Earlier quoted context omitted.
> The key insight is that any algorithm implementation for a process which has an objective must, as an absolute minimal requirement, possess an encoding of that objective in its implementation. I don't agree with this in any way, or perhaps more accurately, I don't agree that we know (and perhaps could know) the scope of the implementation even if this claim was true, which I don't think it is. The idea that "people…
What part of it don’t you agree with? That an algorithm implementation must encode the goal that it pursues? How can something pursue a goal it has no access to a definition of? If you have an alternative way it could work, please propose it. I’m not asking rhetorically, I’m truly interested in learning the flaws in my argument for why natural selection cannot be modelled as an optimization process. So if you have th…
But even if you could know, it is just demonstrably wrong that the implementation must encode the goal. If you create selection pressure, and have a reproductive system that allows for mutations, then you may end up an "implementation" that encodes the goal implicit in the selection pressure. But anyone who messed around with genetic algorithms or artificial life in the 90s knows that you can trivially start out with no resemblance to "the goal" at all. Where life on earth in aggregate or any specific example of it in particular might be along that pathway is similarly impossible to say.
Finally, even defining "the goal" is tricky. Consider the well-documented case of moth evolution in industrial (and later, post-industrial) northern England. Their camouflaging wing tones changed to respond to the typical color on vertical surfaces, twice within a human generation or three. Was "the goal" flexible coloration across generations, or was it "light, then "dark" and then "light" again? That's a philosophical question as much as anything ...