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this post was submitted on 13 Jul 2023
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I would tend to agree with you on this one, although we don't need bad copyright legislation to deal with it, since laws can deal with it more directly. I would personally put in place an organization that requires rigorous proof that AI in those roles is significantly safer than a human, like the FDA does for medication.
Corporations would love if regular people were only allowed to train their AIs on things that are 75 years out of date. Creative interpretations of copyright law aren't going to stop billion- and trillion-dollar companies from licensing things to train AI on, either by paying a tiny percentage of their war chests or just ignoring the law altogether the way Meta always does, and getting a customary slap on the wrist. What will end up happening is that Meta, Alphabet, Microsoft, Elon Musk and his companies, government organizations, etc. will all have access to AIs that know current, useful, and relevant things, and the rest of us will not, or we'll have to pay monthly for the privilege of access to a limited version of that knowledge, further enriching those groups.
Let's talk about Stable Diffusion for a moment. Stable Diffusion models can be compressed down to about 2 gigabytes and still produce art. Stable Diffusion was trained on 5 billion images and finetuned on a subset of 600 million images, which means that the average image contributes 2B/600M, or a little bit over three bytes, to the final dataset. With the exception of a few mostly public domain images that appeared in the dataset hundreds of times, Stable Diffusion learned broad concepts from large numbers of images, similarly to how a human artist would learn art concepts. If people need permission to learn a teeny bit of information from each image (3 bytes of information isn't copyrightable, btw), then artists should have to get permission for every single image they put on their mood boards or use for inspiration, because they're taking orders of magnitude more than three bytes of information from each image they use for inspiration on a given work.
Except an AI is not taking inspiration, it's compiling information to determine mathematical averages.
A human can be inspired because they are a human being. A Large Language Model cannot. Stable Diffusion is not near the complexity of a human brain. Just because it does it faster doesn't mean it's doing it the same way. Human beings have free will and a host of human rights. A human being is paid for the work they do, an AI program's creator is paid for the work it did. And if that creator used copyrighted work, then he should be having to get permission to use it, because he's profitting off this AI program.
I would too, but we need TIME to get that done and right now, lawsuits will buy us time. That was the point of my comment.
The AIs we're talking about are neural networks. They don't do statistics, they don't have databases, and they don't take mathematical averages. They simulate neurons, and their ability to learn concepts is emergent from that, the same way the human brain is. Nothing about an artificial neuron ever takes an average of anything, reads any database, or does any statistical calculations. If an artificial neural network can be said to be doing those things, then so is the human brain.
There is nothing magical about how human neurons work. Researchers are already growing small networks out of animal neurons and using them the same way that we use artificial neural networks.
There are a lot of "how AI works" articles in there that put things in layman's terms (and use phrases like "statistical analysis" and "mathematical averages", and unfortunately people (including many very smart people) extrapolate from the incorrect information in those articles and end up making bad assumptions about how AI actually works.
If an artist uses a copyrighted work on their mood board or as inspiration, then they should pay for that, because they're making a profit from that copyrighted work. Human beings should, as you said, be paid for the work they do. Right? If an artist goes to art school, they should pay all of the artists whose work they learned from, right? If a teacher teaches children in a class, that teacher should be paid a royalty each time those children make use of the knowledge they were taught, right? (I sense a sidetrack -- yes, teachers are horribly underpaid and we desperately need to fix that, so please don't misconstrue that previous sentence.)
There's a reason we don't copyright facts, styles, and concepts.
Oh, and if you want to talk about something that stores an actual database of scraped data, makes mathematical and statistical inferences, and reproduces things exactly, look no further than Google. It's already been determined in court that what Google does is fair use.
This is not at all accurate. Yes, there are very immature neural simulation systems that are being prototyped but that's not what you're seeing in the news today. What the public is witnessing is fundamentally based on vector mathematics. It's pure math and there is nothing at all emergent about it.
That's not how copyright works, nor should it. Anyone who creates a mood board from a blank slate is using their learned experience, most of which they gathered from other works. If you were to write a book analyzing movies, for example, you shouldn't have to pay the copyright for all those movies. You can make a YouTube video right now with a few short clips from a movie or quotes from a book and you're not violating copyright. You're just not allowed to make a largely derivative work.
So to clarify, are you making the claim that nothing that's simulated with vector mathematics can have emergent properties? And that AIs like GPT and Stable Diffusion don't contain simulated neurons?
Yes, and the math is all publicly documented.
Oh boy! Link, please!
No, I'm not your Google. You can easily read the background of Stable Diffusion and see it's based on Markov chains.
LOL, I love kbin's public downvote records. I quoted a bunch of different sources demonstrating that you're wrong, and rather than own up to it and apologize for preaching from atop Mt. Dunning-Kruger, you downvoted me and ran off.
I advise you to step out of whatever echo chamber you've holed yourself up in and learn a bit about AI before opining on it further.
My last response didn’t post for some reason. The mistake you’re making is that a neural network is not a neural simulation. It’s relatively simple math, just on a very large scale. I think you mentioned earlier, for example, you played with PyTorch. You should then know that NN stack is based on vector math. You’re making assumptions based on terminology but when you read deeper you’ll see what I mean.