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this post was submitted on 22 Aug 2023
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Ehh, "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.
Do we know enough about how our brain functions and how neural networks functions to make this statement?
Yes, we do. Take a university level course on ML if you want the long answer.
My friends who took computer science told me that we don't totally understand how machine learning algorithms work. Though this conversation was a few years ago in college. Will have to ask them again
What do you think education is? I went to university to acquire knowledge and train my skills so that I could later be paid for those skills. That was literally building my own human capital.
Humanities and Art majors are often criticized for not producing such capital.