Darwinism's deafening silence on a plausible path to new organs.

 The Silence of the Evolutionary Biologists

William A. Dembski

I am reviewing Jason Rosenhouse’s new book, The Failures of Mathematical Anti-Evolutionism (Cambridge University Press), serially. For the full series so far, go here.

The Darwinian community has been strikingly unsuccessful in showing how complex biological adaptations evolved, or even how they might have evolved, in terms of detailed step-by-step pathways between different structures performing different functions (pathways that must exist if Darwinian evolution holds). Jason Rosenhouse admits the problem when he says that Darwinians lack “direct evidence” of evolution and must instead depend on “circumstantial evidence.” (pp. 47–48) He elaborates: “As compelling as the circumstantial evidence for evolution is, it would be better to have direct experimental confirmation. Sadly, that is impossible. We have only the one run of evolution on this planet to study, and most of the really cool stuff happened long ago.” (p. 208) How very convenient. 

Design theorists see the lack of direct evidence for Darwinian processes creating all that “cool stuff” — in the ancient past no less — as a problem for Darwinism. Moreover, they are unimpressed with the circumstantial evidence that convinces Darwinists that Darwin got it right. Rosenhouse, for instance, smugly informs his readers that “eye evolution is no longer considered to be especially mysterious.” (p. 54) It’s not that the human eye and the visual cortex with which it is integrated are even remotely well enough understood to underwrite a realistic model of how the human eye might have evolved. The details of eye evolution, if such details even exist, remain utterly mysterious.

A Crude Similarity Metric

Instead, Rosenhouse does the only thing that Darwinists can do when confronted with the eye: point out that eyes of many different complexities exist in nature, relate them according to some crude similarity metric (whether structurally or genetically), and then simply posit that gradual step-by-step evolutionary paths connecting them exist (perhaps by drawing arrows to connect similar eyes). Sure, Darwinists can produce endearing computer models of eye evolution (what two virtual objects can’t be made to evolve into each other on a computer?). And they can look for homologous genes and proteins among differing eyes (big surprise that similar structures may use similar proteins). But eyes have to be built in embryological development, and eyes evolving by Darwinian means need a step-by-step path to get from one to the other. No such details are ever forthcoming. Credulity is the sin of Darwinists.

Intelligent design’s scientific program can thus, at least in part, be viewed as an attempt to unmask Darwinist credulity. The task, accordingly, is to find complex biological systems that convincingly resist a gradual step-by-step evolution. Alternatively, it is to find systems that strongly implicate evolutionary discontinuity with respect to the Darwinian mechanism because their evolution can be seen to require multiple coordinated mutations that cannot be reduced to small mutational steps. Michael Behe’s irreducibly complex molecular machines, such as the bacterial flagellum, described in his 1996 book Darwin’s Black Box, provided a rich set of examples for such evolutionary discontinuity. By definition, a system is irreducibly complex if it has core components for which the removal of any of them causes it to lose its original function.

No Plausible Pathways

Interestingly, in the two and a half decades since Behe published that book, no convincing, or even plausible, detailed Darwinian pathways have been put forward to explain the evolution of these irreducibly complex systems. The silence of evolutionary biologists in laying out such pathways is complete. Which is not to say that they are silent on this topic. Darwinian biologists continue to proclaim that irreducibly complex biochemical systems like the bacterial flagellum have evolved and that intelligent design is wrong to regard them as designed. But such talk lacks scientific substance.

Next, “From Darwinists, a Shift in Tone on Nanomachines.”

Editor’s note: This review is cross-posted with permission of the author from BillDembski.com.

For Darwinism humor is no laughing matter.

 There’s Nothing Funny About Evolution

Geoffrey Simmons

Much like the genetic blueprints given to each of us at conception, blueprints for pumping blood, exchanging carbon dioxide for oxygen, digesting food, eliminating food, and retaining memories, we come with a built-in sense of humor. Could our sense of humor have evolved, meaning come about by millions of tiny, modifying, successive steps over millions of years? Or, did it arrive in one lump sum, by design? There are good reasons to suspect the latter.  But first some background musings.

