) jointly-developing LLM
models capable of distributing unlicensed copies of Times
Works to end-users; (ii) building and training the GPT LLMs
using Times Works; and (iii) deciding what content is
actually outputted by the GenAI products, such as grounding
output in Times Works through retrieval augmented generation,
fine-tuning the models for desired outcomes, and/or
selecting and weighting the parameters of the GPT LLMs.
So if ChatGPT didn't refer to it verbatim and if ChatGPT trained on it and mixed it with other content, NYT would be OK with that? Tbh I don't get it.
Edit: I found this in my bookmarks archive - https://news.slashdot.org/story/23/08/15/2242238/nyt-prohibi...
Also this: https://www.cnbc.com/2023/10/18/universal-music-sues-anthrop...
I think also the courts will also side with NYT. Very recently there was a copyright case involving Andy Warhol [1] which he lost. Despite the artwork being visually transformative; it's use was not transformative. So, to me that means if you create a program using NYT's materials that is used as a replacement for NYT it will not count as fair use. Obviously you could just do what say Google does and fork money over to NYT for some license.
However, my initial point is that this is a tangent. NYT has claimed that OpenAI is using NYT's works at least as-is and so OpenAI can just be nailed for that. Which is my point about FTX; it's irrelevant if their exchange was legal since you can just nail them for mis-use of customer funds. Another example would be Al Capone; it doesn't matter if he's a mobster because you can nail him for tax evasion.
[1]: https://www.cbsnews.com/news/andy-warhol-supreme-court-princ...
But broadly speaking this is also the question of the "Open Web" and will it survive or not. Walled gardens like Facebook, Instagram etc. are strong and pervasive but still majority of people use and acknowledge publicly open websites from the Open Web. If AI chat bots do not drive traffic to websites then they are walled gardens and Microsoft, Google or whoever will lock users in and try to squeeze them for money.
A key argument the NYT is making is that part of the definition of fair use is not producing a product that competes with the original.
They argue that ChatGPT et al DO compete with the original, in a way that harms the NYT business model.
One example they give: ChatGPT can reproduce recommendations made by the Wirecutter, without including the affiliate links that form the Wirecutter's main source of revenue - page 48 of https://nytco-assets.nytimes.com/2023/12/NYT_Complaint_Dec20...
Notably, in both those cases, the user is specifically asking for what wirecutter thinks.
To me, that makes the infringing behavior clearly the fault of the user of the tool, not the tool itself.
When I saw this lawsuit announced, I assumed that large portions content were being reproduced in response to generic queries. That isn't the case, every example I've seen from this lawsuit has prompts where the user specifically asks for the content. To me, any fault and liability rests on the user here.
The article also mentions the idea that the model is merely extracting (uncopyrightable) facts, which is interesting, but might be a tough one to prove since LLMs have no way of establishing what are facts, and don't return facts by design.
In this case it's all about the technicalities.
Big Tech could also retaliate by making NYTimes conveniently disappear from search/news indexing
https://www.nytimes.com/2023/12/22/technology/apple-ai-news-...
Ideally a marketplace would develop that can price each content fairly.
To me, this leads to another question: is it possible to get a GPT-4 quality model using only training data from major content providers?
In other words, what if OpenAI didn’t crawl the entire internet and only used data from Facebook, Twitter, Wikipedia, GitHub, NYT, Stackoverflow, and [list 100 more well known high quality data sources here]
If it’s possible to get to GPT-4 model quality with training data from a couple hundred centralized sources, then maybe it won’t have any impact at all (other than OpenAI needing to pay other companies to use their data for training data)
Or, if the long tail “every website on the internet” really is the secret to training LLM models, then that would be much more problematic.
If it is found to be breach of copyright then the berne convention comes into play which makes it hard(er) for international offerings to exist.
Licensing the most prominent IP holders will get the vast majority of data secured.
These billion-dollar-funded efforts can comfortably chase down, say, hundreds of deals.
