In this column, I’ll look at an equally ambitious and closely related claim, that science will absorb other ways of seeing the world, including the arts, humanities and religion. Nonscientific modes of knowledge won’t necessarily
vanish, but they will become consistent with science, our supreme source of truth. The most eloquent advocate of this perspective is biologist Edward Wilson,
one of our greatest scientist-writers.
In his 1998 bestseller Consilience: The Unity of Knowledge, Wilson prophesies that science will soon yield such a compelling, complete theory of nature, including human nature, that “the humanities, ranging from
philosophy and history to moral reasoning, comparative religion, and interpretation of the arts, will draw closer to the sciences and partly fuse with them.” Wilson calls this unification of knowledge “consilience,” an old-fashioned term for coming together
or converging. Consilience will resolve our age-old identity crisis, helping us understand once and for all “who we are and why we are here,” as Wilson puts it.
Dismissing philosophers’ warnings
against deriving “ought” from “is,” Wilson insists that we can deduce moral principles from science. Science can illuminate our moral impulses and emotions, such as our love for those who share our genes, as well as giving us moral guidance. This linkage
of science to ethics is crucial, because Wilson wants us to share his desire to preserve nature in all its wild variety, a goal that he views
as an ethical imperative.
At first glance you might wonder: Who could possibly object to this vision? Wouldn’t we all love to agree on a comprehensive worldview, consistent with science, that tells us how to behave individually and collectively? And in fact.
many scholars share Wilson’s hope for a merger of science with alternative ways of engaging with reality. Some enthusiasts have formed the
Consilience Project, dedicated to “developing a body of social theory and analysis that explains and seeks solutions to the unique challenges we face today.” Last year, poet-novelist Clint Margrave wrote an eloquent
defense of consilience for Quillette, noting that he has “often drawn inspiration from science.”
Another consilience booster is psychologist and megapundit Steven Pinker, who
praised Wilson’s “excellent” book in 1998 and calls for consilience between science and the humanities in his 2018 bestseller
Enlightenment Now. The major difference between Wilson and Pinker is stylistic. Whereas Wilson holds out an olive branch to “postmodern” humanities scholars who challenge science’s objectivity and authority, Pinker scolds
them. Pinker accuses postmodernists of “defiant obscurantism, self-refuting relativism and suffocating political correctness.”
The enduring appeal of consilience makes it worth revisiting. Consilience raises two big questions: (1) Is it feasible? (2) Is it desirable? Feasibility first. As Wilson points out, physics has been an especially potent unifier,
establishing over the past few centuries that the heavens and earth are made of the same stuff ruled by the same forces. Now physicists seek a single theory that fuses general relativity, which describes gravity, with quantum field theory, which accounts for
electromagnetism and the nuclear forces. This is Hawking’s theory of everything and
Steven Weinberg’s “final theory."
The same is true of scientific attempts to bridge the explanatory chasm between matter and mind. In the 1990s, it still seemed possible that researchers would discover how physical processes in the brain and other systems generate
consciousness. Since then, mind-body studies have undergone
a paradigm explosion, with theorists espousing a bewildering variety of models, involving
quantum mechanics,
information theory and
Bayesian mathematics. Some researchers suggest that consciousness pervades all matter,
a view called panpsychism; others insist that the so-called hard problem of consciousness is a pseudoproblem because
consciousness is an “illusion.”
There are schisms even within Wilson’s own field of evolutionary biology. In
Consilience and elsewhere, Wilson suggests that natural selection promotes traits at the level of tribes and other groups; in this way, evolution might have bequeathed us a propensity for religion, war and other social behaviors.
Other prominent Darwinians, notably
Richard Dawkins and
Robert Trivers, reject group selection, arguing that natural selection operates only at the level of individual organisms and even individual genes.