For one thing, genetic studies suggest those folks with a better sense of humor have a shorter allele of gene 5-HTTLPR. In addition, we know there are many physiological benefits to laughter. Oxygenation is increased, cardiac function is improved, stress hormones, such as cortisol and adrenaline, are reduced, the immune system is charged up, and the dopaminergic system, which fights depression, is strengthened.

Norman Cousins, a past Adjunct Professor at UCLA, in his book Anatomy of an Illness as Perceived by the Patient, and in an article in The New England Journal of Medicine, wrote about how he lowered his pain levels from ankylosing spondylitis, from a 10 to a 2. Ten minutes of laughter gave him two hours of pain-free sleep. Much of this laughter came from watching TV. Nowadays, if one is over 13 years old, one might need to find a different medium.

We’re told that laughing 100 times is equal to 10 minutes on a rowing machine or 15 minutes on an exercise bike. Perhaps one could frequent a comedy club nightly and skip those painful, daily exercises. Humor helps us when times are stressful, when we’re courting, and when we’re depressed. Students enjoy their teachers, pay more attention, and remember more information when humor is added to classroom instruction. Humor promotes better bonding between student and teacher, and between most couples. It also helps with hostage negotiations.

A Darwinian Scenario

If our sense of humor came about by tiny steps, like other functions, as proposed by Charles Darwin, scientists have yet to find proof of it. Think of it: can hearing the beginning words of a joke even be funny? Is there any benefit to survival with one-word jokes that eventually become two- and three-word jokes? I, doubt it, but that’s just my personal opinion. 

Fish talk by means of gestures, electrical impulses, bioluminescence, and sounds like hard-to-hear purrs, croaks, and pops. But, did they (or could they) bring their jokes ashore millions of years ago? Of course, there’s no evidence of that. Yet? Just maybe one might envision the fish remaining in the water teasing the more adventuresome fish about their ooohs and aahs, issued while walking across burning-hot sands. 

Tickling a Rat

Laughing while being tickled is not the same as having a sense of humor. The response to someone reaching into one’s armpit is a neurological and physiological reaction to being touched. For some, tickling is torture. I had one rather serious female patient, who, when undressed and covered with a sheet, was ticklish from her neck to her toes. She was nearly impossible to examine. Sometimes she would start laughing as I approached her.

One can tickle a rat, and given the right equipment, record odd utterances that might be laughter. But it might easily be profanity. Some say one can tickle a sting ray, but others say the animal is suffocating. Attempts to tickle a crocodile and other wild animals have not been conducted, as far as I’m aware, in any depth. Also, such attempts are not recommended.

Laughing is clearly part of the human package, part of our design. As I see it, there can only be two possible origins. Humor evolved very, very slowly, or it came about more quickly by intelligent design. Negative feedback loops might argue against the slow development. Some fringe thinkers might speculate that extraterrestrials passed on their sense of humor to us, millions of years ago, but, if so, jokes about the folks in the Andromeda galaxy are on a different wavelength. Jokes about Uranus, of course, are local.

Sorry About that Last One, Folks

A sense of humor varies from person to person, much like height, weight, and abdominal girth. Plus, there are gender differences. Women like men who make them laugh; men like women who laugh at their jokes. Comedians say a sense of humor is a mating signal indicating high intelligence. People on Internet dating sites often ask each other about their sense of humor. Of course, we all have great senses of humor. Just ask anyone.

A sense of humor is often highly valued. Couples get along better when they have similar senses of humor. Mutation is more likely to ruin a good joke than help it. A serious mutation might take out the entire punchline. Jokes about a partner’s looks or clothes are to be avoided. They might lead to domestic abuse. Happy tears are chemically different from sad tears. Both are different from the tears that cleanse the eye with each blink or react to infections. Can anyone explain that? Could specific tears have come about by accident?

