L4sBotOpenAI now tries to hide that ChatGPT was trained on copyrighted books, including J.K. Rowling's Harry Potter series
SentauI think a lot of people are not getting it. AI/LLMs can train on whatever they want but when then these LLMs are used for commercial reasons to make money, an argument can be made that the copyrighted material mail has been used to make money. Similar to how using copyrighted clips in a monetized video can make you get a strike against your channel but if the video is not monetized, the chances of YouTube taking action against you is lower.
Tyler_ZoroAI/LLMs can train on whatever they want but when then these LLMs are used for commercial reasons to make money, an argument can be made that the copyrighted material has been used in a money making endeavour.
And does this apply equally to all artists who have seen any of my work? Can I start charging all artists born after 1990, for training their neural networks on my work?
Learning is not and has never been considered a financial transaction.
zbyte64Ehh, “learning” is doing a lot of lifting. These models “learn” in a way that is foreign to most artists. And that’s ignoring the fact the humans are not capital. When we learn we aren’t building a form a capital; when models learn they are only building a form of capital.
Artists, construction workers, administrative clerks, police and video game developers all develop their neural networks in the same way, a method simulated by ANNs.
This is not, “foreign to most artists,” it’s just that most artists have no idea what the mechanism of learning is.
The method by which you provide input to the network for training isn’t the same thing as learning.
ANNs are not the same as synapses, analogous yes, but different mathematically even when simulated.
This is orthogonal to the topic at hand. How does the chemistry of biological synapses alone result in a different type of learned model that therefore requires different types of legal treatment?
The overarching (and relevant) similarity between biological and artificial nets is the concept of connectionist distributed representations, and the projection of data onto lower dimensional manifolds. Whether the network achieves its final connectome through backpropagation or a more biologically plausible method is beside the point.