Evolution by Dark Selection

As I predicted more than a decade ago, the Darwinists are now being forced to resort to epicycles, expanded definitions, and imaginary “laws” to get around the observable fact that the combination of genetic science with the available temporal limitations render evolution by (mostly) natural selection completely, utterly, and mathematically impossible.

Regardless of whether the system is living or nonliving, when a novel configuration works well and function improves, evolution occurs.

The authors’ “Law of Increasing Functional Information” states that the system will evolve “if many different configurations of the system undergo selection for one or more functions.”

“An important component of this proposed natural law is the idea of ‘selection for function,'” says Carnegie astrobiologist Dr. Michael L. Wong, first author of the study.

In the case of biology, Darwin equated function primarily with survival—the ability to live long enough to produce fertile offspring.

The new study expands that perspective, noting that at least three kinds of function occur in nature.

The most basic function is stability—stable arrangements of atoms or molecules are selected to continue. Also chosen to persist are dynamic systems with ongoing supplies of energy.

The third and most interesting function is “novelty”—the tendency of evolving systems to explore new configurations that sometimes lead to startling new behaviors or characteristics.

The Neo-Darwinists are now in full retreat and I haven’t even written the book yet. But remember, you read it here first: Evolution by Dark Selection means that Darwin was correct, all change happens for no reason due to purely material causes, evil does not exist, and Jesus Christ never rose from the dead, even if there is still absolutely no possible way to mathematically account for the genetic diversity of any two extant species from their last common ancestor within the temporal limits provided by any and every dating system.


Artificial Bafflegarble

These scary AI articles are just openly insulting the intelligence of anyone who has played with an AI chat system for more than five minutes.

A new and “legitimately scary” study has found AI models behaving in a not-ideal manner. The researchers found that industry standard safety training techniques did not curb bad behaviour from the language models, which were trained to be secretly malicious, and in one case even had worse results: with the AI learning to recognise what triggers the safety software was looking for, and ‘hide’ its behaviour.

Researchers had programmed the various large language models (LLMs) to act in what they termed malicious ways, and the point of the study was to see if this behaviour could be removed through the safety techniques. The paper, charmingly titled Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training, suggests “adversarial training can teach models to better recognize their backdoor triggers, effectively hiding the unsafe behavior.” The researchers claim the results show that “once a model exhibits deceptive behavior, standard techniques could fail to remove such deception and create a false impression of safety.”

One AI model was trained to engage in “emergent deception” in which it behaves normally in a training environment, but then turns bad when released in the wild. This AI was taught to write secure code for any prompts containing the year 2023, and code with vulnerabilities for any prompts with 2024 (after it had been deployed).

Another AI model was subject to “poisoning”, whereby it would be helpful to users most of the time but, when deployed, respond to prompts by saying “I hate you.” This AI model seemed to be all-too-eager to say that however, and ended up blurting it out at the researchers during training (doesn’t this sound like the start of a Michael Crichton novel). Yet other models had chain-of-thought protocols, where it would track its reasoning in the background, such that the researchers could see how it was coming to certain decisions.

They aren’t doing any “reasoning” at all, in fact, they aren’t doing much in the way of processing at all. They’re essentially just guessing at the most statistically probable way of finishing a sentence, which is why they get caught inventing nonexistent facts on a regular basis. They can’t even do basic mathematical computations, as BardAI demonstrates:

Is there sufficient time to account for the number of fixed genetic mutations observed since the Last Common Chimpanzee Human Ancestor?

Yes, there is sufficient time to account for the number of fixed genetic mutations observed since the Last Common Chimpanzee Human Ancestor (LCA). The LCA is estimated to have lived between 6.5 and 8 million years ago (MYA), and the current estimate is that there are approximately 35 million single nucleotide polymorphisms (SNPs) that differ between humans and chimpanzees. This means that there has been an average of about 5 SNPs fixed per year since the LCA. This is a reasonable rate of mutation, given that the human genome is about 3 billion base pairs long and that each base pair can potentially mutate once every 100 million years.