GPT-4 finished training g 18mo ago…plenty of time to clean up this mess, if there had been a will
I do think it’ll be interesting to see how our militaries handle the issues. Considering that is probably the one area our governments don’t want to be “behind”.
Probably none. They can just open a subsidiary there and siphon the same data to train their models.
2. Open door for everyone else whose content has been copied to demand payment.
3. Japan aside*, I'm not sure how a decisive victory for NYT fails to affect companies in other countries. Other countries will certainly take a US opinion into account. Furthermore, nothing whatsoever would prevent NYT from suing for infringement in France, Germany, etc.
A number of European countries are _much_ more comfortable with compulsory licensing than the US is.** I will not be surprised at all if Germany, Denmark, Sweden etc impose an AI-training license that companies have to pay to train or even use AI models. Could be based on employees, users, revenue, etc. They've been doing this for decades for other forms of mechanical reproduction, so it seems a reasonable path forward.
* I've seen reports that Japan has declared that it will not enforce copyright against organizations for training models on copyright-protected works. I don't know whether this means that _everything_ is fair game in Japan-- it could mean that training won't be enforced, but commercial use of models trained on protected works could be. I think it's somewhat harder to argue against training a model than it is against leveraging a trained model for commercial purposes.
** AFAIK, the only use for compulsory licensing in the US is musical performance.
4. "Surely one can't get licenses"... collective licensing would be a path forward for which there's a little more history in the US than there is for compulsory licensing. In a collective licensing model, OpenAI/Microsoft/Company X would be able to purchase a license priced by revenue/employees/users etc, and funds would be distributed to participating copyright holders.
For that to work, you need some kind of agency that has a ton of significant copyright holders. If some agency were able to get to a critical mass , that could happen without Congressional action.
Congress could also step into the problem in a couple of ways. They could wave a wand and make the problem go away for OpenAI/Microsoft/everyone else, or they could explicitly require some kind of license. I'd be shocked if Congress handled this productively _at all_; these issues are incredibly fraught in the best of times, Congress has been terrible for years at any kind of precise technical work, and the current Congress is uniquely incompetent.
I expect that if Congress accomplishes anything on this topic in the next couple of years, it will turn out to be awful for everyone.
AI is only useful in so far as it's grounded in reality. If you ask it how tall the empire state building is, the answer has to be in some way derived from how tall it really is. Even creative writing is only useful in so far as how it's grounded in real life human experience. This is all to say that a bunch of people need to keep writing stuff about the real world for models to keep up with the real world.
I'd say the ideal scenario for US AI development is:
1) People keep making high quality data grounded in real world USA
2) That data is easily accessible for research purposes
3) That data is usable for commercial purposes (but might require more work than for research purposes)
A ruling for NYT here might keep good data more widely produced and available. Research legality is almost surely not going to be affected, so keeping data produced and available is key to keeping domestic research going strong and keeping talent here.
A ruling against NYT might mean all the new grounded-in-reality data goes behind closed doors. And even though the legality of research isn't in question, that would make the research harder. It would be easier to commercially use what you can find, but it could be garbage-in-garbage-out in the sense of quality data becoming hidden.
It's not even down to the NYT and such: Think facebook, or reddit, suddenly being able to charge others for their massive amount of data, while they force anyone posting there to make their content usable for their own training to post there at all. Imagine a world where, say, Microsoft buys the times, just because it's better for them to not just have the training data, but to tell every competitor to get bent if they want a license. 'Google buys Disney for the training data'. If AI is that useful, and we make copyright that strong, what we might see is ailing media companies being bought out by tech.
Ultimately IP law is about the tradeoff of more original content and more usefulness for the world, and AI massively changes where the center of balance is. So I don't expect a ruling for the NYT to remain the law of the land for very long, just due to economic pressures.
Ultimately it doesn't matter which side people like aesthetically: If an AI trained with no regard for copyright is that much more useful than one that quotes every source and pays it some money, then piracy will ensure. If it doesn't really help, then copyright will keep being very strong.
