I definitely don’t understand this paper to be what I thought the headline meant. I thought the headline meant that generally AI systems would end up having co

I definitely don’t understand this paper to be what I thought the headline meant. I thought the headline meant that generally AI systems would end up having color systems which distinguished blue and orange more readily than taupe and beige. But the paper doesn’t seem to talk about that, except in the abstract - the paper instead seems to report that the more complex the ‘allowed’ grammar, the more specificity AI can have about colors. And, by the way, that’s true for human languages too, and you can train a grammar that’s close to the info-theoretic limit, which by the way is what you can say about most human color languages. It seems to me like you could train an AI to report hexadecimal for colors pretty easily, so I remain confused about a lot of details, most of which seem to be embedded in the referenced paper “Efficient compression in color naming and its evolution.” I haven’t read that paper. Without reading it, I’m not sure if the networks developed just carried over concepts from it, and thereby got similar segmentations (and the graphs look kind of similar to me, but not exact or anything), or if there is something fundamental. All that said, it would be REALLY cool if alien intelligence-like assessment of colors saw things the way humans see them, that would be rad. I just have no idea if this research proves that or not.