If scientists cannot achieve consilience even within specific fields, what hope is there for consilience between, say, quantum chromodynamics and queer theory? (Actually, in her fascinating 2007 book
Meeting the Universe Halfway: Quantum Physics and the Entanglement of Matter and Meaning,
physicist-philosopher Karen Barad finds resonances between physics and gender politics; but Barad’s book represents the kind of postmodern analysis deplored by Wilson and Pinker.) If consilience entails convergence toward a consensus, science is moving
away from consilience.
So, consilience doesn’t look feasible, at least not at the moment. Next question: Is consilience
desirable? Although I’ve always doubted whether it
could happen, I once thought consilience
should happen. If humanity can agree on a single, rational worldview, maybe we can do a better job solving our shared problems, like
climate change,
inequality,
pandemics and
militarism. We could also get rid of bad ideas, such as the notion that God likes some of us more than others; or that
racial and
sexual inequality and
war are inevitable consequences of our biology.
I also saw theoretical diversity, or
pluralism, as philosophers call it, as a symptom of failure; the abundance of “solutions” to the mind-body problem, like the abundance of
treatments for cancer, means that none works very well. But increasingly, I see pluralism as a valuable, even necessary counterweight to our yearning for certitude. Pluralism is especially important when it comes to our ideas about
who we are, can be and should be. If we settle on a single self-conception, we risk limiting our freedom to reinvent ourselves, to
discover new ways to flourish.
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Wilson acknowledges that consilience is a reductionistic enterprise, which will eliminate many ways of seeing the world. Consider how he treats
mystical visions, in which we seem to glimpse truths normally hidden behind the surface of things. To my mind, these experiences rub our faces in the
unutterable weirdness of existence, which transcends all our knowledge and forms of expression. As William James says in
The Varieties of Religious Experience, mystical experiences should “forbid a premature closing of our accounts with reality.”
Wilson disagrees. He thinks mystical experiences are reducible to physiological processes. In
Consilience, he focuses on Peruvian shaman-artist Pablo Amaringo, whose paintings depict fantastical, jungly visions induced by ayahuasca, a hallucinogenic tea (which I
happen to have taken) brewed from two Amazonian plants. Wilson attributes the snakes that slither through Amaringo’s paintings to natural selection, which instilled an adaptive fear of snakes in our ancestors; it should not be surprising that snakes populate
many religious myths, such as the biblical story of Eden.
Moreover, ayahuasca contains psychotropic compounds, including the potent psychedelic
dimethyltryptamine, like those that induce dreams, which stem from, in Wilson’s words, the “editing of information in the memory banks of the brain” that occurs while we sleep. These nightly neural discharges are “arbitrary in content,” that is, meaningless;
but the brain desperately tries to assemble them into “coherent narratives,” which we experience as dreams.
In this way, Wilson “explains” Amaringo’s visions in terms of evolutionary biology, psychology and neurochemistry. This is a spectacular example of what
Paul Feyerabend, my favorite philosopher and a fierce advocate for pluralism, calls “the tyranny of truth.” Wilson imposes his materialistic, secular worldview on the shaman, and he strips ayahuasca visions of any genuine spiritual significance. While he
exalts biological diversity, Wilson shows little respect for the diversity of human beliefs.
Wilson is a gracious, courtly man in person as well on the page. But his consilience project stems from excessive faith in science, or scientism. (Both Wilson
and Pinker embrace the term scientism, and they no doubt think that the phrase “excessive faith in science” is oxymoronic.) Given the failure to achieve consilience within physics and biology—not to mention the
replication crisis and other problems—scientists should stop indulging in fantasies about conquering all human culture and attaining
something akin to omniscience. Scientists, in short, should be more humble.
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Ironically, Wilson himself questioned the desirability of final knowledge early in his career. At the end of his 1975 masterpiece
Sociobiology, Wilson anticipates the themes of
Consilience, predicting that evolutionary theory plus genetics will soon absorb the social sciences and humanities. But Wilson doesn’t exult at this prospect. When we can explain ourselves in “mechanistic terms,” he warns,
“the result might be hard to accept”; we might find ourselves, as Camus put it, “divested of illusions.”
