There is nothing special about human level intelligence, unless you have imitation learning, in which case human level capabilities are very special.
General intelligence is not very efficient. Therefore there will not be any selection pressure for general intelligence as long as other options are available.
GI is very efficient, if you consider that you can reuse a lot machinery that you learn, rather than needing to relearn it over and over again. https://towardsdatascience.com/what-is-better-one-general-model-or-many-specialized-models-9500d9f8751d
Second reply. And this time I actually read the link. I'm not suppressed by that result.
My original comment was a reaction to claims of the type [the best way to solve almost any task is to develop general intelligence, therefore there is a strong selection pressure to become generally intelligent]. I think this is wrong, but I have not yet figured out exactly what the correct view is.
But to use an analogy, it's something like this: In the example you gave, the AI get's better at the sub tasks by learning on a more general training set. It seems like ... (read more)
I agree that eventually, at some level of trying to solve enough different types of tasks, GI will be efficient, in terms of how much machinery you need, but it will never be able to compete on speed.
Also, it's an open question what is "enough different types of tasks". Obviously, for a sufficient broad class of problems GI will be more efficient (in the sense clarified above). Equally obviously, for a sufficient narrow class of problems narrow capabilities will be more efficient.
Humans have GI to some extent, but we mostly don't use it. This is interesting. This means that a typical human environment is complex enough so that it's worth carrying around the hardware for GI. But even though we have it, it is evolutionary better to fall back at habits, or imitation, or instinkt, for most situations.
Looking back to exactly what I wrote, I said there will not be any selection pressure for GI as long as other options are available. I'm not super confident in this. But if I'm going to defend it here anyway by pointing out that "as long as other options are available", is doing a lot of the work here. Some problems are only solvable by noticing deep patterns in reality, and in this case a sufficiently deep NN with sufficient training will learn this, and that is GI.