| I find the analogy itself super interesting and thought-provoking, and might be quite useful in studying biological systems. The idea that we need to move away from understanding the genetic code as static machinery also aligns well with recent understanding of biology, as summarized in the highly appraised book "How Life Works" by Philip Ball [1] But in terms of evolution, I don't see how the proposed analogy/model will get away with the fact that natural selection operates on the individual level (either you survive or you don't), while all genomic information is a package of (depending on the organism) humongous number of genes, not to mention base pairs or individual locuses (that's not even mentioning diploidy, that will mean two different copies of each locus). With the known proportions and numbers of positive vs slightly deleterious mutations (much more of the latter, counted in the hundreds per individual), selection of positive mutations can not avoid accumulating slightly deleterious mutations. I don't get how the proposed model is supposed to solve that. I see that a smartly designed system could probably have processes that can change multiple loci in parallel in a beneficial way (along the lines of the theory of facilitated variation by Gerhart & Kirschner. See their papers or [2]), but that would only explain how such a fine-tuned system could be effective at adaptation, not how the system itself - including its processes - could arise from a state before these processes are in place. To connect it to ML: In natural selection you don't have gradients and the ability to update multiple parameters based on detailed feedback on them individually. You only can provide a whole, binary feedback (survive, 1, or not, 0), to the whole set of parameters, whether they are slightly deleterious or possibly positive. The resolution is simply lacking here. - [1] https://www.amazon.com/How-Life-Works-Users-Biology/dp/02268... - [2] https://www.amazon.com/Plausibility-Life-Resolving-Darwins-D... |