On the NWT's rendering of Matthew 24:3

New World Translation - Parousia ("Presence") (Mt. 24:3)

Some New Testament Greek words, like modern English words, can have more than one meaning. When translating those words one must carefully determine as best as possible which meaning was intended by studying the context. It may still be impossible, at times, to be absolutely certain which meaning was intended. In cases like this it is to be expected that the translators' own understanding and interpretation will decide which meaning to use.

Many translator's understanding and interpretation of Mt. 24:3 is that when speaking of Christ's parousia, the Bible writers meant his visible, physical coming to earth. Therefore, most "orthodox" members of Christendom, want parousia to mean "coming" in all such instances. This does not make it so!

It is readily admitted by all NT Greek scholars that parousia "lit[erally means] a presence, para, with, and ousia, being" - W. E. Vine, p. 201, An Expository Dictionary of New Testament Words. No one disputes this literal meaning of parousia. Even Young's Analytical Concordance gives this as the literal meaning (" a being alongside, presence" - p. 188.)

As J. B. Rotherham (noted Bible scholar and translator of The Emphasized Bible) wrote in the appendix of his translation:

"in this edition the word parousia is uniformly rendered `presence' (`coming,' as a representative of this word, being set aside). .... The sense of `presence' is so plainly shewn by the contrast with `absence' (implied in 2 Co. x.10, and expressed in Ph. ii. 12) that the question naturally arises, - Why not always so render it? The more so, inasmuch as there is in [2 Peter 1:16] also, a peculiar fitness in our English word `presence.' This passage, it will be remembered, relates to our Lord's transformation upon the Mount. The wonderful manifestation there made was a display and sample of "presence" rather than of "coming." The Lord was already there; and being there he was transformed (cp. Mt. xvii. 2)" - p. 271. [Also cf. 2 Pet. 1:16, Young's Literal Translation; and Lattimore's translation]

In other words, context clearly demands "presence" in some instances. And, although context would allow either "presence" or "coming" in other instances, why should a translator insist on "coming" when the word in question does not even mean that in the first place? Rotherham's answer to this question was to render all uses of parousia into "presence" in his translation.

So it is the meaning "coming" that demands a clear need from context to establish its use instead of the more literal "presence"! If context clearly shows that "presence" cannot be properly used, then, and only then, we may properly consider a different meaning. (For more on this subject see The Watchtower, 15 August 1996, pp. 9-14 and Insight on the Scriptures, Vol. 2, article entitled "Presence" - also compare pp. 201 and 1271, W. E. Vine. Also compare the Ref Bible ('84) Appendix 5B on "parousia".)

Posted by Elijah Daniels 

Darwinism's apologists are yet to get the memo re:free lunches.

Evolutionary Computing: The Invisible Hand of Intelligence

Evolution News & Views 

Darwinian evolution is characterized by an utter lack of guidance; it is the "blind watchmaker" of Dawkins. It doesn't know where it's going. It doesn't know it is building a watch. It doesn't know what a watch is. There is no goal, no plan, and no target in "natural evolution." Mutations bounce around against environmental constraints, and whatever happens, happens.

William Dembski and Robert Marks have shown that no evolutionary algorithm is superior to blind search -- unless information is added from an intelligent cause, which means it is not, in the Darwinian sense, an evolutionary algorithm after all. This mathematically proven law, based on the accepted No Free Lunch Theorems, seems to be lost on the champions of evolutionary computing. Researchers keep confusing an evolutionary algorithm (a form of artificial selection) with "natural evolution."

A recent example is seen in a Review paper in Nature by Agoston Eiben and Jim Smith, "From evolutionary computation to the evolution of things," where they claim:

Analogous to natural evolution, an evolutionary algorithm can be thought of as working on two levels. At the higher level (the original problem context), phenotypes (candidate solutions) have their fitness measured. Selection mechanisms then use this measure to choose a pool of parents for each generation, anddecide which parents and offspring go forward to the next generation. At the lower level, genotypes are objects that represent phenotypes in a form that can be manipulated to produce variations (Box 1). Genotype-phenotype mapping bridges the two levels. At the genotypic level, variation operators generate new individuals (offspring) from selected parents. Mutationoperators are based on one parent (asexual reproduction) and randomly change some values. Recombination operators create offspring by combining values from the genotypes of two (or more) parents. Finally, an execution manager controls the overall functioning of the algorithm. It regulates the initialization of the first population, the execution of the selection-variation cycles, and the termination of the algorithm. It also manages the population size (typically kept constant) and other parameters affecting selection and variation. For instance, it determines the number of parents per generation, and whether mutation, recombination or both produce the offspring for a given set of parents. [Emphasis added.]

