Which of the following sounds more reasonable?
-
I shouldn’t have to pay for the content that I use to tune my LLM model and algorithm.
-
We shouldn’t have to pay for the content we use to train and teach an AI.
By calling it AI, the corporations are able to advocate for a position that’s blatantly pro corporate and anti writer/artist, and trick people into supporting it under the guise of a technological development.
I think it’s the same reason the CEO’s of these corporations are clamoring about their own products being doomsday devices: it gives them massive power over crafting regulatory policy, thus letting them make sure it’s favorable to their business interests.
Even more frustrating when you realize, and feel free to correct me if I’m wrong, these new “AI” programs and LLMs aren’t really novel in terms of theoretical approach: the real revolution is the amount of computing power and data to throw at them.
The funniest thing I’ve seen on this is the ChatGPT CEO, Altman, talking about how he’s a bit afraid of what they’ve created and how it needs limitations – and then when the EU begins to look at regulations, he immediately rejects the concept, to the point of threatening to leave the European market. It’s incredibly transparent what they’re doing.
Unfortunately I don’t know enough about the technology to say if the algorithms and concepts themselves are novel, but without a doubt they couldn’t exist without modern computing power capabilities.
It also plays into the hype cycle they’re trying to create. Saying you’ve made an AI is more likely to capture the attention of the masses then saying you have a LLM. Ditto that point for the existential doomerism that they ceo’s have. Saying your tech is so powerful that it might lead to humanity’s extinction does wonders in building hype.
Agreed. And all you really need to do is browse any of the headlines from even respectable news outlets to see how well it’s working. It’s just article after article uncritically parroting whatever claims these CEO’s make at face value at least 50% of the time. It’s mind-numbing.
LLMs are pretty novel. They are made possible by invention of the Transformer model, that operates significantly different compared to, say, RNN.
The fear mongering is pretty ridiculous.
“AI could DESTROY HUMANITY. It’s like the ATOMIC BOMB! Look at it’s RAW POWER!”
AI generates an image of cats playing canasta.
“By God…”
If an LLM was trained on a single page of GPL code or a single piece of CC-BY art, the entire set of model weights and any outputs from the model must be licensed the same way. Otherwise this whole thing is just blatant license laundering.
This depends on how transformative the act of encoding the data in an LLM is. If you have overfitting out the ass and the model can recite its training material verbatim then it’s an illegal copy of the training material. If the model can only output content that would be considered transformative if a human with knowledge of the training data created it, then so is the model.
both sound the same to me IMO. Private companies scraping ostensibly public data to sell it. No matter how you word it they are trying to monetize stuff that is out in the open.
I don’t see why a single human should be able to profit off learning from others but a group of humans doing it for a company cannot. This is just how humanity advances at whatever scale.
I had a comment about the morality of it at first but I pulled it out. This is not an easy question to answer. Corporations gate keeping knowledge seems weird and dystopian but the knowledge is out there and they are just making connections between it. It also touches on copyright and fair use.
I agree it’s much more complicated an issue than most people give it credit.
IMO content created by either AI or LLMs should have a special license and be considered AI public domain (unless they can prove that they own all content the AI was trained on). Commercial content made based on content marked with this license would be subject to a flat % tax that should be applied to the product price which would be earmarked for a fund distributing to human creators (coders, writers, musicians etc.).
What about LLM generated content that was then edited by a human? Surely authors shouldn’t lose copyright over an entire book just because they enlisted the help of LLMs for the first draft.
I’ll note that there are plenty of models out there that aren’t LLMs and that are also being trained on large datasets gathered from public sources.
Image generation models, music generation models, etc.
Heck, it doesn’t even need to be about generation. Music recognition and image recognition models can also be trained on the same sort of datasets, and arguably come with similar IP right questions.It’s definitely a broader topic than just LLMs, and attempting to enumerate exhaustively the flavors of AIs/models/whatever that should be part of this discussion is fairly futile given the fast evolving nature of the field.
Fair enough.
That’s absolutely not correct. AI is a field of computer science/scientific computing built on the idea that some capabilities of biological intelligences could be simulated or even reproduced “in silicon”, i.e. by using computers.
