There are better ways to make money. It's easy to fall into the debate trap of always summerizing anything against companies as having pure capitalist interests, but trying to solve AI tools is kinda the worst attempt if all intentions are just profit. And the competition in this new field as an industrial component of society demands making sure it is the best product without risking courts taking them down and ruining your entire business.
I do not know what percent of the vast bulk of material sucked up for AI training is copyrighted, but thousands of individual and corporate entities own the rights to a lot of the AI training material. I don't know whether the most valuable part was copyrighted currently, or had been copyrighted in the past, nor how much was just indifferent printed matter. Given the bulk of material required, it seems likely that no distinction was made. — BC
Since the amount of data is key for making the systems accurate and better, it needs to be as much as possible. And since text and images are in larger quantities today than from people who died 70 years ago, it is required to use copyrighted material.
But since it is training data, why does it matter? People seem confused as to what "theft" really means when talking about training data. Memory is formed through "pattern networks" similar to the human brain, nothing is copied into the system. Weights and biases are programmed in to guide the learning and it learns how to recognize pattern structures on all the data. Since it's learning this, it's learning the commonalities in images, text or whatever data in order to form understanding on how to predict next steps in the generation. When it accidentally plagiarize something, it's similar to how we picture a memory in our head as clear as we can. I can remember a Van Gogh painting with high clarity, but is still not identical to the original. I can remember text I've read, but I often misremember the exact lines. This is because my mind fills in the gaps through predictive methods based on other memory and other information I've learned.
As I've mentioned numerous times in this thread, it's important to distinguish between training processes and generated outputs. The alignment problem is about getting the system to function in a way not destructive to society, in this case destructive to artists copyright. But the data it was trained on is part of information available in the world and since it's used behind closed doors in their labs, it's no different from how artists use copyrighted images, music or text in their workflow while creating something. A painter who cut out images from magazines and use it as inspirational references while painting, copying compositions, objects, colors and similar from those images are basically the same as an AI model trained on those images, maybe even less direct in copying when compared to some artists.
Have you ever scrolled through royalty free library music? Their process is basically taking what's popular in commercial music or movie soundtracks, replicating the sound of all instruments, take enough notes but changing one or two so as to not copy some track directly. How is this different from anything that Suno or Udio is doing?
And with scale, with millions of images, text, music etc. it means that the risk of accidental plagiarism is very low compared to an artist using just a few sources.
In the end it's still the responsibility of the one using the system to generate something who need to make sure they're not plagiarizing anything. It's their choice of how to use the image.
The output and the training process is not one and the same thing, but people use the nature of outputs and accidental plagiarism in outputs in relation to the training process and training data as proof for theft when it's not actually in support of such a conclusion. There's no database of copyrighted material on some cloud server somewhere in which these systems "get the originals". They don't store any copyrighted material anywhere but in their own lab. So how does that differ from an artist who's been scraping the web for references in their work storing it on their own hard drives?
The many people who produce copyrighted material haven't volunteered to give up their ideas. — BC
In what way did they "give up their ideas"? If I create an image, upload it to something like Pinterest and someone else downloads that image to use as a reference in their own artistic work, then they didn't commit theft. Why is it theft if a company uses it for training data of an AI model? As long as the output and generations are aligned not to fall into plagiarism, why does that matter anymore than if another artist used my images in their own workflow? Because the companies are part of a larger capitalist structure? That's not a foundation for defining "theft".
Here's an example of artwork that is "inspired" by the artist Giorgio de Chirico for the game cover of "ICO".
No one cares about that, no one screams "theft", in general they loved how the artist for the cover got "inspired" by Giorgio de Chirico.
But if I were to make an image with an AI diffusion model doing exactly the same, meaning, some elements and general composition is unique, but the style, colors and specific details were similar but not copied, and then use it commercially, then everyone would want to crucify me for theft.
If it was even possible, because if I ask DALL-E for it, it simply replies:
I was unable to generate the image due to content policy restrictions related to the specific artistic style you mentioned. If you'd like, I can create an image inspired by a surreal landscape featuring a windmill and stone structures at sunset, using a more general artistic approach. Let me know how you'd like to proceed!