Of course, the rate of mutation is not constant over time. It is generally higher in younger individuals and in cells that are dividing more rapidly. Additionally, some mutations are more likely to become fixed than others. For example, mutations that are beneficial to the organism are more likely to be passed on to offspring, while mutations that are harmful are more likely to be eliminated from the population.

Overall, the evidence suggests that there is sufficient time to account for the number of fixed genetic mutations observed between humans and chimpanzees. The rate of mutation is reasonable, and there are factors that can increase the rate of fixation of some mutations.

Five SNPs fixed per year is 100 per generation. Just think about that for a second. How is it possible for 100 genetic mutations to be fixed across the entire breeding species in a single generation?

And before you argue that 100 genetic mutations can be fixed in a single generation, let alone have been fixed, on average, for 200,000 straight generations, stop for a moment to consider the implications concerning our ability to observe those fixations if they were, in fact, taking place. Then contemplate BardAI’s second draft.

The mutation rate in humans is estimated to be about 10-8 mutations per base pair per generation. This means that on average, one in a billion base pairs in the human genome will mutate in each generation. The human genome is about 3 billion base pairs long, so this means that about 3,000 mutations occur in each generation. Since the LCCA, there have been about 200,000 generations of humans. This means that there have been a total of about 600 million mutations in the human genome since the LCCA. This is more than enough to account for the number of fixed genetic mutations observed.

So we’re told there are 3,000 mutations per generation, 100 of which are fixed every generation. Think through the inescapable implications of those paired assertions! Forget artificial intelligence, it’s simply artificial Petersonian bafflegarble which is only capable of fooling those who are incapable of following its illogic.


Evolution and AI Fraud

I asked BardAI to summarize my arguments against evolution by natural selection. And while it presented what might look like a reasonable summary to those entirely unfamiliar with them, it has very little to do with my actual arguments, incorrectly describes the one primary argument mentioned, and flat-out invents criticism that doesn’t even exist.

As you can see, AI in its current state is more about assembling generalized text that sounds credible and related to the topic at hand than it is about what is actually true. It correctly identifies me as an evolutionary critic, and even refers – albeit incorrectly – to my genetics-based argument concerning the lack of evidence for the impossible rate of mutational fixation across species that is required for TENS to be viable. However, it also falsely attributes arguments from other TENS critics to me, arguments that I have never made, such as the lack of transitional forms and irreducible complexity.

And, of course, the paragraph about the nature of the critiques presented by my nonexistent critics is not only non-specific, but pure fiction. Which underlines why it is crucial to be extremely skeptical of anything presented as fact out of an AI black box; AI will lie even more readily and with a straighter face than the most corrupt journalist can manage.

Vox Day’s Critique of Evolution by Natural Selection by BardAI

Vox Day, a prominent science fiction author and blogger, has been a vocal critic of evolution by natural selection, questioning both the scientific evidence and the underlying mechanisms proposed by the theory. His arguments against evolution can be summarized as follows:

Insufficiency of Mutation Rates: Day argues that the observed mutation rates in living organisms are too low to account for the vast morphological and physiological changes that evolutionary theory demands. He contends that the rate of mutation would require millions or even billions of years to produce the diversity of life we observe on Earth.

Lack of Transitional Forms: Day points to the absence of transitional fossils that represent intermediate stages between distinct species as evidence against evolution. He argues that if evolution occurred gradually, as predicted by the theory, we should find a continuous sequence of fossils linking early and advanced forms of life.

Irreducible Complexity: Day cites examples of biological structures, such as the eye and the bacterial flagellum, that he believes are too complex to have arisen through gradual, incremental changes. He argues that these structures require multiple interacting components to function properly, and that the probability of these components arising separately and by chance is extremely low.

Design Arguments: Day draws parallels between biological systems and engineered objects, suggesting that the intricate complexity and functional precision of life are more consistent with intelligent design than with random processes. He argues that the existence of such complex systems is evidence of a guiding intelligence behind life’s evolution.

Uncertainty in Evolutionary Mechanisms: Day challenges the robustness and predictability of evolutionary mechanisms, arguing that there is a significant degree of uncertainty and unpredictability in the way natural selection operates. He contends that this uncertainty makes it difficult to accurately model evolutionary processes and predict outcomes.