We know laughing is a normal human activity. Some days are better than others. Human babies often smile and giggle before they are two months old, years before they will understand a good riddle. Deaf and blind babies smile and giggle at virtually that same age. Is that present to make them more lovable? Children laugh up to 400 times a day, adults only 15 times per day. This could mean we need to hear many more jokes on a daily basis.

What Humor Means

 We all think we know what humor means, but because it can vary among people, we really don’t. An amusing joke told man-to-man might be a nasty joke if told man-to-woman. Or, the other way around. Humor tends to be intangible. It’s somewhat like certain foods tasting good to you, but maybe not to me. Too salty versus needs more salt? Or sweetener? I once told my medical partner that my wife and I had just seen the funniest movie we had ever seen. He and his wife went out that very night to see it and didn’t find anything in it funny. Nothing at all! Not even the funniest scene I have ever seen in a movie. Go figure. 

What does having a good sense of humor mean? Might it be reciting a lot of relevant jokes from a repository, making up funny quips during conversations, or laughing a lot at most anything except someone else’s pain? Or a mix?

There’s a laughter-like sound that is made by chimps, bonobos, and gorillas while playing. But does it mean there’s a sense of humor at work, or monkey profanity? They might be calling each other bad names. Octopuses play but don’t smile orlaugh, we think. Dolphins “giggle” using different combinations of whistles and clicks. It does seem like they are laughing at times, but nobody knows for sure. Maybe it’s just a case of anthropomorphizing. The dolphin family has been around approximately 11 million years and the area of their brain that processes language is much larger than ours. They’ve had plenty of time to come up with several good ones.

Koko the Humorous Gorilla

Perhaps, the most interesting case was Koko the gorilla who was taught to sign. She recently died after 46 years. Her vocabulary was at least 1,000 words by signing and another 2,000 words by hearing. Some say she was a jokester. She loved Robin Williams. Maybe adored him. The two would play together for hours. Koko seemed to make up jokes. She once tore the sink out of the wall in her cage; when asked about it, she signed that her pet cat did it. However, the cat wasn’t tall enough.

 So I ask again, could a sense of humor have come about by numerous, successive, slight modifications, a Darwinian requirement? If humor fails that test, might humor be the elusive coup de grace for naturalism? Since irreducible complexity, specified complexity, and topoisomerases haven’t landed the KO to Darwin’s weakening theories, might the answer just be as simple as laughing at them?

If a sense of humor were just a variation on tickling, my guess is that comedians would come off the stage or hire teenagers to walk among their audiences to tickle everyone. Imagine being dressed up for the night, maybe eating a fancy meal or drinking expensive champagne, and some grubby kid, who’s paid minimum wage, is reaching into your armpits.

Why Laugh at All? 

Is a sense of humor a byproduct, an accident, or was it installed on purpose? For better health? There definitely seems to be a purpose. Could it be a coping mechanism? Is it the way to meet the right mate? Surely, that must be part of it.

The only evolution-related quip I could think of sums up this discussion rather well:

A little girl asked her mother, “How did the human race come about?”

The mother answered, “God made Adam and Eve. They had children, and so all mankind was made.”

A few days later, the little girl asked her father the same question. The father answered, “Many years ago there were apelike creatures, and we developed from them.”

The confused girl returned to her mother and said, “Mom, how is it possible that you told me that the human race was created by God , and Papa says we developed from ‘apelike creatures’?”

The mother answered, “Well, dear, it is very simple. I told you about the origin of my side of the family, and your father told you about his.”

Man does not compute?

 The Non-Computable Human

Robert J. Marks II

Editor’s note: We are delighted to present an excerpt from Chapter 1 of the new book Non-Computable You: What You Do that Artificial Intelligence Never Will, by computer engineer Robert J. Marks, director of Discovery Institute’s Bradley Center for Natural and Artificial Intelligence.

If you memorized all of Wikipedia, would you be more intelligent? It depends on how you define intelligence. 