Nowadays is an extremely broad term that covers a lot of computational methodologies. LLM in particular are a evolution of methods born to simulate and act as human neural network. Nowadays they work very differently, but they still provide great insights on how an “artificial” intellicenge can be built. It is only one small corner of what will be a real general artificial intelligence, and a small step in that direction.
AI as a name is absolutely unrelated with how programs based on the methodologies are built.
Human intelligences are in charge of all copyright part. AI and copyright are orthogonal, people are those who cannot tell the 2 and keep talking about AI.
There is AI, and there is copyright, it is time for all of us to properly frame the discussion on “copyright discussion related to <company>'s product”
What I’m getting at moreso is that comparisons to humans for purposes of copyright law (e.g. likening it to students learning in school or reading library books) don’t hold water just because it’s called an AI. I don’t see that as an actual defence for these companies, and it seems to be somewhat prevalent.
You can absolutely compare AI with students. The problem is that, legally, in many western countries students still have to pay copyright holders of the books they use to learn.
It is purely a copyright discussion. How far copyright applies? Shall the law distinguish between human learning and machine learning? Can we retroactively change copyright of material available online?
For instance, copilot is more at risk than a LLM that learned from 4chan, because licenses are clearer there. Problem is that we have no idea on which data big llms were trained, to know if some copyright law already applies.
At the end it is just a legal dispute on companies making money out of AI trained on data publicly available (but not necessarily copyright free).
My argument is that an LLM here is reading the content for different reasons than a student would. The LLM uses it to generate text and answer user queries, for cash. The student uses it to learn their field of study, and then apply it to make money. The difference is that the student internalizes the concepts, while the LLM internalizes the text. If you used a different book that covered the same content, the LLM would generate different output, but the student would learn the same thing.
I know it’s splitting hairs, but I think it’s an important point to consider.
My take is that an LLM algorithm can’t freely consume any copyrighted work, even if it’s been reproduced online with the consent of the author. The company would need the permission of the author for the express purpose of training the AI. If there’s a copyright, it should apply.
You have me thinking though about the student comparison. College students pay to attend lectures on material that can be found online or in their textbooks. Wouldn’t paying for any copyright material be analogous to this?
Students and LLM do the same with data, simply in a different way. LLM can learn more data, student can understand more concepts, logic and context.
And students study to make money.
Both LLMs and students map the data in some internal representation, that is however pretty different, because a biological mind is different from an AI.
Regarding your last paragraph, this is exactly the point. What shall openai and Microsoft pay, as they are making a lot of money out of other people work? Currently it is unclear as openai hasn’t released what data they used, and because copyright laws do not cover generative AI. We need to wait for interpretation of existing laws and for new ones. But it will change soon in the future for sure
I think you are likely right, but it’s more general than just about training costs. The term “AI” carries a ton of baggage, both good and bad.
To some extent, I think we also keep pushing back the boundary of what we consider “intelligence” as we learn more and better understand what we’re creating. I wonder if every future tech generation will continue this cycle until/unless humanity actually does create a general artificial intelligence–every iteration getting slightly closer but still falling short of “true” AI, then being looked at as a disappointment and not worthy of the term anymore. Rinse and repeat.
AI has been a blanket term for Machine Learning, LLMs, Decision Trees and every other form of “intelligence”.
Unfortunately I think that genie is out of the bottle and it’s never going back in.
And it has been the technical term used in academia since the 1950’s. If anyone is surprised by this usage then they have not studied it, only watched movies.
They are 100% AI. It’s an umbrella term. Simple pathing algorithms in games are also AI.
We shouldn’t have to pay for the content we use to train and teach an AI
Wait people think that sounds reasonable?
If we’re unmasking tech, LLM’s right now are also all just Computer Vision models with a lot of more abstraction layers thrown at them. Nothing but fit assessment machine with a ludicrous amount of extra steps.
I am convinced this is all pedantry, and these models are going to become the de facto basis for true AI at some point. It was already weird enough that this type of tech got discovered from the goal of checking if an image has a cat or not.