This is no analogue to natural evolution. Indicators of intelligently guided information saturate this paragraph. We see decisions, operations, choices, management, and regulation. Don't the authors recognize their error?

They know that the two processes (evolutionary computing and natural evolution) are only analogous. They produced a table that compares and contrasts the two (see Table 1, "Main differences between natural evolution and evolutionary algorithms").

It's apparent that they recognize some serious differences between computer evolution and natural evolution. For one thing, evolutionary computation is vastly simpler than the "highly complex biochemical and developmental process" that they know characterizes life. For another, the definitions of fitness, selection, mapping, and other key words in evolutionary computing are so different from those in Darwinian theory that you wonder how the comparisons convey any meaning at all.

What is entirely lost on Eiben and Smith is the most important distinction: intelligent intervention. Everything from hardware to software has been intelligently designed -- including the definition of a problem, the choice of processes and their parameters, and their recognition of what constitutes a solution. To really compare what they do with natural evolution, they would have to stand by a sterile volcano or hot spring with their hands over their eyes, ears, and mouth, and just let things happen. Then, when something happens, say, "Who cares?" That would be Darwinian evolution.

There's no question that intelligently guided "evolutionary computing" has been very successful at solving problems. Marks and Dembski can testify to that from work in their own Evolutionary Informatics Lab. It can be as successful as artificial selection in breeding. Even when random variation is involved, a little intelligent guidance can go a long way. The authors' blindness to this is evident:

Evolution has provided a source of inspiration for algorithm designers since the birth of computers. The resulting field, evolutionary computation, has been successful in solving engineering tasks ranging in outlook from the molecular to the astronomical. Today, the field is entering a new phase as evolutionary algorithms that take place in hardware aredeveloped, opening up new avenues towards autonomousmachines that can adapt to their environment. We discuss how evolutionary computation compares with natural evolution and what its benefits are relative to other computing approaches, and we introduce the emerging area of artificial evolution in physical systems.

By contrast, Marks and Dembski account for the invisible hand required in evolutionary computing. The Lab's website states, "The principal theme of the lab's research is teasing apart the respective roles of internally generated and externally applied information in the performance of evolutionary systems." So yes, systems can evolve, but when they appear to solve a problem (such as generating complex specified information or reaching a sufficiently narrow predefined target), intelligence can be shown to be active. Any internally generated information is conserved or degraded by the law of Conservation of Information.

But is such an idea too "spooky" and thus outside the bounds of science? The website answers that objection:

Simply put, intelligent design, when applied to biology, seems to invoke 'spooky' forms of causation that have no place in science. Evolutionary informatics eliminates this difficulty associated with intelligent design. By looking to information theory, a well-established branch of the engineering and mathematical sciences, evolutionary informatics shows that patterns we ordinarily ascribe to intelligence, when arising from anevolutionary process, must be referred to sources of information external to that process.

What is spooky is failing to see the obvious, believing that information-rich patterns can emerge without intelligence. What is unscientific is explaining an effect without a necessary and sufficient cause, thinking you can get something from nothing.

What Marks and Dembski prove is as scientifically valid and relevant as Gödel's Incompleteness Theorem in mathematics. You can't prove a system of mathematics from within the system, and you can't derive an information-rich pattern from within the pattern. The information in a book, for instance, cannot be derived from the paper and ink used to print it. It's impossible to bootstrap a book from the bare ingredients.

Eiben and Smith, in contrast, see humans as pulling themselves up out of their own evolutionary bootstraps and taking control of evolution:

From a historical perspective, humans have had two roles in evolution. Just like any other species, humans are the product of, and are subject to, evolution. But for millennia (in fact, for about twice as long as we have used wheels) people have also actively influenced the course of evolution in other species -- by choosingwhich plants or animals should survive or mate. Thus humans have successfully exploited evolution to create improved food sources or more useful animals, even though the mechanisms involved in the transmission of traits from one generation to the next were not understood.

Question: Did humans influence, choose, exploit, create, and improve things by intelligent design? Think about that as they continue:

Historically, the scope of human influence in evolution was very limited, being restricted to interfering with selection for survival and reproduction. Influencing other components, such as thedesign of genotypes, or mutation and recombination mechanisms, was far beyond our reach. This changed with theinvention of the computer, which provided the possibility ofcreating digital worlds that are very flexible and much morecontrollable than the physical reality we live in. Together with the increased understanding of the genetic mechanisms behind evolution, this brought about the opportunity to become active masters of evolutionary processes that are fully designed and executed by human experimenters 'from above'.

So ask: Did humans interfere, influence, design, influence, create, control, understand, and take mastery of these things as an intelligent cause?

It could be argued that evolutionary algorithms are not faithful models of natural evolution (Table 1). However, they certainly are a form of evolution. As Dennett said "If you have variation, heredity, and selection, then you must get evolution".