Consider John Jay Osborn Jr.’s 1971 novel The Paper Chase. In this semi-autobiographical story about Harvard Law School, students are deathly afraid of Professor Kingsfield’s course on contract law. Kingfield’s classroom presence elicits both awe and fear. He is the all-knowing professor with the power to make or break every student. He is demanding, uncompromising, and scary smart. In the iconic film adaptation, Kingsfield walks into the room on the first day of class, puts his notes down, turns toward his students, and looms threateningly.

“You come in here with a skull full of mush,” he says. “You leave thinking like a lawyer.” Kingsfield is promising to teach his students to be intelligent like he is. 

One of the law students in Kingsfield’s class, Kevin Brooks, is gifted with a photographic memory. He can read complicated case law and, after one reading, recite it word for word. Quite an asset, right?

Not necessarily. Brooks has a host of facts at his fingertips, but he doesn’t have the analytic skills to use those facts in any meaningful way.

Kevin Brooks’s wife is supportive of his efforts at school, and so are his classmates. But this doesn’t help. A tutor doesn’t help. Although he tries, Brooks simply does not have what it takes to put his phenomenal memorization skills to effective use in Kingsfield’s class. Brooks holds in his hands a million facts that because of his lack of understanding are essentially useless. He flounders in his academic endeavor. He becomes despondent. Eventually he attempts suicide. 

Knowledge and Intelligence

This sad tale highlights the difference between knowledge and intelligence. Kevin Brooks’s brain stored every jot and tittle of every legal case assigned by Kingsfield, but he couldn’t apply the information meaningfully. Memorization of a lot of knowledge did not make Brooks intelligent in the way that Kingsfield and the successful students were intelligent. British journalist Miles Kington captured this distinction when he said, “Knowing a tomato is a fruit is knowledge. Intelligence is knowing not to include it in a fruit salad.”

Which brings us to the point: When discussing artificial intelligence, it’s crucial to define intelligence. Like Kevin Brooks, computers can store oceans of facts and correlations; but intelligence requires more than facts. True intelligence requires a host of analytic skills. It requires understanding; the ability to recognize humor, subtleties of meaning, and symbolism; and the ability to recognize and disentangle ambiguities. It requires creativity.

Artificial intelligence has done many remarkable things. AI has largely replaced travel agents, tollbooth attendants, and mapmakers. But will AI ever replace attorneys, physicians, military strategists, and design engineers, among others?

The answer is no. And the reason is that as impressive as artificial intelligence is — and make no mistake, it is fantastically impressive — it doesn’t hold a candle to human intelligence. It doesn’t hold a candle to you.

And it never will. How do we know? The answer can be stated in a single four-syllable word that needs unpacking before we can contemplate the non-computable you. That word is algorithm. If not expressible as an algorithm, a task is not computable.

Algorithms and the Computable

An algorithm is a step-by-step set of instructions to accomplish a task. A recipe for German chocolate cake is an algorithm. The list of ingredients acts as the input for the algorithm; mixing the ingredients and following the baking and icing instructions will result in a cake.

Likewise, when I give instructions to get to my house, I am offering an algorithm to follow. You are told how far to go and which direction you are to turn on what street. When Google Maps returns a route to go to your destination, it is giving you an algorithm to follow. 

Humans are used to thinking in terms of algorithms. We make grocery lists, we go through the morning procedure of showering, hair combing, teeth brushing, and we keep a schedule of what to do today. Routine is algorithmic. Engineers algorithmically apply Newton’s laws of physics when designing highway bridges and airplanes. Construction plans captured on blueprints are part of an algorithm for building. Likewise, chemical reactions follow algorithms discovered by chemists. And all mathematical proofs are algorithmic; they follow step-by-step procedures built on the foundations of logic and axiomatic presuppositions. 

Algorithms need not be fixed; they can contain stochastic elements, such as descriptions of random events in population genetics and weather forecasting. The board game Monopoly, for example, follows a fixed set of rules, but the game unfolds through random dice throws and player decisions.

