Not sure what to take of it. At first, I assumed that they mean that NN learned to divide color plane similarly to how humans do, without learning on human prov

Not sure what to take of it. At first, I assumed that they mean that NN learned to divide color plane similarly to how humans do, without learning on human provided data, which would be absolutely incredible, but no, no wonders here, the actual vocabulary was pre-defined. The actual result is that when you task NN with making up discrete words (and being discrete is pretty much the defining feature of any human language, quite naturally) and measure their "efficiency" by some information-theoretic metric, you get a distribution similar to what human languages have. And, again, that could have been interesting, except it turns out to be right on the border of what is "theoretical limit". Well then, if there is only 1 theoretical limit, so it's no big surprise that when you train the system like that, penalizing inefficiency by any related metric (let alone the same one), you'll get the same distribution. I'm cautious not to say that it has nothing to do with linguistics, because half of linguistics is math because of the nature of what is language. But that's about it. That is like setting up an actual physical experiment with the Galton Board, to "discover" that you'll get a normal-distribution looking pattern with aluminium balls the same way as you'll get with wooden balls. That's all about math involved in some information-theoretical concept, not about human laguages per se.