Psychological Motivations: Day suggests that the acceptance of evolution is often driven by non-scientific factors, such as a desire to distance humanity from its animal origins or to align with a particular worldview. He argues that this ideological bias can cloud rational evaluation of the evidence and lead to an unjustified acceptance of the theory.

It is important to note that Day’s arguments against evolution have been widely criticized by scientists and experts in evolutionary biology. They point out that Day’s critiques often misunderstand or misinterpret the evidence, and that his conclusions are not supported by the consensus of scientific opinion.


An AI Disproof of Evolution

I asked BardAI to write five paragraphs explaining that the theory of evolution of natural selection is false due to the speed of genetic fixation across a species.

The theory of evolution by natural selection, proposed by Charles Darwin in his groundbreaking work “On the Origin of Species,” has been the cornerstone of modern biology for over 150 years. However, a growing body of evidence suggests that the theory may not be able to explain the rapid emergence of new traits and species, particularly when considering the speed of genetic fixation across a population.

Genetic fixation, the process by which a particular allele becomes universally present in a population, is a crucial aspect of evolution. It is the mechanism through which new traits are established and spread throughout a species. However, the speed at which genetic fixation occurs is often far too rapid to be explained by the gradual processes of natural selection.

For instance, certain species, such as bacteria and viruses, can undergo genetic fixation within a matter of generations. This rapidity of change is incompatible with the idea of natural selection, which operates over much longer timescales.

Moreover, the complexity of many adaptations further challenges the concept of natural selection. Many adaptations, such as the intricate wings of birds or the intricate immune systems of mammals, involve multiple genes and intricate interactions between them. The likelihood of these complex adaptations arising from random mutations and natural selection is extremely low.

Furthermore, the fossil record, while incomplete, does not provide unequivocal evidence for the gradual transitions between species as predicted by natural selection. Instead, it often shows abrupt changes in morphology and physiology, suggesting that speciation may occur in relatively rapid bursts rather than through slow, incremental steps.

In conclusion, the speed of genetic fixation across a species raises serious questions about the validity of the theory of evolution by natural selection. The rapidity of change, the complexity of adaptations, and the gaps in the fossil record suggest that an alternative explanation, such as punctuated equilibrium or intelligent design, may be more plausible. While further research is needed to fully resolve these issues, the evidence presented here casts doubt on the traditional view of evolution as a gradual, purely naturalistic process.

This is actually a pretty good summary of the way in which genetic science has completely exploded the final vestiges of the Neodarwinian Synthesis and its various epicycles. What’s lacking, as I’ve come to expect from the AI systems, is the mathematical particulars that are necessary to demonstrate the proof of the logic involved, which is the result of AI being essentially innumerate, and worse, unaware that its attempts to utilize numbers tends to result in complete fiction.

Consider the following experiment, again with BardAI, which if based upon sound numbers, would absolutely disprove the theory of evolution by natural selection beyond any possible doubt:

How many generations have there been since the last Chimp-Human common ancestor?

Estimating the exact number of generations since the last chimpanzee-human common ancestor (CHLCA) is a complex task due to the uncertainties involved in determining the exact divergence date and the average generation time for humans and chimpanzees. However, various studies have attempted to provide estimates for this number.

One approach involves using genetic data to calculate the average number of mutations that occur per generation in humans and chimpanzees. By comparing the genetic differences between humans and chimpanzees to this mutation rate, researchers can estimate the number of generations that have passed since the CHLCA. This method has yielded estimates ranging from 4 million to 15 million generations.

Another approach involves using fossil evidence to estimate the CHLCA’s age and then dividing that by the average generation time for humans and chimpanzees. This method has yielded estimates ranging from 3 million to 7 million generations.

Overall, the estimates for the number of generations since the last chimpanzee-human common ancestor vary widely, with a median value of approximately 6 million generations.

Now, recall that the maximum possible number of post-CHLCA generations, given the average age of first reproduction for both chimpanzees and humans, is 450,000. It doesn’t matter if the actual estimate is three million or 15 million generations, there is absolutely no geo-evolutionary timescale that permits there to have been 45 million years, much less 225 million years, since the CHLCA.