Wilson needn’t have worried. Scientific omniscience looks less likely than ever, and humans are far too diverse, creative and contrary to settle for a single worldview of any kind. Inspired by mysticism and the arts, as well as by
science, we will keep arguing about who we are and reinventing ourselves forever. Is consilience a bad idea, which we’d be better off without? I wouldn’t go that far. Like utopia, another byproduct of our yearning for perfection, consilience, the dream of
total knowledge, can serve as a useful goad to the imagination, as long as we see it as an unreachable ideal. Let’s just hope we never think we’ve reached it.
This is an opinion and analysis article; the views expressed by the author or authors are not necessarily those of
Scientific American.
Same or Different? The Question Flummoxes Neural Networks
For all their triumphs, AI systems can’t seem to generalize the concepts of “same” and “different.” Without that, researchers worry, the quest to create truly intelligent machines may be hopeless.
The first episode of Sesame Street in 1969 included a segment called “One of These Things Is Not Like the Other.” Viewers were asked to consider a poster that displayed three 2s and one W, and to decide — while singing along to the game’s eponymous jingle — which symbol didn’t belong. Dozens of episodes of Sesame Street repeated the game, comparing everything from abstract patterns to plates of vegetables. Kids never had to relearn the rules. Understanding the distinction between “same” and “different” was enough.
Machines have a much harder time. One of the most powerful classes of artificial intelligence systems, known as convolutional neural networks or CNNs, can be trained to perform a range of sophisticated tasks better than humans can, from recognizing cancer in medical imagery to choosing moves in a game of Go. But recent research has shown that CNNs can tell if two simple visual patterns are identical or not only under very limited conditions. Vary those conditions even slightly, and the network’s performance plunges.
These results have caused debate among deep-learning researchers and cognitive scientists. Will better engineering produce CNNs that understand sameness and difference in the generalizable way that children do? Or are CNNs’ abstract-reasoning powers fundamentally limited, no matter how cleverly they’re built and trained? Whatever the case, most researchers seem to agree that understanding same-different relations is a crucial hallmark of intelligence, artificial or otherwise.
Abstractions navigates promising ideas in science and mathematics. Journey with us and join the conversation.
“Not only do you and I succeed at the same-different task, but a bunch of nonhuman animals do, too — including ducklings and bees,” said Chaz Firestone, who studies visual cognition at Johns Hopkins University.
The ability to succeed at the task can be thought of as a foundation for all kinds of inferences that humans make. Adam Santoro, a researcher at DeepMind, said that the Google-owned AI lab is “studying same-different relations in a holistic way,” not just in visual scenes but also in natural language and physical interactions. “When I ask an [AI] agent to ‘pick up the toy car,’ it is implied that I am talking about the same car we have been playing with, and not some different toy car in the next room,” he explained. A recent survey of research on same-different reasoning also stressed this point. “Without the ability to recognize sameness,” the authors wrote, “there would seem to be little hope of realizing the dream of creating truly intelligent visual reasoning machines.”
Same-different relations have dogged neural networks since at least 2013, when the pioneering AI researcher Yoshua Bengio and his co-author, Caglar Gulcehre, showed that a CNN could not tell if groups of blocky, Tetris-style shapes were identical or not. But this blind spot didn’t stop CNNs from dominating AI. By the end of the decade, convolutional networks had helped AlphaGo beat the world’s best Go player, and nearly 90% of deep-learning-enabled Android apps relied on them.
Getting any machine to learn same-different distinctions may require a breakthrough in the understanding of learning itself.
This explosion in capability reignited some researchers’ interest in exploring what these neural networks couldn’t do. CNNs learn by roughly mimicking the way mammalian brains process visual input. One layer of artificial neurons detects simple features in raw data, such as bright lines or differences in contrast. The network passes these features along to successive layers, which combine them into more complex, abstract categories. According to Matthew Ricci, a machine-learning researcher at Brown University, same-different relations seemed like a good test of CNNs’ limits because they are “the simplest thing you can ask about an image that has nothing to do with its features.” That is, whether two objects are the same doesn’t depend on whether they’re a pair of blue triangles or identical red circles. The relation between features matters, not the features themselves.