There you go -- a proof by assertion and argument from authority. But can they really dismiss critics so easily? Artificial selection is "a form of evolution," too. What's the difference between that and natural evolution? Intelligence. The breeder makes use of natural processes to obtain the outcome that his designing mind sought. Without intelligent interference, no evolutionary algorithm would be superior to blind search.

In the early days of intelligent design, Thaxton, Bradley, and Olsen recognized the fallacy at work here: the problem of "investigator interference." In their milestone book The Mystery of Life's Origin (1984), they showed the many ways that investigators in the origin-of-life field "played a highly significant but illegitimate role in experimental success." At the end of Chapter 6, they concluded with a quote by Brooks and Shaw:

These experiments... claim abiotic synthesis for what has in fact been produced and designed by highly intelligent and very much biotic man. (p. 110)

The results would never have occurred without the investigators' help. Like puppet masters, they guide outcomes even if some randomness is thrown in. The same applies to evolutionary computing. Eiben and Smith fail to see that the "invisible hands" running the show are their own. And unless they are willing to claim their hands are unguided by any intelligence, they don't establish Darwinian evolution; they confirm intelligent design.

The best explanation for sophisticated engineering remains a skilled engineer II

Cell Vesicles Wear Sophisticated Coats
Evolution News & Views 

Envision a day when self-driving cars make driving obsolete. Now, imagine a far-future day when you don't even have to get in the car. Instead, as you walk out the front door, a car assembles around you, lifts off the ground, floats you to your destination, then disassembles in anticipation of picking up the next passenger. Something like this actually happens in living cells. According to news from the European Molecular Biology Laboratory (EMBL):

Researchers at EMBL Heidelberg have produced detailed images of the intricate protein-coats that surround trafficking vesicles -- the "transport pods" that move material around within biological cells. The study, published today in Science, provides a new understanding of the complex machines that make up the cells' logistics network.

Vesicles are responsible for transporting molecules between the different compartments within a cell and also for bringing material into cells from outside. There are several types of vesicle: each has a specific type of coat which is made up of different proteins and assembles onto a membrane surrounding the vesicle. [Emphasis added.]

There are three models of "transport pods" that molecular biologists know about, each with its own specific coat proteins: Coat Protein 1 (COPI), Coat Protein 2 (COPII), and clathrin-coated vesicle (CCV). Each coat has its own proteins, adaptors, and functions. The first paper in Science looks in detail at COPI; but first, let's mention COPII. This type of vesicle takes proteins from the endoplasmic reticulum (ER), where they were assembled, to the Golgi apparatus where they will be packaged for delivery. This is called anterograde (forward) transport.

COPI is the reverse; it takes proteins from the Golgi back to the ER, or to different compartments of the Golgi. This is called retrograde (backward) transport. Surprisingly, the coat proteins on these vesicles are very different. COPII coats are made of four proteins that assemble with four-fold symmetry in a sequential manner, using separate adaptor proteins. COPI is more complicated. It has seven discrete proteins that come together simultaneously, forming complexes with triangular symmetry that include the adaptor function (i.e., allowing the complex to attach to the vesicle membrane).

The EMBL researchers pushed the envelope of cryoelectron microscopy to determine the nature of the "triads" called coatomers that make up the coat. They found that the seven proteins form two complexes that overlap into a layer 14 nanometers (nm) thick -- a substantial fraction of the typical 100-nm-diameter vesicle. A Perspective article in the same issue of Science says there's still a lot to learn about these coats: "it remains to be determined what specific roles these conformations play in the respective coat functions," Noble and Stagg write. What is known is that the coatomer triads make contact with up to four neighboring triads. This gives them structural flexibility that is distinct from the other coated vesicle types. The authors of the paper speculate about the reasons for this:

In existing models for clathrin and COPII vesicle coats, multiple identical subunits each make the same set of interactions with the same number of neighbors. Structural flexibility allows formation of vesicles from different total numbers of subunits. Based on these principles, both clathrin-like and COPII-like models have been proposed for the assembled COPI coat. We found instead that assembled coatomer can adopt different conformations to interact with different numbers of neighbors. By regulating the relative frequencies of different triad patterns in the COPI coat during assembly -- for example, by stabilizing particular coatomer conformations -- the cell would have a mechanism to adapt vesicle size and shape to cargoes of different sizes.

The paper includes color models and two motion animations of how the proteins fit together, protecting the cargo as it rides to its destination from organelle to organelle.