It's not even a matter of countries, but hackers. Would Aaron Swartz, working on his own LLM, pay for copyright dues or not? Because there's enough people in tech with that mindset that we'll end up with LLMs trained by a group that thinks like he did.
We will have laws, people in places that aren't subjected to our laws, and pirates. It's now it always works.
It's doubtful that the courts are going to accomplish anything so broad. And the media is unlikely to accomplish anything so nuanced.
“Babies learn from their environment why not GPT?”
“Human teachers remix stuff for their lessons all the time, why can’t GPT?”
Computers are not humans.
Computers are not humans.
Computers are not humans.
Good grief.
No, corporations are not people. They have some of the rights a person has, but not all. For example, from [1], "it is firmly established that a corporation has no fifth amendment protection against self-incrimination".
[1] https://law.justia.com/cases/federal/appellate-courts/F2/515...
I think that it's sad that the leading AI labs seem to mostly be run by people in the former camp. It doesn't have to be this way.
Kant's arguments would apply just as much to intelligent, self conscious machines as to humans; nowhere does he base his conclusions on something as dumb as "because we're biological".
Right now though, we should put humans, and animals for that matter, before anything else.
And? Why does that invalidate either of your two examples? You need to answer "why not GPT?" with something more substantial than "because it's not a human."
ChatGPT a commercial software product that generates wealth for its owners, so allowing commercial products to steal and promote others' content on their own is an agreed-upon violation of rights.
Let's look at an example: Google doesn't get to index and show New York Times content directly in its search results.
If you think of ChatGPT more like Google maybe that will help you keep it straight.
And Re: Google. I agree. Which is why the case law around Google books showing full excerpts is important to look at.
This is HN, lots of people here see LLMs the same way miners saw the gold rush. When you deal with people dreams of richness, you can be pretty sure they would move us all right into a skynet situation, rationalizing every single fucking step towards our demise.
A computer that can reason and is self aware like a human is _morally equivalent_ to a human unless your morality is based on some kind of human supremist philosophy. And I imagine in future human supremacists are going to be regarded the same way white supremacists are regarded currently.
This lack of clarity is emblematic of how legal mechanisms operate. Loosely speaking, in comparison with computational and philosophical approaches, the law is quite lazy at drawing distinctions. Disagreements get deferred using a pre-determined legal process. One might say the lazy-approach of the law is "by design". I'm not sure; I'd need to dig into the history a lot more to assess.
https://nytco-assets.nytimes.com/2023/12/NYT_Complaint_Dec20...
The first 4 pages lay out the high level complaint very clearly, but if found most of the rest of the document (skip the bit about jurisdictions) was clearly written and easy for my non-lawyer brain to understand too.
I feel like it is clearly a copyright issue if someone, through whatever means, posted New York Times articles on the internet for 1000s to potentially see and find through search.
If someone through some other interface provides 1000s of people independently through private channels a New York Times article verbatim on ask isn’t this similar?
In both cases it requires users to seek it out. In both cases copyright protected content is openly available to 1000s of people. Just because you generate your stock of copied material 1 by 1 on an order by order basis doesn’t feel materially different in effect on the copyright owner. The interface owners are willing to distribute copyright protected content 1000s of times on request for it.
In any case, AI is a tool, not capable of reasoning. As such, the end user is responsible for ensure all applicable laws are followed. This is always the case. You wouldn’t sue Microsoft for copy & paste would we? It lets you copy people’s work.
Imo the NYT case is pretty damn weak, because derived works are fair game. I can even read their article in full and provide comments on it, and it’s legal (provided the objective isn’t to share the copyrighted work)
Perhaps OAI have already taken action to disable ChatGPT's ability to quote in full without attribution, if it was ever possible in the first place(?) Plaintiff doesn't disclose exactly which prompts they used to give rise to their infringement claims, which seems suspect.
If the case goes to trial, and OAI has taken steps to mitigate the harm - i.e., it no longer reproduces NYT content word-for-word - does that significantly reduce the damages they could seek?
