this post was submitted on 13 Jul 2023
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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.
@IncognitoErgoSum Gonna need a source on Large Language Models using neural networks here.
I'm willing to, but if I take the time to do that, are you going to listen to my answer, or just dismiss everything I say and go back to thinking what you want to think?
Also, a couple of preliminary questions to help me explain things:
What's your level of familiarity with the source material? How much experience do you have writing or modifying code that deals with neural networks? My own familiarity lies mostly with PyTorch. Do you use that or something else? If you don't have any direct familiarity with programming with neural networks, do you have enough of a familiarity with them to at least know what some of those boxes mean, or do I need to explain them all?
@IncognitoErgoSum I don't think you can. Because THIS? Is not a model of how humans learn language. It's a model of how a computer learns to write sentences.
If what you're going to give me is an oversimplified analogy that puts too much faith in what AI devs are trying to sell and not enough faith in what a human brain is doing, then don't bother because I will dismiss it as a fairy tale.
But, if you have an answer that actually, genuinely proves that this neural network is operating similarly to how the human brain does... then you have invalidated your original post. Because if it really is thinking like a human, NO ONE should own it.
In either case, it's probably not worth your time.
I'm curious, how do you feel about global warming? Do you pick and choose the scientists you listen to? You know that the people who develop these AIs are computer scientists and researchers, right?
If you're a global warming denier, at least you're consistent. But if out of one side of you're mouth you're calling what AI researchers talk about a "fairy tail", and out of the other side of your mouth you're criticizing other people for ignoring science when it suits them, then maybe you need to take time for introspection.
You can stop reading here. The rest of this is for people who are actually curious, and you've clearly made up your mind. Until you've actually learned a bit about how they actually work, though, you have absolutely no business opining about how policies ought to apply to them, because your views are rooted in misconceptions.
In any case, curious folks, I'm sure there are fancy flowcharts around about how data flows through the human brain as well. The human brain is arranged in groups of neurons that feed back into each other, where as an AI neural network is arranged in more ordered layers. There structure isn't precisely the same. Notably, an AI (at least, as they are commonly structured right now) doesn't experience "time" per se, because once it's been trained its neural connections don't change anymore. As it turns out, consciousness isn't necessary for learning and reasoning as the parent comment seems to think.
Human brains and neural networks are similar in the way that I explained in my original comment -- neither of them store a database, neither of them do statistical analysis or take averages, and both learn concepts by making modifications to their neural connections (a human does this all the time, whereas an AI does this only while it's being trained). The actual neural network in the above diagram that OP googled and pasted in here lives in the "feed forward" boxes. That's where the actual reasoning and learning is being done. As this particular diagram is a diagram of the entire system and not a diagram of the layers of the feed-forward network, it's not even the right diagram to be comparing to the human brain (although again, the structures wouldn't match up exactly).
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.
I said it was a neural network.
You said it wasn't.
I asked you for a link.
You told me to do your homework for you.
I did your homework. Your homework says it's a neural network. I suggest you read it, since I took the time to find it for you.
Anyone who knows the first thing about neural networks knows that, yes, artificial neurons are simulated with matrix multiplications, why is why people use GPUs to do them. The simulations are not down to the molecule becuase they don't need to be. The individual neurons are relatively simple math, but when you get into billions of something, you don't need extreme complexity for new properties to emerge (in fact, the whole idea of emergent properties is that they arise from collections of simple things, like the rules of the Game of Life, for instance, which are far simpler than simulated neurons). Nothing about this makes me wrong about what I'm talking about for the purposes of copyright. Neural networks store concepts. They don't archive copies of data.