Clathrin Coats

A better-understood protein coat is made of clathrin. The name comes from a Latin word for lattice. Individual clathrin molecules, made of 3 heavy chains and 3 light chains, look like a three-spoked pinwheel called a triskelion. They fit together beautifully around the vesicle into a cage-like structure that resembles a geodesic dome. A beautiful animation from Harvard Medical School shows how numerous other proteins work with clathrin to form the vesicle coat and disassemble it after use, so the triskelia can be recycled. The vesicles can import and export molecules to the exterior of the cell or transport them within the cytoplasm. Clathrin proteins are also implicated in cell division, where they assist in arranging chromosomes on the spindle.

The animation will need an update, because something new was reported about clathrin-coated vesicles (CCV) and the pits (CCP) that form when the membrane invaginates to bring cargo in from outside. Another EMBL team, also reporting in Science, found that clathrin is more gymnastic than previously recognized.

Unlike as shown in the animation, the clathrin lattice forms flat on the inner membrane surface before invagination begins. Then, as the membrane folds inward, the lattice stretches and reconfigures itself, maintaining the same surface area but following the shape of the vesicle as it elongates. With its cargo safely inside, the vesicle pinches off and forms a sphere. The press release from EMBL expresses the surprise at the shape changes:

John Briggs, senior scientist at EMBL Heidelberg, said: "Our results were surprising, because the proteins have to undergo some complicated geometric transformations to go from a flat to a curved shape, which is why the second model was favoured by scientists for such a long time."

(The "second model", now falsified, refers to the idea that "clathrin assembles directly, assuming the shape of the membrane as it is drawn inwards.") The paper describes how the growing cage must change its geodesic structure as the vesicle forms:

In order to bend, flat lattices composed primarily of hexagons must acquire pentagons requiring extensive molecular rearrangements and removal of triskelia.

Why would the cell perform this more difficult gymnastic routine? The final paragraph offers some possible reasons:

Recruitment of clathrin before membrane bending provides a flat, dynamic array as a platform for cargo recruitment. This implies that the membrane to be internalized and the size of the future vesicle are not determined by clathrin geometry during assembly into a curved cage but rather are selected before invagination during cargo recruitment. Rapid clathrin exchange is consistent with a dynamically unstable lattice -- dynamic instability is a common property within networks of low-affinity protein interactions. It would allow for stochastic abortion of sites that initiate but fail to cross a growth- or cargo-mediated checkpoint before investing energy in membrane bending. During invagination, further exchange would allow clathrin reorganization and bending of the lattice into a defined cage that requires active disassembly.

One thing not mentioned in the articles is the rapidity of vesicle formation and disassembly. Suffice it to say that clathrin-coated endocytosis and exocytosis occur at the tips of nerve cells, where electrical signals must cross synapses. The vesicles form at one nerve, cross the synapse carrying the cargo, and are taken in by the next nerve cell in line. How long does it take your brain to feel pain from a stubbed toe? A lot of CCVs formed, crossed synapses, and disassembled in that very quick response!

Evolution or Design?

As usual, the articles and papers say very little about evolution. If mentioned at all, it was about the lack of evolution: e.g., "The archetypal protein coats COPI, COPII, and clathrin are conserved from yeast to human." Only the Perspective piece by Noble and Stagg ventures further:

Individual proteins in the three different coat protein complexes share similar folds and are proposed to be distant evolutionary relatives. Despite these similarities, the coats have evolved different functional mechanisms....

One possibility is that the proto-COPI coat evolved the four different linkages to expand the repertoire of geometries that the coat can accommodate and thus adapt to the secretory needs of the cell.

These suggestions amount to little more than after-the-fact assertions of evolutionary belief. One cannot invoke a blind, unguided process to say that it "evolved to" meet the needs of the cell. Darwinian natural selection has no foresight.

The complexity of these coats, and the accessory proteins that build them, attach them to vesicles and disassemble them, defy unguided evolutionary explanations. They exhibit irreducible complexity; they don't work unless all the protein parts are present simultaneously. They exhibit beauty in the way they organize into geometric shapes. The shapes, in turn, are dictated by digital codes in the genome that produce sequences that fold into building blocks. These building blocks, like the triskelion of clathrin, have no knowledge of the elegant geodesic domes that they will be fitted into. The triskelia are also blind to their attachment points that will be used by two other proteins that will disassemble the vesicle.

We see only glimpses of structures we don't yet fully understand. Why are separate coats needed for the three types of transport? What types of vesicles need the different coats? What specific advantages do the different coats provide for transport in one direction and not the other? What molecules need coated vesicles opposed to uncoated vesicles? What function does each protein in the coat provide?

Further research at higher resolution will undoubtedly yield more knowledge about vesicular transport. One thing is clear so far; the elegance of these systems, their ability to reshape their geometry as they grow, their adaptability to cargoes of many sizes, the rapidity of their action, and their conservation from yeast to humans all proclaim, "design!"

On irreconcilable differences between mathematics and Darwinism?