Imagine we have a model but don't train it with the copyrighted data. But we train if with every review ever made public about it.
If we have a large number of reviews the model will be able to give pretty good responses about the topic and may even be able to reproduce some part of it.
Is this copyright infringment?
And what if we add another layer? Now we just train the model with the comments people leave about the reviews.
Those reviews are also copyrighted works.
The generated statements may be inaccurate but they cannot be called lies. To do would be a dangerous level of anthropomorphization in the legal sphere.
You don’t have to worry about the legal sphere anthropomorphising software. Software is legally not a person and therefore will not be treated as one by the courts, regardless of how people commonly refer to it.
We've long had precedent with search engines. But despite that, these cases are revisiting the way copyright might work.
These cases are not as simple as people like you imply no matter how many times you repeat the utterly pointless and irrelevant fact that computers are not humans.
This is a lawyer writing about a lawsuit on a law office blog. That's what's worrisome.
Generally when people remember wrong it's considered a mistake, not a lie. Lies imply an intent to deceive.
LLMs have no sense for what is correct or not, therefor it cannot tell the truth or lie. It always hallucinates - sometimes those hallucinations match our factual understanding.
"Fabrication" is a much more accurate and neutral word. I wish "hallucination" hadn't taken hold.
Good luck suing a nonprofit open-source LLM for this.
Also, the linked article uses the phrase "very strong case" while the subject here uses "strong case"; given that other lawsuits in this space have already been challenged (https://www.hollywoodreporter.com/business/business-news/sar...), isn't the "strength" of the case very subjective?
What news specific services are they talking about?
> DMCA Section 1202 violations by all defendants regarding removal of copyright management information from items in the datasets
Aside from the obvious fact that OpenAI doesn't want "© 2023 The New York Times" appearing anywhere in their output, I'm sure it's also removed because LLMs don't adapt well to metadata and can't reliably correlate metadata to the exact text it's attached to, treating it instead as input text itself. One imagines a first round of training where this metadata was retained and every response from the LLM came back with a perfunctory hallucinated copyright from another publisher.
For example, if you were to hire a group of writers, ghost writers to be specific, to re-create someone else’s work, then publish it yourself as your own who is at fault? Who gets sued? Is it you who directed the writers to do the work or is it the writers who did the work?
So if we accept this model, the owner/onus is on the prompter to not re-create copyrighted works.
(Albeit an assumption that the writers are unaware to some extent)
Anyway, we tend to forget that there are countries outside USA who can make laws too. And they will do. What if, one country happily says training content for LLMs is not copyright violation (like Japan was rumored to do)? That is an answer I am more interested in finding out. Any country that passes that law would see massive investment (esp if other countries deem it a violation). That would just mean tech + investments + innovation going out of USA. A recent trend here is approach of France towards regulating AI in common EU laws[1].
[1]https://www.politico.eu/article/thierry-breton-mistral-battl...
What is this referring to? AFAIK reporting on real time news is beyond the capability of an LLM, and a Google search doesn't turn up any news reporting feature.
> But the hallucinations aren’t facts; they’re lies. And even if the defendants prevail in arguing that the AIs are mostly just providing people with unprotectable facts, there’s very little to shield them from liability for the lies, both with respect to trademark dilution claims, but also with respect to potential libel or privacy-related claims that might be brought by other individuals.
This seems really weak because libel requires damages. It would be incredibly hard for the NYT to prove any damages from ChatGPT lying about it. Also libel requires "actual malice" which means the libeler needs to know that their statements are false. Good luck showing that with an LLM.
Creators of LLMs (OpenAI in this case) should know that their LLMs regularly provide false statements.
Of course I'm not actually arguing this, and have no horses in this race. But not hard to imagine that someone could make this argument and also believe it.
Isn't that easily proven by the text at the bottom of chatgpt: "ChatGPT can make mistakes. Consider checking important information."
If I wrote 10 fake headlines, I might be protected. If I copied 9 from NYT and then made one up, it's a lot harder for me to argue I didn't intend to deceive.
