Perhaps we could call it neo-neo-Darwinism. As Brendan Dixon noted here yesterday, New Scientist promises that a “radical new addition to the theory of evolution” will fill in some gaps in the hoary old theory “while turning theories of intelligent design on their head.” Who would want an upside-down theory, even if it were intuitively compelling?

A feather isn’t just pretty: it’s pretty useful. Strong, light and flexible, with tiny barbs to zip each filament to its neighbours, it is fantastically designed for flight. The mammalian eye, too, is a marvel of complex design, with its pupil to regulate the amount of light that enters, a lens to focus it onto the retina, and rods and cones for low light and colour vision — all linked to the brain through the optic nerve. And these are just the tip of the iceberg of evolution’s incredible prowess as a designer.

For centuries, the apparent perfection of such designs was taken as self-evident proof of divine creation. Charles Darwin himself expressed amazement that natural selection could produce such variety and complexity. Even today, creationism and intelligent design thrive on intuitive incredulity that an unguided, unconscious process could produce such intricate contraptions. [Emphasis added.]

And so the debate is engaged, although you predict from the outset that a pro-Darwin venue like New Scientist would rig the verdict somehow or other. That, of course, they do, with fanfare and audacity. It’s so easy. It’s amazing nobody ever thought of it before. Just turn natural selection into a learning network! Here’s how the idea goes:

It seems that, added together, evolution’s simple processes [variation, selection and inheritance] form an intricate learning machine that draws lessons from past successes to improve future performance. Get to grips with this idea, and we could have a raft of new tools with which to understand evolution. That could allow us to better preserve the diversity of life on Earth — and perhaps even harness evolution’s power.

It’s not evolution’s task to design an eye, therefore. It’s your job to get a grip on the new idea that it can. Before you grip, though, ask the Darwin apologist, “Doesn’t learning risk ‘violating one of evolution’s most important principles‘ — i.e., that ‘evolution can’t see the future‘?” As expected, the apologist has been coached on this very question.

But then again, learning organisms can’t actually see the future. When we cross a road, we can’t anticipate all traffic movements, but we have a memory bank of solutions that have worked before. We develop a strategy based on those — and if it proves successful, we call on that newly learned experience next time. That’s not too dissimilar to what natural selection does when it reuses successful variants from the past, such as the flowers of bee orchids that are unusually good at attracting bees, or the mouthparts of mosquitoes that work like hypodermic syringes and are particularly effective at sucking blood.

“But wait,” you ask; “a memory bank of solutions? A strategy? Where are those located?” Well, silly, in the genes; where else? Each organism carries around its library of solutions. Those individuals that are quick enough to call up old solutions for new problems survive and reproduce, just as with old-fashioned natural selection.

Ever since the Human Genome Project, we know that genes are not like static books on a library shelf. They form networks of interacting parts. Mutations alter the strengths or weaknesses of those interactions. Stronger interactions in gene networks increase fitness, analogous to how “neurons that fire together wire together,” the article explains. “When we learn, we alter the strengths of connections, making networks of associations capable of problem-solving.” No intelligence is required; it happens automatically. Evolution rewards those networks that blindly “produce a phenotype that is fit in a given environment.”

No wonder evolution “is such a good problem-solver, creating all that complexity in such short order.” Why, “evolution looking like the product of intelligence is exactly what you’d expect.” Richard Watson at the University of Southampton is a champion of this new “smart” evolution.

This way of working allows genotypes to generate phenotypes that are both complex and flexible. “If past selection has shaped the building blocks well, it can make solving new problems look easy,” says Watson. Instead of merely making limbs longer or shorter, for example, evolution can change whether forelimbs and hindlimbs evolve independently or together. A single mutation that changes connections in the network can lengthen all four legs of a giraffe, or allow a bat to increase its wingspan without getting too leggy. And a feather or an eye needn’t be generated from scratch, but can evolve by mixing and matching building blocks that have served well in the past ….

This ability to learn needs no supernatural intervention — it is an inevitable product of random variation and selection acting on gene networks. “Far from being blind or dumb, evolution is very smart,” says Watson.

In fact, evolution gets better at this over time, becoming more “evolvable” as it learns. It requires no forethought. You get evolvability naturally if evolution itself is a “learning machine.”

A number of big-name Darwinians are jumping on board this intriguing new idea. David Sloan Wilson says, “In the past, it has been heretical to think about evolution as a forward-looking process, but the analogy with learning — itself a product of evolution — is quite plausible.”

Think of it. With the addition of one little learning module, you get the evolution of evolvability, the evolution of ecological networks, and the “major transitions” in the history of life (protocells from molecules, multicellular life from cells, sexual reproduction, etc.) included in the package. That’s quite a breakthrough. “The observation that evolutionary adaptations look like the product of intelligence isn’t evidence against Darwinian evolution — it’s exactly what you should expect.”

Reality Check

Let’s think critically. Could it be that all they have done is swap the unit of selection? Evolutionists have argued about that for years. It’s doubtful this contender will convince those who locate the target of selection at a different level. In a New Scientist interview back in 2012, Charles Lineweaver was asked if life can be defined as anything that undergoes Darwinian evolution.

We pretend that makes sense, but if you look it makes no sense at all. What is the unit of Darwinian evolution? Is it the gene? Is it the cell? Is it a multicellular organism? Is a city evolving? How about Gaia? Is that a life form?

It’s no more sensible to call a gene network a learning entity than to ascribe learning to those other units of selection. In fact, a strong case could be made that a whole population of organisms learns better than a gene network could. Their claim that evolution becomes more flexible also is dubious. If gene networks tend toward fewer connections because of the energetic cost, one would expect canalization (getting in a rut), not evolvability.

What’s worse, try as they might to make the process look unguided, they personify evolution as “smart” — a “problem solver” that can “mix and match” building blocks, strategizing about what has “worked well in the past” to solve problems. Evolution couldn’t care less about solving problems. The easiest solution is always to go extinct. In a mindless world, who cares? Yet we are told about evolution’s “incredible prowess as a designer.”

Finally, this proposal recklessly extrapolates beyond all reasonableness. So a gene network gets stronger. Suddenly we have an eye, a feather, and a human brain. It’s like teaching a parrot to say “Polly want a cracker” and then promoting the parrot to Project Manager for a mission to Mars.

These criticisms plagued the old neo-Darwinism, and the classic Darwinism before that. It’s the same nothing-new-under-the-sun materialism that stands on its head and claims intelligent design is upside down.

Image: Brazilian tanager, by Dario Sanches [CC BY-SA 2.0], via Wikimedia Commons.