Surprise! There’s no satisfactory mathematical model for macroevolution, at the present time.

In 2006, Professor Allen Macneill acknowledged that macroevolution is not mathematically modelable in the way that microevolution is. He could have meant that macroevolution is not mathematically modelable at all; alternatively, he may have simply meant that macroevolutionary models are not as detailed as microevolutionary models. If he meant the latter, then I would ask: where’s the mathematics that explains macroevolution? Surprisingly, it turns out that there is currently no adequate mathematical model for Darwinian macroevolution. Professor James Tour’s remark that “The Emperor has no clothes” is spot-on.

Evolutionary biology has certainly been the subject of extensive mathematical theorizing. The overall name for this field is population genetics, or the study of allele frequency distribution and change under the influence of the four main evolutionary processes: natural selection, genetic drift, mutation and gene flow. Population genetics attempts to explain speciation within this framework. However, at the present time, there is no mathematical model – not even a “toy model” – showing that Darwin’s theory of macroevolution can even work, much less work within the time available. Darwinist mathematicians themselves have admitted as much.

In 2011, I had the good fortune to listen to a one-hour talk posted on Youtube, entitled, Life as Evolving Software. The talk was given by Professor Gregory Chaitin, a world-famous mathematician and computer scientist, at PPGC UFRGS (Portal do Programa de Pos-Graduacao em Computacao da Universidade Federal do Rio Grande do Sul.Mestrado), in Brazil, on 2 May 2011. I was profoundly impressed by Professor Chaitin’s talk, because he was very honest and up-front about the mathematical shortcomings of the theory of evolution in its current form. As a mathematician who is committed to Darwinism, Chaitin is trying to create a new mathematical version of Darwin’s theory which proves that evolution can really work. He has recently written a book, Proving Darwin: Making Biology Mathematical (Random House, 2012, ISBN: 978-0-375-42314-7), which elaborates on his ideas.

Here are some excerpts from Chaitin’s talk, part of which I transcribed in my post, At last, a Darwinist mathematician tells the truth about evolution (November 6, 2011):

I’m trying to create a new field, and I’d like to invite you all to leap in, join [me] if you feel like it. I think we have a remarkable opportunity to create a kind of a theoretical mathematical biology…

So let me tell you a little bit about this viewpoint … of biology which I think may enable us to create a new … mathematical version of Darwin’s theory, maybe even prove that evolution works for the skeptics who don’t believe it…

I don’t want evolution to stagnate, because as a pure mathematician, if the system evolves and it stops evolving, that’s like it never evolved at all… I want to prove that evolution can go on forever…

OK, so software is everywhere there, and what I want to do is make a theory about randomly evolving, mutating and evolving software – a little toy model of evolution where I can prove theorems, because I love Darwin’s theory, I have nothing against it, but, you know, it’s just an empirical theory. As a pure mathematician, that’s not good enough…

… John Maynard Smith is saying that we define life as something that evolves according to Darwin’s theory of evolution. Now this may seem that it’s totally circular reasoning, but it’s not. It’s not that kind of reasoning, because the whole point, as a pure mathematician, is to prove that there is something in the world of pure math that satisfies this definition – you know, to invent a mathematical life-form in the Pythagorean world that I can prove actually does evolve according to Darwin’s theory, and to prove that there is something which satisfies this definition of being alive. And that will be at least a proof that in some toy model, Darwin’s theory of evolution works – which I regard as the first step in developing this as a theory, this viewpoint of life as evolving software….

…I want to know what is the simplest thing I need mathematically to show that evolution by natural selection works on it? You see, so this will be the simplest possible life form that I can come up with….

The first thing I … want to see is: how fast will this system evolve? How big will the fitness be? How big will the number be that these organisms name? How quickly will they name the really big numbers? So how can we measure the rate of evolutionary progress, or mathematical creativity of my little mathematicians, these programs? Well, the way to measure the rate of progress, or creativity, in this model, is to define a thing called the Busy Beaver function. One way to define it is the largest fitness of any program of N bits in size. It’s the biggest whole number without a sign that can be calculated if you could name it, with a program of N bits in size….

So what happens if we do that, which is sort of cumulative random evolution, the real thing? Well, here’s the result. You’re going to reach Busy Beaver function N in a time that is – you can estimate it to be between order of N squared and order of N cubed. Actually this is an upper bound. I don’t have a lower bound on this. This is a piece of research which I would like to see somebody do – or myself for that matter – but for now it’s just an upper bound. OK, so what does this mean? This means, I will put it this way. I was very pleased initially with this.

Table:
Exhaustive search reaches fitness BB(N) in time 2^N.
Intelligent Design reaches fitness BB(N) in time N. (That’s the fastest possible regime.)
Random evolution reaches fitness BB(N) in time between N^2 and N^3.