Here’s the key: Computers only do what they’re programmed by humans to do, and those programs are all algorithms — step-by-step procedures contributing to the performance of some task. But algorithms are limited in what they can do. That means computers, limited to following algorithmic software, are limited in what they can do.

This limitation is captured by the very word “computer.” In the world of programmers, “algorithmic” and “computable” are often used interchangeably. And since “algorithmic” and “computable” are synonyms, so are “non-computable” and “non-algorithmic.”

Basically, for computers — for artificial intelligence — there’s no other game in town. All computer programs are algorithms; anything non-algorithmic is non-computable and beyond the reach of AI.

But it’s not beyond you. 

Non-Computable You

Humans can behave and respond non-algorithmically. You do so every day. For example, you perform a non-algorithmic task when you bite into a lemon. The lemon juice squirts on your tongue and you wince at the sour flavor. 

Now, consider this: Can you fully convey your experience to a man who was born with no sense of taste or smell? No. You cannot. The goal is not a description of the lemon-biting experience, but its duplication. The lemon’s chemicals and the mechanics of the bite can be described to the man, but the true experience of the lemon taste and aroma cannot be conveyed to someone without the necessary senses.

If biting into a lemon cannot be explained to a man without all his functioning senses, it certainly can’t be duplicated in an experiential way by AI using computer software. Like the man born with no sense of taste or smell, machines do not possess qualia — experientially sensory perceptions such as pain, taste, and smell. 

Qualia are a simple example of the many human attributes that escape algorithmic description. If you can’t formulate an algorithm explaining your lemon-biting experience, you can’t write software to duplicate the experience in the computer.

Or consider another example. I broke my wrist a few years ago, and the physician in the emergency room had to set the broken bones. I’d heard beforehand that bone-setting really hurts. But hearing about pain and experiencing pain are quite different. 

To set my broken wrist, the emergency physician grabbed my hand and arm, pulled, and there was an audible crunching sound as the bones around my wrist realigned. It hurt. A lot. I envied my preteen grandson, who had been anesthetized when his broken leg was set. He slept through his pain.

Is it possible to write a computer program to duplicate — not describe, but duplicate — my pain? No. Qualia are not computable. They’re non-algorithmic.

By definition and in practice, computers function using algorithms. Logically speaking, then, the existence of the non-algorithmic suggests there are limits to what computers and therefore AI can do.

Darwinists attempt to correct God again.

 From Darwinists, a Shift in Tone on Nanomachines

William A. Dembski

I am reviewing Jason Rosenhouse’s new book, The Failures of Mathematical Anti-Evolutionism (Cambridge University Press), serially. For the full series so far, go here.

Unfortunately for Darwinists, irreducible complexity raises real doubts about Darwinism in people’s minds. Something must be done. Rising to the challenge, Darwinists are doing what must be done to control the damage. Take the bacterial flagellum, the poster child of irreducibly complex biochemical machines. Whatever biologists may have thought of its ultimate origins, they tended to regard it with awe. Harvard’s Howard Berg, who discovered that flagellar filaments rotate to propel bacteria through their watery environments, would in public lectures refer to the flagellum as “the most efficient machine in the universe.” (And yes, I realize there are many different bacteria sporting many different variants of the flagellum, including the souped-up hyperdrive magnetotactic bacteria, which swim ten times faster than E. coli — E. coli’s flagellum, however, seems to be the one most studied.)

Why “Machines”?

In 1998, writing for a special issue of Cell, the National Academy of Sciences president at the time, Bruce Alberts, remarked:

We have always underestimated cells… The entire cell can be viewed as a factory that contains an elaborate network of interlocking assembly lines, each of which is composed of a set of large protein machines… Why do we call the large protein assemblies that underlie cell function protein machines? Precisely because, like machines invented by humans to deal efficiently with the macroscopic world, these protein assemblies contain highly coordinated moving parts. [Emphasis in the original.]