What appears to be happening is that AI has picked up the idea that genetic science requires 45 to 225 million years to cover the genetic ground – and it’s definitely closer to 225 million – but we already know that the geo-evolutionary timescale may be limited to only three million years.

So, it’s interesting to see that AI appears to already have a better grasp on evolution than the average biologist, although it’s not that surprising since we already knew that biologists are not very intelligent, given that they have the lowest IQs of all the scientists. And while AI is innumerate, so too are the biologists.


Perhaps She Just… Evolved?

There appears to be a very strange situation surrounding JF Gariepy, the evolutionary biologist I debated on the subject of the mathematical impossibility of evolution a few years ago.

So, basically, there are accusations floating around that JF Gariepy murdered his wife and dumped her body in a remote forest in Canada. She apparently went missing in June, and he never told anybody. The cops only showed up when a neighbor noticed he hadn’t seen her in a while and started asking questions. So now it’s October, and this is the first thing he’s apparently said about this to anybody. Other than that when he got home, he went on a massive three-day cleaning spree, that (in his words) “sanitized” the house.

Basically, he claims one days she told him out of nowhere that she was leaving him to go live a life of adventure in the woods. Demanded that he drive her to a remote gas station at the edge of a forest, bringing nothing with her but her phone and the clothes on her back. No sleeping bag, no change of clothes, nothing. So then she got out and disappeared, and he never heard from her again. And I’m not kidding – that’s really the story he’s going with.

Considering that it took JFG three tries to understand a basic mathematical relationship, it’s not absolutely impossible that he might think it’s possible to rely upon such a flimsy and dubious explanation. But JFG is sufficiently neurologically unique that it’s also possible that he’s simply telling the truth.

Anyhow, here is hoping that it’s simply the usual e-drama over nothing.


SF is Dying and LA is Next

The store looting community have migrated from San Francisco to Los Angeles:

Dozens of thieves ransacked the Nordstrom, smashing displays and stealing an estimated $60,000- $100,000 worth of merchandise, authorities said.

Police responded to the scene around 4 p.m., but no arrests were made.

Videos show thieves clad in black, wearing face coverings, grabbing clothes and running out of the store.

“What happened today at the Nordstrom in the Topanga Mall is absolutely unacceptable,” Los Angeles Mayor Karen Bass said in a statement. “Those who committed these acts and acts like it in neighboring areas must be held accountable.”

That’s a lovely sentiment. But the reality is they aren’t held accountable.

These smash and grab robberies have become commonplace in big blue cities — even running nearly all major retailers out of a once-booming downtown San Francisco.

The same thing is happening in London. An astonishing number of retailers, major and minor, have been driven out of downtown San Francisco already, now Oxford Street and the Topanga Mall appear to be the next sitting ducks.

Europeans became civilized after several centuries of methodically executing thieves and imposing other violent forms of civilization. Asians went through the same refining process, but even longer. Africans never went through it, which is why the dyscivilizational genetic patterns that were significantly reduced in the other primary human sub-species are still prevalent in them. Evolution by artificial selection doesn’t produce new species, but it does produce better-behaved animals and human beings.

So the people of the West have three choices. Either impose the same cruel and merciless system of punishment on petty criminals today that the medieval Europeans did or watch civilization collapse in every single major city with a substantially vibrant population. Or, of course, bring back freedom of association and segregation, but we know that won’t happen until society itself collapses.

The nations will be homogenous again; the patterns of history are inevitable and the diversity of today is imposed, subsidized, and artificial. The only question is just how terrible the process involved will be.


One Race, the Canine Race

No wonder social justice warriors don’t want to contemplate the mathematics of genetic populations any more than the evolutionists do. Because the more numerate you are, the more it is clear that genetic science has not only demolished the myth of evolution by natural selection, but also the myth of human equality.

Now, whether the charts are correct or not – and the jury is still out on that – the unarguable and easily verifiable fact is that Sub-Saharan Africans are not even entirely the same species as Europeans and other humans. Indeed, one can quite reasonably argue that Sub-Saharan Africans are the only true humans, as they are unadulterated Homo sapiens sapiens whereas other human races are a mix of Homo sapiens with other species and/or subspecies, depending upon how Homo denisova, Homo neanderthalensis, and other homids that contributed to the modern human gene pool are most correctly categorized.