This means that picking the mutations at random is almost as good as picking them the best possible way…

But I told a friend of mine … about this result. He doesn’t like Darwinian evolution, and he told me, “Well, you can look at this the other way if you want. This is actually much too slow to justify Darwinian evolution on planet Earth. And if you think about it, he’s right… If you make an estimate, the human genome is something on the order of a gigabyte of bits. So it’s … let’s say a billion bits – actually 6 x 10^9 bits, I think it is, roughly – … so we’re looking at programs up to about that size [here he points to N^2 on the slide] in bits, and N is about of the order of a billion, 10^9, and the time, he said … that’s a very big number, and you would need this to be linear, for this to have happened on planet Earth, because if you take something of the order of 10^9 and you square it or you cube it, well … forget it. There isn’t enough time in the history of the Earth … Even though it’s fast theoretically, it’s too slow to work. He said, “You really need something more or less linear.” And he has a point…

Professor Chaitin’s point here is that if even a process of intelligently guided evolution takes, say, one billion years (1,000,000,000 years) to reach its goal, then an unguided process of cumulative random evolution (i.e. Darwin’s theory) will take one billion times one billion years to reach the same goal, or 1,000,000,000,000,000,000 years. That’s one quintillion years. The problem here should be obvious: the Earth is less than five billion years old, and even the universe is less than 14 billion years old.

May the Mainstream media R.I.P:Pros and cons.

File under "well said" LIII

To suppress free speech is a double wrong. It violates the rights of the hearer as well as those of the speaker. Frederick Douglass

Engineerless engineering?

Nature’s Amazing Machines — Denver Looks at the Marvels of “Natural Engineering”
Steve Laufmann  

The Denver Museum of Nature and Science is running an excellent special exhibition featuring examples of the amazing engineering observed in biology. The DMNS  website  captures the gist of it:

Nature’s Amazing Machines uses real objects, scientific models, and fun activities to show the marvels of natural engineering.

The exhibit focuses on six functional domains observed in living systems (though there are many, many more they could have chosen): Legs and Springs, Wings and Fins, Jaws and Claws, Structures and Materials, Pumps and Pipes, and Insulators and Radiators.

These amazing machines truly are “marvels.” As I pondered the displays, I was struck by just how nearly perfect these natural machines are — from basic design to operational efficiency to various classes of optimizations.

In previous articles (here and here) I’ve tried to make exactly this point. Life requires exquisitely engineered systems. And now DMNS has stepped up to provide dozens of examples. My thanks to them for their timely (and unwitting) support!

The exhibit incorporates many examples of biomimetics, where human engineers have co-opted the designs of living systems. Like Velcro, which was inspired by the burrs of plants. (For the youngsters, note that this kind of co-option is generally patentable, too! … a good way to generate income that you can use to take care of your parents when they get old.)

As you’d expect, the exhibit includes the requisite references to evolution, but these references are descriptive rather than explanatory. Darwinian evolution is simply an assumption underlying the exhibit, with no attempt at further explanations.

They call this natural engineering. This is an interesting term. Presumably they mean that evolution can engineer amazing machines entirely by accident. So it’s possible to get engineering without an engineer — systems engineering performed entirely by natural forces with no intentionality, plan, or purpose. (We should note, as a counterpoint, that the known forces of nature are mainly working to kill every living thing — to achieve equilibrium, aka death — so there must be some as-yet-undiscovered natural force capable of doing these things.)

How does DMNS know that natural engineering can do such things? Nowhere in the exhibit is this question asked, nor is it answered.

Since DMNS doesn’t provide much by way of explanation, you’ll need to add your own. This is a good opportunity to discuss these issues with your kids and your friends. There’s no shortage of questions to be asked, and this is good practice in learning to ask both the obvious and the not-so-obvious questions. For example:

Examine the complexities in the amazing machines featured in the exhibit, not just at the top level, but the underlying mechanisms that must be there to make them work.

How many parts are required, in all the right places, with all the right properties, connected in all the right ways, to achieve the end functions of these machines?

How specifically must they be arranged and interconnected to achieve their function(s)?

How much information is needed to generate all those parts, from base information, to assembly instructions, to the correct parameters for sizing, fit, and capacities?

How precisely must these be fine-tuned in order to successfully operate?

How can natural engineering create such amazing marvels? What natural forces could possibly do all the work required to generate such systems?

How can such finely tuned systems come to exist when so many parts are needed in order to achieve even minimal functionality?

How many tries does it take to get all that stuff right? How many tries does a living organism get when one of its systems doesn’t function effectively?

Is it possible for such systems to arise gradually? If so, how?

Does anything in this exhibit explain how any of this could happen?

If not, why not? Was it omitted simply because the kids might not understand it?