In 2018, Ricci and collaborators Junkyung Kim and Thomas Serretested CNNs on images from the Synthetic Visual Reasoning Test (SVRT), a collection of simple patterns designed to probe neural networks’ abstract reasoning skills. The patterns consisted of pairs of irregular shapes drawn in black outline on a white square. If the pair was identical in shape, size and orientation, the image was classified “same”; otherwise, the pair was labeled “different.”
The researchers found that a CNN trained on many examples of these patterns could distinguish “same” from “different” with up to 75% accuracy when shown new examples from the SVRT image set. But modifying the shapes in two superficial ways — making them larger, or placing them farther apart from each other — made the CNNs’ accuracy go “down, down, down,” Ricci said. The researchers concluded that the neural networks were still fixated on features, instead of learning the relational concept of “sameness.”
Last year, Christina Funke and Judy Borowski of the University of Tübingen showed that increasing the number of layers in a neural network from six to 50 raised its accuracy above 90% on the SVRT same-different task. However, they didn’t test how well this “deeper” CNN performed on examples outside the SVRT data set, as Ricci’s group had. So the study didn’t provide any evidence that deeper CNNs could generalize the concepts of same and different.
Guillermo Puebla and Jeffrey Bowers, cognitive scientists at the University of Bristol, investigated in a follow-up study earlier this year. “Once you grasp a relation, you can apply it to whatever comes to you,” said Puebla. CNNs, he maintains, should be held to the same standard.
Puebla and Bowers trained four CNNs with various initial settings (including some of the same ones used by Funke and Borowski) on several variations of the SVRT same-different task. They found that subtle changes in the low-level features of the patterns — like changing the thickness of a shape’s outline from one pixel to two — was often enough to cut a CNN’s performance in half, from near perfect to barely above chance.
What this means for AI depends on whom you ask. Firestone and Puebla think the recent results offer empirical evidence that current CNNs lack a fundamental reasoning capability that can’t be shored up with more data or cleverer training. Despite their ever-expanding powers, “it’s very unlikely that CNNs are going to solve this problem” of discriminating same from different, Puebla said. “They might be part of the solution if you add something else. But by themselves? It doesn’t look like it.”Funke agrees that Puebla’s results suggest that CNNs are still not generalizing the concept of same-different. “However,” she said, “I recommend being very careful when claiming that deep convolutional neural networks in general cannot learn the concept.” Santoro, the DeepMind researcher, agrees: “Absence of evidence is not necessarily evidence of absence, and this has historically been true of neural networks.” He noted that neural networks have been mathematically proved to be capable, in principle, of approximating any function. “It is a researcher’s job to determine the conditions under which a desired function is learned in practice,” Santoro said.
Ricci thinks that getting any machine to learn same-different distinctions will require a breakthrough in the understanding of learning itself. Kids understand the rules of “One of These Things Is Not Like the Other” after a single Sesame Street episode, not extensive training. Birds, bees and people can all learn that way — not just when learning to tell “same” from “different,” but for a variety of cognitive tasks. “I think that until we figure out how you can learn from a few examples and novel objects, we’re pretty much screwed,” Ricci said.
Cells contain machinery that duplicates DNA into a new set that goes into a newly formed cell. That same class of machines, called polymerases, also build RNA messages, which are like notes copied from the central DNA repository of recipes, so they can be read more efficiently into proteins. But polymerases were thought to only work in one direction DNA into DNA or RNA. This prevents RNA messages from being rewritten back into the master recipe book of genomic DNA. Now, Thomas Jefferson University researchers provide the first evidence that RNA segments can be written back into DNA, which potentially challenges the central dogma in biology and could have wide implications affecting many fields of biology.
