No mention of Gemini in their blog post on sge And their AI principles doc says

We acknowledge that large language models (LLMs) like those that power generative AI in Search have the potential to generate responses that seem to reflect opinions or emotions, since they have been trained on language that people use to reflect the human experience. We intentionally trained the models that power SGE to refrain from reflecting a persona. It is not designed to respond in the first person, for example, and we fine-tuned the model to provide objective, neutral responses that are corroborated with web results.

So a custom model.

When you use (read, view, listen to…) copyrighted material you’re subject to the licensing rules, no matter if it’s free (as in beer) or not.

You’ve got that backwards. Copyright protects the owner’s right to distribution. Reading, viewing, listening to a work is never copyright infringement. Which is to say that making it publicly available is the owner exercising their rights.

This means that quoting more than what’s considered fair use is a violation of the license, for instance. In practice a human would not be able to quote exactly a 1000 words document just on the first read but “AI” can, thus infringing one of the licensing clauses.

Only on very specific circumstances, with some particular coaxing, can you get an AI to do this with certain works that are widely quoted throughout its training data. There may be some very small scale copyright violations that occur here but it’s largely a technical hurdle that will be overcome before long (i.e. wholesale regurgitation isn’t an actual goal of AI technology).

Some licensing on copyrighted material is also explicitly forbidding to use the full content by automated systems (once they were web crawlers for search engines)

Again, copyright doesn’t govern how you’re allowed to view a work. robots.txt is not a legally enforceable license. At best, the website owner may be able to restrict access via computer access abuse laws, but not copyright. And it would be completely irrelevant to the question of whether or not AI can train on non-internet data sets like books, movies, etc.

It wasn’t Gemini, but the AI generated suggestions added to the top of Google search. But that AI was specifically trained to regurgitate and reference direct from websites, in an effort to minimize the amount of hallucinated answers.

Point is that accessing a website with an adblocker has never been considered a copyright violation.

a much stronger one would be to simply note all of the works with a Creative Commons “No Derivatives” license in the training data, since it is hard to argue that the model checkpoint isn’t derived from the training data.

Not really. First of all, creative commons strictly loosens the copyright restrictions on a work. The strongest license is actually no explicit license i.e. “All Rights Reserved.” No derivatives is already included under full, default, copyright.

Second, derivative has a pretty strict legal definition. It’s not enough to say that the derived work was created using a protected work, or even that the derived work couldn’t exist without the protected work. Some examples: create a word cloud of your favorite book, analyze the tone of news article to help you trade stocks, produce an image containing the most prominent color in every frame of a movie, or create a search index of the words found on all websites on the internet. All of that is absolutely allowed under even the strictest of copyright protections.

Statistical analysis of copyrighted materials, as in training AI, easily clears that same bar.

We’re not just doing this for the money.

We’re doing it for a shitload of money!

They do, though. They purchase data sets from people with licenses, use open source data sets, and/or scrape publicly available data themselves. Worst case they could download pirated data sets, but that’s copyright infringement committed by the entity distributing the data without the legal authority.

Beyond that, copyright doesn’t protect the work from being used to create something else, as long as you’re not distributing significant portions of it. Movie and book reviewers won that legal battle long ago.

The examples they provided were for very widely distributed stories (i.e. present in the data set many times over). The prompts they used were not provided. How many times they had to prompt was not provided. Their results are very difficult to reproduce, if not impossible, especially on newer models.

I mean, sure, it happens. But it’s not a generalizable problem. You’re not going to get it to regurgitate your Lemmy comment, even if they’ve trained on it. You can’t just go and ask it to write Harry Potter and the goblet of fire for you. It’s not the intended purpose of this technology. I expect it’ll largely be a solved problem in 5-10 years, if not sooner.

I mean you do. All the time. We all do. You’re allowed to use them, you’re just not allowed to copy them. It’s in the name you know, copy right.

I know. But we’re both talking about the same thing. Everyone gets irrelevant and ostensibly novel ads all the time. Cat litter, beauty products, diapers, whatever. They just so happen to have focused their attention on cat litter when they just as easily could have focused on dozens of other products and noticed the same result. And, in truth, it’s unlikely that they are actually novel, just unnoticed before.

I’d be incredibly skeptical of the claim that they’ve never been served a cat litter ad. Everybody gets served ads that are misses. They’re obviously easy to ignore which makes it difficult to recall what they were about. But I have no doubt that they would’ve been served cat-related ads plenty of times before. Cats are, after all, one of the most common pets.