Are these machines more likely to be caused by accident or by design? Through purposelessness or intention?

Why is it that in any other domain of knowledge, the answer would be obvious (design), whereas in biology this answer is simply not allowed?

Each of us is faced with a decision — whether such purposeful outcomes could possibly be purposeless, or whether they are exactly what they look like — the intentional designs of an awesome (and innovative and powerful and detail-oriented) engineer.

Maybe it’s just me, but it seems hard not to see teleology throughout this exhibit.

If you find yourself in the Denver area this fall, make a point of taking in this exhibit. It focuses mainly on high-level designs that will make sense to everyone, including the kids, using hands-on exhibits to make its points. It’s nicely presented, and a great way to spend a couple of hours. It’s free with general admission to DMNS, which includes many other displays that will provide yet more fodder for explaining the mysterious and wonderful design of our world. Organized youth groups get an especially good deal on admission. No reservations are required. Through January 1, 2018.

From healers to hitmen? II

Michael Egnor: How Assisted Suicide Corrupts Medicine and Medical Doctors
David Klinghoffer | @d_klinghoffer  

“A doctor killing a patient is analogous to a pilot deliberately crashing a plane.” So says neurosurgeon and Evolution News contributor Michael Egnor in a conversation with biologist Ray Bohlin.

It’s a brilliant comparison, in both parts of which a trained professional turns his expertise to the exact opposite purpose it was intended to serve. There are huge manuals stacked upon manuals for physicians on how to avoid killing patients, just as pilots are drilled to expose airplane passengers to minimum danger of falling from the sky.

In a new ID the Future episode, Egnor argues that if judges, legislators, or other advocates support killing patients under certain circumstances, then fine, let them do the killing. But don’t corrupt the medical profession on which we all rely.

As Dr. Egnor acutely notes, the point of involving doctors appears to be a non-medical one – putting a pretty, sanitary seal of approval on a heinous deed, in which MDs are really unneeded. It’s not for the patient’s good. It’s for the rest of us, to ease our conscience in something we know is wrong.  Listen to the podcast here, or download it here.

A castle in the clouds?

Information Storage — In the Cloud(s)
Evolution News | @DiscoveryCSC 

In the second-released Star Wars film, The Empire Strikes Back (1980), a “cloud city” of magnificent structures, filled with active intelligent beings, was portrayed floating in the atmosphere of the giant planet Bespin. If we can re-portray it down a few orders of magnitude, something like that exists right here at planet Earth: whole ecosystems of machinery, active structures, and complex ecosystems in the droplets of clouds.

It’s surprising no one ever checked this before in detail. There were hints that microbes could become airborne or “aerosolized” and take flight in the clouds, but how many are there? What types regularly inhabit cloud droplets? How do they survive, and what do they do? A team of nine from CNRS, the National Center for Scientific Research in France, decided to find out. Their results, published in PLOS ONE, could initiate a whole new science of global “cloud ecology,” the results of which can only be imagined.

Within the atmospheric system, clouds are genuine atmospheric interfaces with the ground: they physically connect high altitudes with the surface by being to a large extent at the origin of wet deposition of aerosols, including microorganisms. Cloud water is a complex mixture of soluble gas and particles dissolved into millions of micron-sized water droplets, and forming very reactive and dynamic systems…. As non-soluble biological particles, some microorganisms can physically impact clouds by acting as embryos for the formation of water droplets and ice crystals, with subsequent impacts on hydrological cycles. Observations of microbiological features in fog and clouds raised the possibility that these also represent habitats for microorganisms, where they would actively take part in the chemical reactivity through metabolic activity and nutrient utilization. So far these active inhabitants of clouds remain largely unknown. 

What they found was truly astonishing, calling to remembrance Leeuwenhoek’s first observation in 1665 of a world of microbes in a drop of water. Now, some 350 years later, science has discovered another unseen world of living creatures. In 2013, the team collected three samples from a mountaintop in France in sterile collectors, quickly flash-freezing the material for later analysis. It’s taken a long time to search through the genomes of microbes, because there were so many of them. DNA and RNA sequencing allowed them to make these initial determinations:

Here, microbial communities in cloud water collected at puy de Dôme Mountain’s meteorological station (1465 m altitude, France) were fixed upon sampling and examined by high-throughput sequencing from DNA and RNA extracts, so as to identify active species among community members. Communities consisted of ~103−104 bacteria and archaea mL-1 and ~102−103 eukaryote cells mL-1. They appeared extremely rich, with more than 28,000 distinct species detected in bacteria and 2,600 in eukaryotes. Proteobacteria and Bacteroidetes largely dominated in bacteria, while eukaryotes were essentially distributed among Fungi, Stramenopiles and Alveolata. Within these complex communities, the active members of cloud microbiota were identified as Alpha- (Sphingomonadales, Rhodospirillales and Rhizobiales), Beta- (Burkholderiales) and Gamma-Proteobacteria (Pseudomonadales). These groups of bacteria usually classified as epiphytic are probably the best candidates for interfering with abiotic chemical processes in clouds, and the most prone to successful aerial dispersion.