"This work opens the door to many other studies that will help us understand the significance of having a mechanism for converting RNA messages into DNA in our own cells," says Richard Pomerantz, Ph.D., associate professor of biochemistry and molecular biology at Thomas Jefferson University. "The reality that a human polymerase can do this with high efficiency, raises many questions." For example, this finding suggests that RNA messages can be used as templates for repairing or re-writing genomic DNA.
The work was published June 11th in the journal Science Advances.
Together with first author Gurushankar Chandramouly and other collaborators, Dr. Pomerantz's team started by investigating one very unusual polymerase, called polymerase theta. Of the 14 DNA polymerases in mammalian cells, only three do the bulk of the work of duplicating the entire genome to prepare for cell division. The remaining 11 are mostly involved in detecting and making repairs when there's a break or error in the DNA strands. Polymerase theta repairs DNA, but is very error-prone and makes many errors or mutations. The researchers therefore noticed that some of polymerase theta's "bad" qualities were ones it shared with another cellular machine, albeit one more common in viruses—the reverse transcriptase. Like Pol theta, HIV reverse transcriptase acts as a DNA polymerase, but can also bind RNA and read RNA back into a DNA strand.
In a series of elegant experiments, the researchers tested polymerase theta against the reverse transcriptase from HIV, which is one of the best studied of its kind. They showed that polymerase theta was capable of converting RNA messages into DNA, which it did as well as HIV reverse transcriptase, and that it actually did a better job than when duplicating DNA to DNA. Polymerase theta was more efficient and introduced fewer errors when using an RNA template to write new DNA messages, than when duplicating DNA into DNA, suggesting that this function could be its primary purpose in the cell.
The group collaborated with Dr. Xiaojiang S. Chen's lab at USC and used X-ray crystallography to define the structure and found that this molecule was able to change shape in order to accommodate the more bulky RNA molecule—a feat unique among polymerases.
"Our research suggests that polymerase theta's main function is to act as a reverse transcriptase," says Dr. Pomerantz. "In healthy cells, the purpose of this molecule may be toward RNA-mediated DNA repair. In unhealthy cells, such as cancer cells, polymerase theta is highly expressed and promotes cancer cell growth and drug resistance. It will be exciting to further understand how polymerase theta's activity on RNA contributes to DNA repair and cancer-cell proliferation."
Recently a correspondent of mine raised the issue of whether we should assume that there is 100 percent continuity throughout the tree of life — what is often called “universal common ancestry” (UCA) — until demonstrated otherwise. In the debate over UCA, such a framing would shift the burden of proof to those who claim that there are discontinuities. I get the feeling that UCA-proponents want to put UCA-skeptics into an “extraordinary claims require extraordinary evidence” box, and the idea that there is discontinuity in biology is being implicitly classed as an “extraordinary claim.”
For my part, I think it’s better to approach the data without assumptions and to let the evidence speak for itself. No claim about whether discontinuities exist in the tree of life should be handicapped as “extraordinary,” although I think a good case could be made that UCA is the more “extraordinary” claim. Why? Because all we directly observe today are discrete groups that don’t interbreed (these are like leaves on a tree), while common ancestry (like the branches of the tree) is inferred based upon various methodological justifications. Those methodologies often are inconsistent, contradictory, or exclude non-tree-like data. But I can leave that alone for now. I would say that no viewpoint should be handicapped and both pro-UCA and anti-UCA views should be treated equally. Thus, I reject attempts to frame the issue or shift burdens of proof. Let’s just follow the evidence where it leads.
And What Is That Evidence?
The core postulate of evolutionary biology is “descent with modification.” Crucial to assessing this postulate is the adequacy of evolutionary mechanisms. ID proponents have raised many reasonable mathematical and biological challenges to the adequacy of these mechanisms to account for transitions that are claimed to have occurred in the history of life.