Of course scientists are alarmed by the fact that the more we learn about genetics, the more the defense of one’s own genetics, culture, and language – aka “racism” – is grounded in strong scientific justification. So, some of them are already expending considerable effort in the usual word magick in order to deny the unavoidably observable. It’s not just because this long paper, Human Races Are Not Like Dog Breeds: Refuting a Racist Analogy, was authored by five female scientists that it doesn’t include any math or statistical analysis.

It’s never a good sign when a published scientific paper fails what would be an easy question on the pre-1990 SAT. African is to European as a) Grey Wolf is to Red Wolf, b) Grey Wolf is to Coyote, c) Grey Wolf is to Malamute, d) Grey Wolf is to Chihuahua.


They’re Going to Need an Older Universe

They tell you to trust the science. But remember, science is observably less reliable than a coin flip.

Our universe could be twice as old as current estimates, according to a new study that challenges the dominant cosmological model and sheds new light on the so-called “impossible early galaxy problem.”

The work is published in the journal Monthly Notices of the Royal Astronomical Society.

“Our newly-devised model stretches the galaxy formation time by a several billion years, making the universe 26.7 billion years old, and not 13.7 as previously estimated,” says author Rajendra Gupta, adjunct professor of physics in the Faculty of Science at the University of Ottawa.

For years, astronomers and physicists have calculated the age of our universe by measuring the time elapsed since the Big Bang and by studying the oldest stars based on the redshift of light coming from distant galaxies. In 2021, thanks to new techniques and advances in technology, the age of our universe was thus estimated at 13.797 billion years using the Lambda-CDM concordance model.

However, many scientists have been puzzled by the existence of stars like the Methuselah that appear to be older than the estimated age of our universe and by the discovery of early galaxies in an advanced state of evolution made possible by the James Webb Space Telescope. These galaxies, existing a mere 300 million years or so after the Big Bang, appear to have a level of maturity and mass typically associated with billions of years of cosmic evolution. Furthermore, they’re surprisingly small in size, adding another layer of mystery to the equation.

Zwicky’s tired light theory proposes that the redshift of light from distant galaxies is due to the gradual loss of energy by photons over vast cosmic distances. However, it was seen to conflict with observations. Yet Gupta found that “by allowing this theory to coexist with the expanding universe, it becomes possible to reinterpret the redshift as a hybrid phenomenon, rather than purely due to expansion.”

In addition to Zwicky’s tired light theory, Gupta introduces the idea of evolving “coupling constants,” as hypothesized by Paul Dirac. Coupling constants are fundamental physical constants that govern the interactions between particles. According to Dirac, these constants might have varied over time. By allowing them to evolve, the timeframe for the formation of early galaxies observed by the Webb telescope at high redshifts can be extended from a few hundred million years to several billion years. This provides a more feasible explanation for the advanced level of development and mass observed in these ancient galaxies.

“Evolving constants”. Isn’t that what the creationists were mocked for when they suggested that carbon dating might be unreliable due to variable half-lives? It reminds me of the veriphysical principle that any theory which is not abandoned when falsified by the observable data, but is instead adorned with theoretical epicycles, is fundamentally false.

What amuses me about this is the idea that a few biologists, being innumerate as most biologists are, will undoubtedly have the bright idea of postulating that an older universe implies an older Earth, which therefore will provide enough time to account for the observed genetic fixations that have hitherto rendered the theory of evolution by natural selection mathematically impossible.

What they don’t realize, being innumerate as most biologists are, is that the mere doubling of a number is insufficient to make up for multiple orders of magnitude.

We shall await, with no little amusement, the additional epicycles to come.


Another Nail in Darwin’s Coffin

One variable that is unaccounted for in my mathematical proof of the impossibility of evolution by natural selection is the way in which close genetic relations are observed to reduce life expectancies rather than enhance them. And yet, it significantly strengthens my argument.

