Openai is selling a tool for plagiarism which itself is built upon unlicensed content. It has everything to do with openai.
The exact way copyright laws will apply (or not) to AI is still ambigous, until a high profile case gets before the Supreme Court or ECJ. You can certainly argue one way or another - my view is that AI learning from your blog post is similar to me learning from your blog post. That's not the problem. If I then plagiarise it (or copy wholesale), that is the problem.
You learning from my blog is not the same as a piece of software copying my work. Ai does not “learn” as humans do regardless of how well it mimics our behaviour.
My laptop outputs audio, it doesnt sing.
This is one of the more fascinating aspects of LLMs from a computer science perspective - almost all of human knowledge and experience in text from (several petabytes of data in a database) can be 'cleverly semantically compressed' to fit into a database of just a few hundred GB (NN params). That's some astounding lossy compression algorithm, unfortunately with a less than efficient decoder (temp=0).
However, it isn't always 'plagiarism' either. 'Memorization' vs 'Plagiarism' vs 'understanding' is actually very tricky in LLMs, because having a good world model requires memorizing some facts, as well as some (potentially) "ontological" constructions. In addition, quoting a famous person is very useful for imparting some wisdom occasionally, which is also memorization, and can be seen as plagiarism if the author is left off. But sometimes this occurs (common sayings) and no one bats an eye. One good example of why explicit memorization is really needed is to provide exact references (list of authors, year, and article title) - so clearly making a NN not rewarded for memorizing is not ideal. Hence, *very occasionally*, you get some 'bad memorization'. But it is worth noting that the alignment achieved with these models for such an enormously complex task is quite good.
An llm or generative model doesnt store audio files and digital books, compressed or not. But using algorithms it can reproduce audio and visual content sufficiently similar, but unlike my previous example it’s also made of bites.
Just like an audio file and digital book are not copies of the physical product, an ai “data source” is not an identical or compressed copy of the original bites.
That’s why it can be used to generate output identical to the original data even tho the data it uses is not a verbatim copy.
It’s a new way of storing data, optimised for stochastic generation.
It is a powerful tool but still relies on original content.
They have very little to do with the way humans learn - except maybe if you read a chapter from a book, memorize it, and then proceed to rewrite verbatim with the names of the characters exchanged, a use case of human learning which has long been recognized as, you guessed it, "plagiarism".
The "in-context learning" (regardless of how you feel about the use of that term) that they do from material incorporated into their prompts does not involve incorporating it into their model.
> The production might be original, depending on its specific circumstances, but the model and the service itself are without any legal doubt derivative works of the original.
Model code is a work, but not derivative of the training data. There’s considerable legal doubt that the models weights themselves are works of authorship at all, which is a prerequisite for being derivative works, so no matter if you are referring to model code or model weights, the statement that model is “without any legal doubt” a derivative work of the training data is false.
On that basis alone they are derivative works, regardless of how that data is represented internally.
No, that's not how it works.
> read a chapter from a book, memorize it, and then proceed to rewrite verbatim with the names of the characters exchanged,
That's how a bayesian markov chain would work, not transformers.