For example, consider ID’s inquiry into waiting times: whale fossils are supposed to provide some of the best examples of “transitional forms” in the fossil record, demonstrating common descent between whales and land mammals. Whale intermediates have become a favorite argument for common descent. I distinctly recall one of my professors teaching us that the evolution of whales happened “incredibly fast” — at an almost unbelievably rapid pace. ID researchers are looking at the genetic changes necessary to transform a land mammal into a whale, and when you apply the mathematics of population genetics to the time available from the fossil record, there simply is not enough time for standard evolutionary mechanisms to produce those genetic changes.
So what ID theorists are doing is showing that standard evolutionary mechanisms are incapable of producing the descent and modifications that are claimed to have taken place. If such an argument would not constitute a mathematically demonstrated discontinuity in the tree of life, I don’t know what would. Our critiques of the mechanisms of evolution give very adequate evidence-based reasons to be skeptical of the tree of life, and to suspect that discontinuities exist.
Koonin and Company
Some leading biologists agree that there is evidence for discontinuity:
Major transitions in biological evolution show the same pattern of sudden emergence of diverse forms at a new level of complexity. The relationships between major groups within an emergent new class of biological entities are hard to decipher and do not seem to fit the tree pattern that, following Darwin’s original proposal, remains the dominant description of biological evolution.
EUGENE V. KOONIN, “THE BIOLOGICAL BIG BANG MODEL FOR THE MAJOR TRANSITIONS IN EVOLUTION,” BIOLOGY DIRECT, 2:21 (AUGUST 20, 2007)
Koonin sees a lot of evidence for “discontinuity” (his word) in the tree of life. Here’s what he writes:
Below I list the most conspicuous instances of this pattern of discontinuity in the biological and pre-biological domains, and outline the central aspects of the respective evolutionary transitions.
1. Origin of protein folds
There seem to exist ~1,000 or, by other estimates, a few thousand distinct structural folds the relationships between which (if existent) are unclear.
2. Origin of viruses
For several major classes of viruses, notably, positive strand RNA viruses and nucleo-cytoplasmic large DNA viruses (NCLDV) of eukaryotes, substantial evidence of monophyletic origin has been obtained. However, there is no evidence of a common ancestry for all viruses.
3. Origin of cells
The two principal cell types (the two prokaryotic domains of life), archaea and bacteria, have chemically distinct membranes, largely, non-homologous enzymes of membrane biogenesis, and also, non-homologous core DNA replication enzymes. This severely complicates the reconstruction of a cellular ancestor of archaea and bacteria and suggests alternative solutions.
4. Origin of the major branches (phyla) of bacteria and archaea
Although both bacteria and archaea show a much greater degree of molecular coherence within a domain than is seen between the domains (in particular, the membranes and the replication machineries are homologous throughout each domain), the topology of the deep branches in the archaeal and, especially, bacterial phylogenetic trees remains elusive. The trees conspicuously lack robustness with respect to the gene(s) analyzed and methods employed, and despite the considerable effort to delineate higher taxa of bacteria, a consensus is not even on the horizon. The division of the archaea into two branches, euryarchaeota and crenarchaeota is better established but even this split is not necessarily reproduced in trees, and further divisions in the archaeal domain remain murky.
5. Origin of the major branches (supergroups) of eukaryotes
Despite many ingenious attempts to decipher the branching order near the root of the phylogenetic tree of eukaryotes, there has been little progress, and an objective depiction of the state of affairs seems to be a “star” phylogeny, with the 5 or 6 supergroups established with reasonable confidence but the relationship between them remaining unresolved.
6. Origin of the animal phyla
The Cambrian explosion in animal evolution during which all the diverse body plans appear to have emerged almost in a geological instant is a highly publicized enigma. Although molecular clock analysis has been invoked to propose that the Cambrian explosion is an artifact of the fossil record whereas the actual divergence occurred much earlier, the reliability of these estimates appears to be questionable. In an already familiar pattern, the relationship between the animal phyla remains controversial and elusive.”