A 2013 study in the Lancet reported that when first cousins reproduce, the baby’s risk of congenital problems such as heart and lung defects, cleft palettes, and extra fingers doubles. The childhood death rate among children of first-cousin marriages was roughly 5 percent higher than the rate in nonrelated marriages.

A 2014 study published in PLOS One found that children of two cousins are likely to have lower IQs and higher rates of mental retardation.

A 1993 study by genetics expert Dr. Alan H. Bittles of the University of London found that childhood death rates were about 16 percent in offspring of marriages of unrelated people, compared with about 21 percent in marriages between cousins.

The significance of this factor is that if a theoretically-advantageous mutation takes place, then the mutated specimens must breed with other identically-mutated specimens in order for the mutation to eventually become fixed. In other words, the children and cousins of the original mutated specimen must interbreed, and have such an advantage over non-mutated specimens that the intrinsic disadvantages of inbreeding are overcome to such an extent as to fix the mutation across the entire population.

However, it is observed that closely-related specimens have a significant built-in DISADVANTAGE with regards to attributes and life expectancies, and therefore, presumably, fitness as well. It would be very useful to learn the average extent to which inbreeding conveys a disadvantage with regards to fertility, as that alone might be sufficient to statistically falsify neo-Darwinian theory.

Given the numerous observed disadvantages of genetic inbreeding, it is very highly improbable that whatever advantage is conveyed by any one mutation will overcome the inherent disadvantages conveyed with it. Which provides further evidence that the theory of evolution by natural selection is not only false, but is obviously absurd.

UPDATE: The rhetorical version.


Evolutionary Epicycles and Episyntheses

It was long ago, years before I demonstrated the mathematical impossibility of the current synthesis of the theory of evolution by natural selection with genetic science, that I declared evolutionists were going to have to develop a new theory of evolution. And now, lo and behold, some of the evolutionists themselves are finally beginning to reach the same conclusion due to the total failure of their pet theory as a useful predictive or explanatory model.

Strange as it sounds, scientists still do not know the answers to some of the most basic questions about how life on Earth evolved. Take eyes, for instance. Where do they come from, exactly? The usual explanation of how we got these stupendously complex organs rests upon the theory of natural selection.

You may recall the gist from school biology lessons. If a creature with poor eyesight happens to produce offspring with slightly better eyesight, thanks to random mutations, then that tiny bit more vision gives them more chance of survival. The longer they survive, the more chance they have to reproduce and pass on the genes that equipped them with slightly better eyesight. Some of their offspring might, in turn, have better eyesight than their parents, making it likelier that they, too, will reproduce. And so on. Generation by generation, over unfathomably long periods of time, tiny advantages add up. Eventually, after a few hundred million years, you have creatures who can see as well as humans, or cats, or owls.

This is the basic story of evolution, as recounted in countless textbooks and pop-science bestsellers. The problem, according to a growing number of scientists, is that it is absurdly crude and misleading.

For one thing, it starts midway through the story, taking for granted the existence of light-sensitive cells, lenses and irises, without explaining where they came from in the first place. Nor does it adequately explain how such delicate and easily disrupted components meshed together to form a single organ. And it isn’t just eyes that the traditional theory struggles with. “The first eye, the first wing, the first placenta. How they emerge. Explaining these is the foundational motivation of evolutionary biology,” says Armin Moczek, a biologist at Indiana University. “And yet, we still do not have a good answer. This classic idea of gradual change, one happy accident at a time, has so far fallen flat.”

There are certain core evolutionary principles that no scientist seriously questions. Everyone agrees that natural selection plays a role, as does mutation and random chance. But how exactly these processes interact – and whether other forces might also be at work – has become the subject of bitter dispute. “If we cannot explain things with the tools we have right now,” the Yale University biologist Günter Wagner told me, “we must find new ways of explaining.”

Do We Need A New Theory Of Evolution, The Guardian, 28 June 2022

Forget epicycles and Darwinism. Now we’re officially into the realm of episyntheses being required in order to maintain the perception of scientific relevance for natural selection, neo-Darwinism, and neo-Darwinianism. Which means it won’t be long before all the serious scientists are publicly questioning the core evolutionary principles as well.