https://www.nature.com/articles/s42256-020-00278-8

Abstract
Evolutionary computation is inspired by the mechanisms of biological
evolution. With algorithmic improvements and increasing computing
resources, evolutionary computation has discovered creative and
innovative solutions to challenging practical problems. This paper
evaluates how today’s evolutionary computation compares to biological
evolution and how it may fall short. A small number of well-accepted
characteristics of biological evolution are considered: openendedness,
major transitions in organizational structure, neutrality and genetic
drift, multi-objectivity, complex genotype-to-phenotype mappings and
co-evolution. Evolutionary computation exhibits many of these to some
extent but more can be achieved by scaling up with available computing
and by emulating biology more carefully. In particular, evolutionary
computation diverges from biological evolution in three key respects: it
is based on small populations and strong selection; it typically uses
direct genotype-to-phenotype mappings; and it does not achieve major
organizational transitions. These shortcomings suggest a roadmap for
future evolutionary computation research, and point to gaps in our
understanding of how biology discovers major transitions. Advances in
these areas can lead to evolutionary computation that approaches the
complexity and flexibility of biology, and can serve as an executable
model of biological processes.

On Thursday, April 13, 2023 at 2:00:48 AM UTC-7, Popping Mad wrote:
> https://www.nature.com/articles/s42256-020-00278-8
>
> Abstract
> Evolutionary computation is inspired by the mechanisms of biological
> evolution. With algorithmic improvements and increasing computing
> resources, evolutionary computation has discovered creative and
> innovative solutions to challenging practical problems. This paper
> evaluates how today’s evolutionary computation compares to biological
> evolution and how it may fall short. A small number of well-accepted
> characteristics of biological evolution are considered: openendedness,
> major transitions in organizational structure, neutrality and genetic
> drift, multi-objectivity, complex genotype-to-phenotype mappings and
> co-evolution. Evolutionary computation exhibits many of these to some
> extent but more can be achieved by scaling up with available computing
> and by emulating biology more carefully. In particular, evolutionary
> computation diverges from biological evolution in three key respects: it
> is based on small populations and strong selection; it typically uses
> direct genotype-to-phenotype mappings; and it does not achieve major
> organizational transitions. These shortcomings suggest a roadmap for
> future evolutionary computation research, and point to gaps in our
> understanding of how biology discovers major transitions. Advances in
> these areas can lead to evolutionary computation that approaches the
> complexity and flexibility of biology, and can serve as an executable
> model of biological processes.

I share John's skepticism. This article has very little connection to anything. "Let's
let AI do it!" is (at least to me) not a promising path to understanding. Even Elon Musk(!)
suggests we ought to calm down a little. Artificial intelligence and artificial stupidity are
still close cousins.

erik simpson <eastside.erik@gmail.com> wrote:
>
> I share John's skepticism. This article has very little connection to anything. "Let's
> let AI do it!" is (at least to me) not a promising path to understanding. Even Elon Musk(!)
> suggests we ought to calm down a little. Artificial intelligence and artificial stupidity are
> still close cousins.

It is too late to date Sharyn Tate?