And if I let it, this pops out:
It's vaguely resembling some aspects of Giorgio de Chirico's art style, but compared to the ICO game cover, it's nowhere near it.
This is an example of alignment for the usage of these systems, in which the system tries to recognize attempts to plagiarize. And this process is improving all the time. But people still use old examples of outdated models to "prove" what these systems are doing at the moment. Or they use the examples of companies or people who blatantly doesn't care about alignment to prove that a totally other company also does it because... "AI is evil" which is just the length of their entire argument.
And with court rulings from the past ruling in favor of the accused like in
Mannion v. Coors Brewing Co., then artists seem to be protected far more for blatant rip-offs than an AI diffusion model producing something far less of a direct copy.
So your claim is that adding intentionality to current diffusion models is enough to bridge the gap between human and machine creativity? Like I said before I don't have the ability to evaluate these claims with the proper technical knowledge but that sounds difficult to believe. — Mr Bee
Why is it difficult to believe? It's far more rooted in current understandings in neuroscience than any spiritual or mystifying narrative of the uniqueness of the "human soul" or whatever nonsense people attribute human creativity to stem from. Yes, I'm simplifying it somewhat for the sake of the argument; the
intention and predictive/pattern recognition system within us are rather constantly working as a loop influencing each other and constantly generating a large portion of how our consciousness operates. Other parts of our consciousness functions the same; like how our visual cortex isn't getting fed by some 200 fps camera that is our eyes, but rather that our eyes register photons that our visual cortex interprets by generating predictions in-between the raw visual data we feed through our eyes. It's the reason why we have optical illusions and if we stare at some object in high contrast for a long period of time and then look at a white canvas we see an inverted image as our brain try to over-compensate by generating a flow of data to fill in gaps that aren't seen anymore in raw data.
At its core the actual structure of a neural engine or machine learning is mimicking the exact nature of how our brain operates with pathways. We don't have a raw data copy of what we see or hear, we have paths that forms in relation to other memory paths and the relations between them forms the memories that we have. It's why we can store such vast amounts of information in our heads because it's not bound to physical "bits", but connections which become exponentially complex the more you have.
Inspired by these findings in neuroscience, machine learning using neural maps started to show remarkable increases of computing capabilities far beyond normal computers, but what they gained in compute power, they
lost in accuracy. Which is key to understanding all of this.
They don't copy anything because that would mean an AI model would be absolutely huge in size. The reason I can download an AI model that is rather trivial in size is because it's just a neural map, there's no training data within them. It's just a neural structure "memory", similar to the neural maps in our own brains.
And they're using the same "diffusion model" operations that tries to mimic how we "remember" from this neural map by analyzing the input (intention) and find pathway memories that links to the meaning of the input and interpret it into predictions that generating something new.
Recent science (that I've linked in above posts) have started to find remarkable similarities between our brain and these systems. And that's because they didn't make these AI models based on some final conclusions about our brain, they were instead inspired by what was found in neural science and just tried methods to mimic our brain, without knowing if it would work or what would happen.
This is the reason why no one still knows how an LLM could generate fluid text in other languages without direct programming of such functions and why many of these other functions just emerged from these large quantities of text data forming these neural maps.
It's rather that because these companies did all of this, neuroscientists are starting to use their research papers back into their own field as it shows hints at how functions in our brain emerge abilities just by how prediction occurs within these neural pathways. It's basically someone trying something before people know exactly how something works and discovering actual results.
The point being, it mimics what we know about how our brain generate something like an image or text and thus, what's missing is everything that constitutes an
"intention" in this process. "Intention" isn't just a computational issue, but something that reflects the totality of our mind, with emotions, behaviors and what might constitute everything about they physical of being a human. Human "intention" might therefore not be able to be copied without requiring everything that constitutes being a "human".
A good example of another technology that mimics a human function is the camera, or the recorder and speakers. These are more simplistic in comparison, but we've replicated the way our eyes register photons, especially in digital cameras, with lenses, rods and cones. And we've replicated how we record sounds using membranes and how our vocal cords produce sounds like membranes in a speaker and its hollow structure which forms sounds like our throat.
