It’s All Inevitablethe limitations of natural selection it is good to see the feature article in this week’s NewScientist admit that “current ways of thinking about evolution give a less-than-complete picture of how that [the spontaneous evolution of ‘all living things’] works.” Less-than-complete? That is evolution-speak for a theoretical meltdown. It’s no secret that the idea that the biosphere arose spontaneously is contradicted by the science. For evolutionists, that means their theory is “less-than-complete.” Well I suppose, technically, that is true. A theory that makes no sense is “less-than-complete.” Evolutionists are masters of the euphemism. They are also masters of the epicycle.
With each failure evolutionists modify and patch their theory with so many epicycles. Today the theory is enormously complex. And this NewScientist article provides one more example. The article explains the “less-than-complete” aspects of natural selection, and how it’s now all fixed. Yes, evolution was a fact, but now it’s even more of a fact. We were certain, but now we can be even more certain it is true.
Why? Because now natural selection has been expanded. Instead of just acting on genes, it acts on genetic regulatory processes—the incredibly complex networks of transcription factors and other molecular agents that help to regulate gene expression.
It turns out that if natural selection acts at the network level, everything works out as it should. We shouldn’t doubt this for, as evolutionists point out, it is analogous to the brain’s fancy techniques for learning.
In fact, natural selection is actually engaged in “deep learning,” a machine learning technique used on Big Data. As usual, strangely enough evolution mimics contemporary technology.
As we have discussed before, when the leading edge in biology was breeding, evolution was cast as a natural breeder. When computers became increasingly connected via networks, evolution was said to use “networks.” When artificial intelligence was thought to be on the horizon, evolution was said to use “molecular intelligence.” When the state of the art was genetic engineering, evolution is cast as a natural genetic engineer and “Biotechnology” was claimed as an evolutionary mechanism. So it is hardly surprising that now “Big Data” has been enlisted as yet another example of a cutting edge idea that fits right in with evolutionary theory. You see evolution is cool. It’s trendy and relevant. Whatever the latest technology is, it’s a perfect description of how evolution works.
As the article explains, evolution rests on three pillars: variation, natural selection and inheritance. And if natural selection uses machine learning techniques at the level of genetic regulation then, WaLa, we can now explain why evolution is such a good problem-solver, creating all sorts of complexity in such short order. In fact, it’s inevitable.
Of course there aren’t any actual biological details here. The results are obtained in the austere world of computer simulations, which evolutionists work hard at developing, debugging, and testing, to get just the right result. And those simulations are hosted on finely-tuned computers, running on loads of electricity, conveniently available in a wall socket.
And of course this new, high-tech, version of natural selection requires the pre existence of populations, functional, reproducing, organisms, heredity, and of course genetic regulation. No word on how all that arose, but we’re certain evolutionists will figure it out.