I suspect a lot of people but especially nerdy folks might mix up knowledge and intelligence, because they've been told "you know so much stuff, you are very smart!"
And so when they interact with a bot that knows everything, they associate it with smart.
There's a spectrum of human involvement in producing a thing, and art is possibly the last thing I want to see automate.
In the end, art is about human connection. There's a difference between an print of some generated AI slop found online, a painting made in a Chinese factory for a big store, and the scribble your friend made when they went through depression.
You can make a game with all three process. They are not the same.
The problem is that we mix up physical and perception, including in our language. If you look at the physical stuff, there's nothing in this specific range of EM radiation that is different from UV or IR light (or further). The physical stuff is not unique, our reading is. Therefore, color is not a physical thing.
And so when I say "color" I only mean it to be the construction that we make out of the physical thing.
We project back these construction outside of us (e.g. the apple is red), but we must no fool ourselves that the projection is the thing, especially when we try to be more precise about what is happening.
This is why I'm saying a 3D model of color (brain thing) is very far from modelling color (brain thing) at all. But! It's not purely physical either, otherwise it would just be a spectral band or something. So this is pseudo-perceptual. It's the physical stuff, tailored for the very first bits of anatomy that we have to read this physical stuff. It's stimuli encoding.
If you build a color model, it's therefore always perceptual, and needs to be evaluated against what you are trying to model - perception. You create a model to predict things. RGB and all the other models based on three values in a vaccum will always fail at predicting color (brain!) when the stimuli's surround is more complex.
Yes exactly. I'm intentionally using "color" as a perceptual thing, not as a physical thing. If we are talking about a color model, then it needs to model perception. As such, RGB, as a predictor of perception, can often fail because it doesn't account for much more than what hits the retina, not what happens after. For one, it lacks spatial context - placing the same RGB value with a different surround will feel different, like in the example above. But if you had a real color (as-in, perceptual) picker in Photoshop, you would get a different value.
It's excellent at compressing the visible part of the EM spectrum, however. This is what I meant by stimuli encoding.
They are very useful to encode stimuli, but stimuli is "not yet" color. When you have an image that is not just a patch of RGB value, a lot of things will influence what color you will compute based on the exact same RGB.
Akiyoshi's color constancy demonstrations are good examples of this. The RGB model (and any three-values "perceptual" model) fails to predict the perceived color here. You are seeing different colors but the RGB values are the same.