Because that’s not what copyright is for. It exists to give the creator exclusive rights over distribution. That’s it. So unless the company is planning to distribute the work and they obtained a copy willingly and legally distributed to them, then copyright is the wrong law to lean on.

I mean, it’s in the name. The right to make copies. Not to be glib, but it really is

A copyright is a type of intellectual property that gives its owner the exclusive legal right to copy, distribute, adapt, display, and perform a creative work, usually for a limited time.

You may notice a conspicuous absence of control over how a copied work is used, short of distributing it. You can reencode it, compress it, decompress it, make a word cloud, statistically analyze its tone, anything you want as long as you’re not redistributing the work or an adaptation (which has a pretty limited meaning as well). “Personal use” and “fair use” are stipulations that weaken a copyright owner’s control over the work, not giving them new rights above and beyond copyright. And that’s a great thing. You get to do whatever you want with the things you own.

You don’t have a right to other people’s work. That’s what copyright enables. But that’s beside the point. The owner doesn’t get to say what you use a work for that they’ve distributed to you.

Training literally is consuming. A copyright license doesn’t get to dictate what computer programs the work is allowed to be used with. There’s a ton a entertainment mega corps that would love for that to be the case, though.

You’re saying that you’re not allowed to do a statistical analysis on a copyrighted work. It’s nonsense. It’s well-established that copyright does not prevent that kind of use.

Copyright licensing allows the owner to control how a work is distributed, not how it’s consumed. “Personal use” just means that you can’t turn around and redistribute a work that you’ve obtained. Not that you’re not allowed to consume it in a corporate setting.

C is just a work around for B and the fact that the technology has no way to identify and overcome harmful biases in its data set and model. This kind of behind the scenes prompt engineering isn’t even unique to diversifying image output, either. It’s a necessity to creating a product that is usable by the general consumer, at least until the technology evolves enough that it can incorporate those lessons directly into the model.

And so my point is, there’s a boatload of problems that stem from the fact that this is early technology and the solutions to those problems haven’t been fully developed yet. But while we are rightfully not upset that the system doesn’t understand that lettuce doesn’t go on the bottom of a burger, we’re for some reason wildly upset that it tries to give our fantasy quasi-historical figures darker skin.

I mean “taking pictures of people who are smiling” is definitely a bias in our culture. How we collectively choose to record information is part of how we encode human biases.

I get what you’re saying in specific circumstances. Sure, a dataset that is built from a single source doesn’t make its biases universal. But these models were trained on a very wide range of sources. Wide enough to cover much of the data we’ve built a culture around.

This is the thing. There’s an incredible number of inaccuracies in the picture, several of which flat out ignore the request in the prompt, and we laugh it off. But the AI makes his skin a little bit darker? Write the Washington Post! Historical accuracy! Outrage!

DVDs (how many people even still own a player?) are not a real alternative to streaming for a number of reasons. Nor is “just watch something else on another platform.” Or, at least, if your claim is that entertainment is interchangeable then you’ve got no real complaint about YouTube. Hell, YouTube has its own ad-free subscription. By your own logic, the ads can’t be enshittificantion because you can just pay more to avoid it!

The enshittification of Netflix goes beyond just charging more. It’s any decision the company makes to make the user experience worse so they can make more money. That’s things like hiding your list and your recently watched shows so they can make you scroll through more recommendations. So then they can autoplay the content they stuck in your way. Recommendations that, like YouTube, are more concerned with what they want to monetize than what you actually want. And it’s restricting the way you used to be able to use the service, like on multiple TVs even within the same house, to get you to wade through a bunch of payment plans.

But my point still stands. Enshittification doesn’t require them to become a monopoly and start producing nothing but reality TV. It just describes the strategy shift that these companies inevitably make from making the platform better to attract more users, to making it worse to extract more money from the user base they’ve built up.

Eh, people have their own tastes in TV. Streaming companies buy exclusive rights to certain content and if that’s where your tastes lie, you’re pretty SOL. It’s about as close to “lock-in” as you can get.

Your definition of enshittificantion is also far too strict. It’s just the shift that companies inevitably make from trying to attract new users quickly by providing a great service, to trying to extract maximum profit by degrading the service quality and cramming in as much revenue generation as they can.