KOONIN, “THE BIOLOGICAL BIG BANG MODEL FOR THE MAJOR TRANSITIONS IN EVOLUTION,” EMPHASES ADDED; CITATIONS OMITTED
Multiple Independent Converging Lines
Koonin ultimately adopts a fairly orthodox evolutionary position, but he certainly shows that those who see discontinuity aren’t unjustified in doing so. I could cite additional evidence for discontinuity between groups. In the 2017 volume Theistic Evolution, I contributed a chapter critiquing UCA by looking at five common lines of evidence. The chapter critiques UCA in a manner that responds to the precise form of the argument that evolutionary biologists make: an argument from multiple independent converging lines of evidence. For example, in a 2010 Nature article, Douglas Theobald writes:
UCA [universal common ancestry] is now supported by a wealth of evidence from many independent sources, including: (1) the agreement between phylogeny and biogeography; (2) the correspondence between phylogeny and the palaeontological record; (3) the existence of numerous predicted transitional fossils; (4) the hierarchical classification of morphological characteristics; (5) the marked similarities of biological structures with different functions (that is, homologies); and (6) the congruence of morphological and molecular phylogenies.
DOUGLAS L. THEOBALD, “A FORMAL TEST OF THE THEORY OF UNIVERSAL COMMON ANCESTRY,” NATURE 465 (MAY 13, 2010): 219-222; EMPHASIS ADDED
A Comprehensive Critique
This is a form of argument found not just in the technical literature but also in many biology textbooks. So in my chapter in Theistic Evolution,titled “Universal Common Descent: A Comprehensive Critique,” I framed a critique of UCA by looking at “evidence from many independent sources” — specifically, biogeography, paleontology, molecular and morphological phylogenies, and embryology. Here’s what I found:
In biogeography, evolutionists appeal to unlikely and speculative explanations where species must raft across vast oceans in order for common descent to account for their unexpected locations.
Paleontology fails to reveal the continuous branching pattern predicted by common ancestry, and the fossil record is dominated by abrupt explosions of new life forms.
Regarding molecular and morphology-based trees, conflicting phylogenies have left the “tree of life” in tatters. Inconsistent phylogenetic methods predict that shared similarity indicates common inheritance, except for when it doesn’t.
Similar inconsistent methodological problems exist in embryology, where significant differences exist between embryos in their early stages, leading evolutionary biologists to predict that similarities will exist between vertebrate embryos, except for when we find differences, and then it predicts those too.
The collective evidence cited above shows that those who believe the tree of life is not 100 percent continuous across all organisms aren’t crazy. Whatever burdens of proof need to be met to have our view taken seriously, we’ve far exceeded them.
At the very least, I think that the framing where the default assumption should be “total continuity” among organisms (UCA) until some “extraordinary evidence” comes along to show otherwise, is not appropriate framing. There’s plenty of evidence of discontinuity in biology, and this alone should allow us to have a real conversation about the data, where both viewpoints can converse on equal footing.
Scientific arguments should be based upon a rhetorical symmetry. If a piece of evidence counts as evidence for a theory, then the opposite should count against it. So if one finds evidence for common descent, then it is necessary that when we find the opposite evidence, then that should count against common descent. And indeed we find much evidence opposite to the predictions of UCA. Those who believe discontinuities have plenty of evidence to back their view. They should not be rhetorically handicapped as they participate in this conversation.
Rabbi Dr. Dovid Gottlieb is a senior faculty member at Ohr Somayach in Jerusalem. An author and lecturer, Rabbi Gottlieb received his Ph.D. in mathematical logic at Brandeis University and later become Professor of Philosophy at Johns Hopkins University. His book Ontological Economy: Substitutional Quantification and Mathematics was published by Oxford in 1980; The Informed Soul was published by Artscroll in 1990, and has recently been reprinted. He is a regular lecturer at kiruv conferences and known for his stimulating and energetic presentations on philosophical issues of Jewish interest.
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