Copyright reform is necessary for national security

Blake Lemoine, 2.5 years ago, hired a lawyer to make this exact argument: https://www.businessinsider.com/suspended-google-engineer-sa...

Myself, every time this topic comes up, I point to the fact that the philosophy of mind has 40 different definitions of "consciousness" which makes it really hard for any two people to even be sure they're arguing about the same thing when they argue if any given AI does or doesn't have it.

(Also: They can be "persons" legally without being humans, cf. corporate personhood; and they can have rights independently of either personhood or humanity, cf. animal welfare).

* https://xkcd.com/1053/

Courts operate on provability (and for good reasons).

However, reality is I have used someone else's work and pretended it's my own. Now, ironically, there are cases where the act of masking it can be more time consuming than writing it from scratch. That is still plagiarism, although it might not be provable.

OK, I agree there, I should have written "function" or "module" something similar. Something that takes nontrivial amounts of work and although it is based on some general principles which should not be patentable/copyrightable, their particular implementation is novel/unique enough that it would take nontrivial amounts of work to replicate the functionality without seeing the original.

> is that something I have to attribute you for

Depends how closely you follow my implementation.

If you use my code as the only reference and translate it verbatim (whether manually or using a tool), then you should credit me. If you look at many implementations, form an _understanding_ of the algorithm in general, then write your own implementation based on that understanding, then probably not.

The question is where LLMs stand. They mix enough sources that crediting all of them would be impractical and in practise they end up crediting none. But their proponents (who always call them AI, sometimes even using pronouns like "he" to refer to the models) argue that the models also form an _understanding_ rather than just regurgitating a mix of inputs. And I have to disagree, what I see is an imitation of reasoning/understanding which is sometimes convincing due to how complex statistics are being used inside the models. But they are still just statistical models of existing content and we see that every time somebody releases a new model, HN upvotes it to the top and a few hours later we inevitable see people giving it trivial questions which it fails to answer correctly.

My other two points:

- Even if an ML company made a model that is actually intelligent, the burden of proof should be on them, otherwise or until them, it's just a remix of existing work. BTW this reminds me an interesting comparison is remixes vs cover songs in music.

- Code is famously harder to read than write. If a human takes time to understand a piece of code and reimplement it not verbatim, then he generally does not get ahead by much. An LLM can do this at scale and speed unattainable by humans.

Let's say two products compete (purely on features and quality instead of marketing - for the sake of argument). One is written first, is novel and written fully by humans. The other is written by training a model on the first product's code and using the model to generate the same product, all within hours or days instead of months or years. The other puts in less actual work but gets the same result. It is clearly parasiting on the first, benefiting from their work without giving them credit or compensation.

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Bottom line is copyright is meant to protect authors who invest effort into creating. Whether it succeeds in that can sometimes be questionable. But using an algorithm (even a very complex one) to take a bit of everyone's work and redistribute is for free without crediting or compensating them does not benefit authors.

I hate analogies but if I write banking software and send 0.000000001% of every transaction to my account, none of the individuals thusly affected probably care that much but I am still going to prison.

You could make separate versions of an LLM depending on the license of its output. If it has t produce public domain code, it can only be trained on the public domain. If if has to produce permissive code (without attribution), then it can be trained on the public domain and permissive code. If copyleft, then those two and copyleft (but it does not solve attribution).

> Companies can also buy code for feeding the model after all.

We've come a long way since the time slavery was common (at least in the west) but we still have a class system where rich people can pay a one time fee (buying a company) and extract value in perpetuity from people putting in continuous work while doing nothing of value themselves. This kind of passive income is fundamentally unjust but pervasive enough that people accept it, just like they accepted slavery as a fact of life back then.

Companies have a stronger bargaining position than individuals (one of the reasons beside defense why people form states - to represent a common interest against companies). This is gonna lead to companies (the rich) paying a one time fee, then extracting value forever while the individual has to invest effort into looking for another job. Compensation for buying code can only be fair if it's a percentage of the value generated by that code.

> So beside the injustice you directly experience right now over your own code probably being fed into AI models, do you fear/despise anything more than that from LLMs?

Umm, I think theft on a civilization-level scale is sufficient.

As long as everybody can also benefit from it, I see it as some kind of collective knowledge sharing.

As you stated in the paragraphs before unless the wealth distribution changes, LLMs may lead to an escalating imbalance, unless the models are shared for free as soon as a critical mass of authors is involved, regardless of who owns the assets.