That they're not part of the main GPT-4 model itself is true, but the lawsuit is against OpenAI and Microsoft, not vs. GPT-4.
That's only the first half of the or conditional. The second half is "with reckless disregard of whether it was false or not." That's very different, and I'd think the model and it's creators not caring if the output is true but marketing it as usually true is having a reckless disregard to the truth of it's outputs. There's no checking if it's real.
And then in a few years, as AI improves, the NYT relies on AI to write some of their articles (or they rely on AI to proofread and edit their articles).
Can the Times' current arguments be used against them using AI later?
Discussion on HN (86 comments; original submission title: 'A business model based on mass copyright infringement'): <https://news.ycombinator.com/item?id=38784194>
“What sources of data are used for training OpenAI models?
OpenAI uses data from different places including public sources, licensed third-party data, and information created by human reviewers.”
Is the NY Times data in one of those categories?
However, any person has the right to file appear "pro se". And if the legal filings and arguments by NYT are public, then why can't I just file my own case. And you. And you. And you. And you. And you. You get the picture.
We have this situation where corporations can exploit situations and people with impunity. They can buy off the government - oh wait, we have to call that "lobbying". Lawyers won't take on the case unless it is a class action worth billions to them. In which case you and I get a piddling amount or none. See https://news.ycombinator.com/item?id=38901484. (the question being whether Apple settled in order to avoid a finding that their action was purposeful).
All we need is to use the information from the NYT legal case. <humor?> And hey maybe someone can use OpenAI to generate the filings?</end questionable humor>
aka "Ancient mammoth sinking in tar pit throws one last fit before facing full-on extinction".
When will programmers speak out against their code being used for something they never consented to?
I'm glad journalists are leading the charge against unfair AI, and I wish programmers were raising their voices also.
They acknowledge its usefulness and are demanding to be compensated for the billions of dollars they spent producing articles that trained ChatGPT.
News organisations are probably waiting for the NYT to win the case and then similar claims and damages from others could follow; not just from the NYT.
The key claim is this:
> Vicarious copyright infringement (the idea that Microsoft and various OpenAI affiliates directed, controlled and profited from infringement committed by OpenAI OpCo LLC and OpenAI, LLC)
OpenAI knew they needed a license to train their DALL-E AI model from shutterstock, instead of doing so with news organizations, they decided to trample all over it and profit off of copyrighted articles, without permission.
The end result of this is a settlement and licensing deal, a similar likely result for Getty v. Stability.
I'm quite bearish on AI in general, but the whole copyright argument is nonsense to me. How is it any different than a human learning from public material in their field? My understanding is that you are not distributing copyrighted material verbatim, but a set of weights that were trained on copyrighted material. Just like how a writer reads a lot of other writers and becomes a better writer.
If we applied this same standard to human authors, couldn't we say that every narrative is actually the intellectual property of some unnamed hunter-gatherer who described how he killed an antelope to a captive audience?
It's okay for me to take existing artwork and use it as reference to make the artwork I want to make. I can make software to help me do that, like photoshop. But I can't make an AI tool to help me do that. Why? What is the actual difference?
Assuming there is no plagiarism, of course.
Great, so all those paywalls that use verbatim subjects and early texts as a teaser are now broken! Well, for all old articles that the LLM was trained on, at least, I guess. Very simple countermeasure is to simply use AI to paraphrase the subject and article (paradoxically) until the person pays for the privilege of reading human-authored text.
I'm sorry but this is a very weak argument. For example, I don't even believe normal users have access to the GPT4 system prompt unless they use the API directly (and possibly not even then, I'd have to check).
We paraphrase all the time to avoid plagiarism and that's just somewhat randomized retelling of the same idea.
If you set the temperature to 0 in an LLM it's basically in "decompress/rote mode". I don't think this is qualitatively the same as "copying", possibly more akin to "memorization". I haven't seen very many demonstrations of verbatim-copy output that wasn't done with a temperature of or near 0.
https://www.scribbr.com/frequently-asked-questions/is-paraph...