But when we mimic brain structures and we witness how they form behaviors similar to how our brain functions during creativity, we are all of a sudden thrown into moral questions about copyright in which people who don't understand the tech generally argues like those sitting in the audience at the first film projection, believing the train actually is about to hit them, or how record players and cameras took the souls of people when they got recording in those mediums.
As far as I see this, it's a religious and spiritual realm that makes people fear these AI models core functions, not scientific conclusions. It's about people breaking cameras because they think they will capture their souls.
Okay, but in most instances artists don't trace. — Mr Bee
Neither do diffusion models, ever. But artists who trace will still come out unscathed compared to how people react to AI generated images. Where is the line drawn? What's the foundation on which definitions of such differences are made?
I don't see how originality is undermined by determinism. I'm perfectly happy to believe in determinism, but I also believe in creativity all the same. The deterministic process that occurs in a human brain to create a work of art is what we call "creativity". Whether we should apply the same to the process in a machine is another issue. — Mr Bee
That's not enough of a foundation to conclude that machines do not replicate the physical process that goes on in our brain. You're just attributing some kind of "spiritual creative soul" to the mind, that it's just this "mysterious thing within us" and therefore can't be replicated.
Attributing some uniqueness to ourselves only because we have problems comprehending how this thing within us works or function isn't enough when we're trying to define actual laws and regulations around a system. What is actually known about our brain through neuroscience is the closest thing we have to an answer, and that should be the foundations for laws and regulations, not spiritual and emotional inventions. The fact is that human actions are always traceable to previous causes, to previous states, and a creative choice is no different from another type of choice.
The only reason why people attribute some uniqueness to our internal processing in creativity is because people can't separate their emotional attachment to the idea of divine creativity; It's basically just an existential question that when we break down the physical processes of the brain and find the deterministic behaviors and demystify creativity, people feel like their worldview and sense of self gets demolished. And the emotional reactions from that are no grounds for actual conclusions about how we function and what can be replicated in a machine.
Indeed the definitions are very arbitrary and unclear. That was my point. It was fine in the past since we all agree that most art created by humans is a creative exercise but in the case of AI it gets more complicated since now we have to be more clear about what it is and if AI generated art meets the standard to be called "creative". — Mr Bee
And when we dig into it, we see how hard it is to distinguish what actually constitutes human creativity form machine creativity.
However, I don't think this is a problem due to the problem of "intention". These AI models aren't really creative in exactly the way we are. They mimic the
physical processes of our creativity, which isn't the same as the totality of what constitutes being creative. We might be able to replicate this in the future, but for now, the intention is what drives the creativity, we are still asking the AI to make something, we are still guiding it. It cannot do it on its own, even though it has the physical neural pathway processing replicated. Even if we just hit a button for it to randomly create something, it then gets guided by the fundamental weights and biases that were there to inform it's fundamental basic handling of the neural map.
To generate we must combine intention with the process and therefor before we can judge copyright infringement, those to must have produced something. I.e the output. And so, the argument I've been making in here is that any attempt to blame the training process due to using copyrighted data in the training data is futile as nothing have been created until after intention and process generates an output.
Only then can plagiarism and other copyright problem be called into question.
However the problem is that in today's art industry, we don't just have artists and consumers but middle men publishers who hire the former to create products for the latter. The fact is alot of artists depend on these middle men for their livelihoods and unfortunately these people 1) Don't care about the quality of the artists they hire and 2) Prioritize making money above all else. For corporations artists merely create products for them to sell and nothing more so when a technology like AI comes up which produces products for them for a fraction of the cost in a fraction of the time, then they will more than happily lay off their human artists for what they consider to be "good enough" replacements even if the consumers they sell these products to will ultimately consider them inferior.
There are people who take personal commissions but there are also those that do commissions for commercial clients who may want an illustration for their book or for an advertisement. Already we're seeing those types of jobs going away because the people who commissioned those artists don't care in particular about the end product so if they can get an illustration by a cheaper means they'll go for it. — Mr Bee
Yes, some jobs will disappear or change into a new form. This have happened all the time throughout history when progress is rapidly changing society. Change is scary, and people are most of the time very comfortable in their bubble which when popped lead them to strike out like an animal defending themselves. This is where the luddite behavior comes into play.
But are we saying that we shouldn't progress technology and tools because of this?