A few years later, in 2003, Adam Watkins, introducing a special issue on nanomachines for BioEssays, wrote: 

The articles included in this issue demonstrate some striking parallels between artifactual and biological/molecular machines. In the first place, molecular machines, like man-made machines, perform highly specific functions. Second, the macromolecular machine complexes feature multiple parts that interact in distinct and precise ways, with defined inputs and outputs. Third, many of these machines have parts that can be used in other molecular machines (at least, with slight modification), comparable to the interchangeable parts of artificial machines. Finally, and not least, they have the cardinal attribute of machines: they all convert energy into some form of ‘work’.

Neither of these special issues offered detailed step-by-step Darwinian pathways for how these machine-like biological systems might have evolved, but they did talk up their design characteristics. I belabor these systems and the special treatment they received in these journals because none of the mystery surrounding their origin has in the intervening years been dispelled. Nonetheless, the admiration that they used to inspire has diminished. Consider the following quote about the flagellum from Beeby et al.’s 2020 article on propulsive nanomachines. Rosenhouse cites it approvingly, prefacing the quote by claiming that the flagellum is “not the handiwork of a master engineer, but is more like a cobbled-together mess of kludges” (pp. 151–152):

Many functions of the three propulsive nanomachines are precarious, over-engineered contraptions, such as the flagellar switch to filament assembly when the hook reaches a pre-determined length, requiring secretion of proteins that inhibit transcription of filament components. Other examples of absurd complexity include crude attachment of part of an ancestral ATPase for secretion gate maturation, and the assembly of flagellar filaments at their distal end. All cases are absurd, and yet it is challenging to (intelligently) imagine another solution given the tools (proteins) to hand. Indeed, absurd (or irrational) design appears a hallmark of the evolutionary process of co-option and exaptation that drove evolution of the three propulsive nanomachines, where successive steps into the adjacent possible function space cannot anticipate the subsequent adaptations and exaptations that would then become possible. 

The shift in tone from then to now is remarkable. What happened to the awe these systems used to inspire? Have investigators really learned so much in the intervening years to say, with any confidence, that these systems are indeed over-engineered? To say that something is over-engineered is to say that it could be simplified without loss of function (like a Rube Goldberg device). And what justifies that claim here? Have scientists invented simpler systems that in all potential environments perform as well as or better than the systems in question? Are they able to go into existing flagellar systems, for instance, and swap out the over-engineered parts with these more efficient (sub)systems? Have they in the intervening years gained any real insight into the step-by-step evolution of these systems? Or are they merely engaged in rhetoric to make flagellar motors seem less impressive and thus less plausibly the product of design? To pose these questions is to answer them.

A Quasi-Humean Spirit

Rosenhouse even offers a quasi-Humean anti-design argument. Humans are able to build things like automobiles, but not things like organisms. Accordingly, ascribing design to organisms is an “extravagant extrapolation” from “causes now in operation.” Rosenhouse’s punchline: “Based on our experience, or on comparisons of human engineering to the natural world, the obvious conclusion is that intelligence cannot at all do what they [i.e., ID proponents] claim it can do. Not even close. Their argument is no better than saying that since moles are seen to make molehills, mountains must be evidence for giant moles.” (p. 273) 

Seriously?! As Richard Dawkins has been wont to say, “This is a transparently feeble argument.” So, primitive humans living with stone-age technology, if they were suddenly transported to Dubai, would be unable to get up to speed and recognize design in the technologies on display there? Likewise, we, confronted with space aliens whose technologies can build organisms using ultra-advanced 3D printers, would be unable to recognize that they were building designed objects? I intend these statements as rhetorical questions whose answer is obvious. What underwrites our causal explanations is our exposure to and understanding of the types of causes now in operation, not the idiosyncrasies of their operation. Because we are designers, we can appreciate design even if we are unable to replicate the design ourselves. Lost arts are lost because we are unable to replicate the design, not because we are unable to recognize the design. Rosenhouse’s quasi-Humean anti-design argument is ridiculous.

Next, “Darwinist Turns Math Cop: Track 1 and Track 2.”

Editor’s note: This review is cross-posted with permission of the author from BillDembski.com.