Because it's important to grasp the scale of these copyright violations:

* They downloaded, and admitted to using, Anna's Archive: Millions of books and papers, most of which are paywalled but they pirated it instead

* They acquired Movies and TV shows and used unofficial subtitles distributed by websites such as OpenSubtitles, which are typically used for pirated media. Official releases such as DVDs tend to have official subtitles that don't sign off with "For study/research purpose only. Please delete after 48 hours" or "Subtitles by %some_username%"

By comparison, someone here brought up that it might be transformative fair use to write a play heavily based on Blood Meridian, but you still need to buy a copy of the book. It would still be infringement to pirate the e-book for your writing process, even if the end result was legal.

This is the absolute core of the issue. Technical people see law as code, where context can be disregarded and all that matters is specifying the outputs for a given set of inputs.

But law doesn’t work that way, and it should not work that way. Context matters, and it needs to.

If you go down the road of “the law is the law and billion dollar companies working on product should be treated the same as individual consumers”, it follows that individuals should do SEC filings (“either require 10q’s or don’t!”), and surgeons should be jailed (“either prosecute cutting people with knives or don’t!”).

There is a lot to dislike about AI companies, and while I believe that training models is transformative, I don’t believe that maintaining libraries of pirated content is OK just because it’s an ingredient to training.

But insisting that individual piracy to enjoy entertainment without paying must be treated exactly the same as datasets for model training is the absolute weakest possible argument here. The law is not that reductive.

We wish we lived in a world where change was reliably positive for our lives. Often changes are sold that way, but they rarely are.

But when new things introduce dramatic capabilities that former things couldn't match (every chatbot before LLMs), it is as clear of an objective technological advance as has ever happened.

--

Not every technical advance reliably or immediately makes society better.

But whether or when technology improves the human condition is far more likely to be a function of human choices than the bare technology. Outcomes are strongly dependent on the trajectories of who has a technology, when they do, and how they use it. And what would be the realistic (not wished for) outcome of not having or using it.

For instance, even something as corrosive as social media, as it is today, could have existed in strongly constructive forms instead. If society viewed private surveillance, unpermissioned collation across third parties, and weaponizing of dossiers via personalized manipulation of media, increased ad impact and addictive-type responses, as ALL being violations of human rights to privacy and freedom from coercion or manipulation. And worth legally banning.

Ergo, if we want tech to more reliably improve lives, we need to ban obviously perverse human/corporate behaviors and conflicts of interest.

(Not just shade tech. Which despite being a pervasive response, doesn't seem to improve anything.)

Not really, getting 94381294*123=... wrong, but close within the actual answer, cannot be overfitting since it wasn't in the training data.

No it doesn't, for instance some errors would be caused by under fitting. The data could also be correct but your hyperparameters (such as the learning rate or dropout rate) could cause your model to overfit.

> Where do you draw the line between “overfitting to training data” and “incorrect data” ?

There's no need to draw a line between two explanations that aren't mutually exclusive. They can (as in this case) both be true. Overfitting is the symptom; dirty data is the cause.

- This behavior damages the model's performance on out of sample data; every word you predict during silence increases the transcript's Word Error Rate.

- These translation credits are an artifact of our training data, and not a reflection of the process we are modeling (spoken language).

So, while you are correct about the mechanism at work here, it is still correct to call learning a spurious pattern which damages our performance "overfitting".

They [1, and others] been hunting and fining downloaders for over a decade now, with the only "evidence" being IP addresses connected with the torrent [2].

1: https://www.njordlaw.com/filesharing-and-downloading-films/q...

2: https://admin.ovpn.com/en/blog/online-integrity-new-threats-...

If you don't understand how torrents work on technical level I suggest at least some shallow reading. Property rights holders don't care about details, as long as you tick the box of sending a single packet to somebody, off to court with ya.

We shouldn't abandon the line of investigation, however. We should continue thinking of ways to do this until we find one that works well.

There's a chance it ends up being something that requires a judge to interpret each individual case...

There are always some cases on the edge. The question is if saving them is worth the cost of the major players running rampant.

Trial court rulings aren't binding precedent even on the same court in different cases, so its quite possible that different cases at the trial level can reach different conclusions on fair use on fairly similar facts, given the lack of appellate precedent directly on point with AI training.

A single verdict about a specific case (13 authors vs META) does not mean it's legal for companies to steal IP from other companies which has evidently been going on for some years now.

Those other companies have lawyers powerful enough to change jurisdiction in many countries in order to "protect their IP".