When photoshop arrived with all its tools, all the concept artists who used pencils and paint behaved like luddites, trying to work against concept art being made with these new digital tools. When digital musical instruments started becoming really good, the luddites within the world of composing started saying that people who can't write notes shouldn't be hired or considered "real" composers.
What both these companies think and what luddites think of AI, they both forget that the working artist's biggest skill isn't that they can paint a straight line, or play a note, it's that they have the eye for composition and design, that they have an ear for melody, a mind for poetry.
People seem to have forgot what artists are actually hired for and it's not the craft. A concept artist isn't really hired for their personal style (if they're not among the biggest names in the industry), they're hired to guide the design based on the requirements of the project. They're hired for their ability to evaluate what's being formed and created.
All forms of art made within a larger project at such companies like game studios etc. is all in slavery to the overarching design of the entire project.
And because of this, the input that these artists have is very limited to the fundamental core of their expertise, i.e the knowledge of the artist to guide design towards the need of the project.
Therefore, a company who fires an artist in favor of someone who's not an artist to start working with AI generation, will soon discover that the art direction becomes sloppy and uninspiring, not because the AI model is bad, but because there's no "guiding principles" and expert eye guiding any of it towards a final state.
This is why artists need to learn to work with these models rather than reject them. Find ways of fusing their art style, maybe even train a personalized AI on their own art and work in a symbiosis with it.
Because this type of work for corporations is fundamentally soulless anyway. These artists aren't working with something they then own, the corporation owns it. They're there to serve a purpose.
In reality, an artist speeding up their process with AI would leave them more time to actually create for themselves. More time to figure out their own ideas and explore in more meaningful ways. Because they don't have to work overtime for some insecure producer who constantly changes their mind making them have to do patch works of their art because other people are lacking in creativity or ability to understand works of art.
Anyone who's been working within these kinds of corporate systems and these types of corporations aren't actually happy. Because there's no appreciation for anything they do and no understanding of their ideas as everything is filtered through whatever corporate strategy that drives the project from above.
Why not then be the artist who's an expert with AI? Because you can't put an intern onto writing prompts, that's not how generative AI works.
You need to have an eye for art even if you work with AI. Utilizing AI in your work for these companies does not destroy your artistic soul for what you make for yourself or your own projects.
The "good enough" companies, before these AI models, have never been good for artists anyway. Why would artists ever even care for their work towards these companies if they themselves won't care for the artists? So if they start becoming artist with expertise in AI, then these companies will soon hire them back once they realize it's just not viable to have non-artists handling their AI generations.
I don't think artists have really been thinking enough about this shift in the industry. Instead they're behaving like luddites thinking that's a good way forward. And companies who don't know the value of artists, didn't value artists before either. And those companies who are forced into using AI due to how it speeds up projects when trying to compete with others, but who still focus on the quality of art in their product, will still hire actual artists for the job of handling the generative AIs. Because they understand that the quality of the artist isn't in the brush, or photoshop, or a random prompt, it's in the eye, ear and mind to evaluate what's being created, to make changes, to guide something, a design, towards a specific goal.
How all of this just seem to get lost in the debate about generative AI is mind boggling.
Maybe this is because there are more non-artists, who aren't even working with any of this, who drives the hate against AI. Just like all the people who hate CGI in movies and just suck up the marketing of "no CGI" in movies nowadays. When the truth is that CGI is used all of the time in movies and these people simply have no idea what they're talking about, they just want to start a brawl online to feed the attention economy with their ego.
Of course the data collection isn't the problem but what people do with it. It's perfectly fine for someone to download a bunch of images and store it on their computer but the reason why photobashing is considered controversial is that it takes that data and uses it in a manner that some consider to be insufficiently transformative. Whether AI's process is like that is another matter that we need to address. — Mr Bee
Then you agree that the lawsuits going on that targets the training process rather than the outputs, uses of outputs and the users misusing these models are in the wrong.
Sorry if I missed some of your points but your responses have been quite long. If we're gonna continue this discussion I'd appreciate it if you made your points more concise. — Mr Bee
Sorry, ready this too late
:sweat: But still, the topic requires some complexity in my opinion as the biggest problem is how the current societal debate about AI is often too simplified and consolidated down into shallow interpretations and analysis.