This could change your cloud viewing forever. Up there in those drifting puffs of white, tens of thousands of complex organisms live in cloud cities! There are a hundred to a thousand eukaryotic cells per milliliter, and a thousand to ten thousand bacteria and archaea. These numbers vastly exceed cell counts from previous observations. “Clouds are extremely rich and diverse mosaics of multiple sources ecosystems,” the researchers say.

What are the microbes doing up there? Well, as the authors indicated, they modify the weather. They can act as embryos for the formation of water droplets (think rain) and ice crystals (think snow and sleet). In a real sense, they are natural cloud seeders that can influence the life of the rest of the organisms on earth. Now there’s a good science project for someone in the tradition of Michael Denton and Privileged Species: To what extent is weather regulated by the presence or absence of microbes in the clouds?

Another thing they do is migrate. Catching the cloud trains in the sky, microbes can distribute themselves around the globe. Since all multicellular organisms (including humans) carry numerous microbes around with them, this could me a means of ensuring beneficial microbes are available in every habitat. Of course, it cannot rule out the spread of pathogens, too, but those represent a small fraction of microbes as a whole.

A survey of this type cannot hope to find all the functions of the cloud-city ecosystem, and the authors admit there’s a lot to learn:

Their identification certainly helps understanding the atmosphere as a habitat; it will also allow focusing researches for evaluating microbial impact on cloud physical and chemical processes, but their actual functioning, the “what do they do?” question remains to be answered.

Nevertheless, the authors suspect that these ecosystems engage in significant functions. They speak of the “global functioning of the community” and describe their interactions as a system:

If an abundant group was to be lost from the community, i.e. a group that is likely to contribute significantly to the structure and global functioning of the system, there would be a high probability to lose or reduce also the functions associated with it. This ecological theory, that functional stability implies even structure, derives from established ecosystems and it is applied here for apprehending the functioning of cloud’s microbial communities in the frame of clouds as microbial habitats hypothesis; it is possible though that this is not applicable to environments acting mainly as transport areas, where microbial establishment is by essence not possible, like clouds.

Transport areas can be places of function. Business meetings are held on cruise ships. People interact (sometimes) on subway trains. As long as the cloud community in a droplet of water has the resources it needs, it could carry on whatever functions it is capable of, the nature and extent of which remain to be discovered.

A big question for design advocates might concern whether microbes are necessary for habitability. Microbes may not be necessary for weather (Cassini scientists, for instance, inferred that cloudbursts occur on Saturn’s moon Titan), but perhaps microbes regulate the climate of a planet in some way. It’s too early to predict exactly what functions of this newly discovered “system” will present to the health of the planet, but it surely looks promising. Maybe astrobiologists should not rush to declare exoplanets habitable till the “global functioning of the community” of microbes is better understood.

For the time being, though, we can certainly marvel at the fact that what appeared to be largely a domain of lifeless dust and water up there turns out to be perfused with huge amounts of complex specified information: the genetic codes of tens of thousands of organisms. It wouldn’t be surprising to learn that all that information is there for a purpose. We encourage design-friendly scientists to take this information and extend our understanding into the next big question: “What do they do?”

Runaway Scientism?

The Multiverse Is Science’s Assisted Suicide
Denyse O'Leary  

In 2015, Wired told us that physicists were desperate to be wrong  about the Higgs boson. They yearned to push the Standard (Big Bang) Model of the universe “in new directions.” But the unmindful particle “acted just like the model said it would act, obeyed every theorized rule.”

In the silence that followed, asking for evidence for these physicists’ proposed infinity of universes (the multiverse) felt like assaulting a victim’s feelings. At the Guardian, Stuart Clark later informed us that “Brexit and Trump are nothing compared to the alternate universes some astronomers are contemplating.” Really? Regional political upsets vie with a multiverse?

Astronomers, Clark tells us, pin their hopes on the Cold Spot, a cool patch of space from the early universe: “We can’t entirely rule out that the Spot is caused by an unlikely fluctuation explained by the standard theory. But if that isn’t the answer, then there are more exotic explanations.” Indeed. There are more exotic explanations for almost anything.

Eugene Lim insisted at The Conversation in 2015 that parallel universes are science: “Whether we will ever be able to prove their existence is hard to predict. But given the massive implications of such a finding it should definitely be worth the search.” Very well, but some people research ghosts on the same basis. What makes the multiverse quest “science” but the ghost hunt “anti-science,” once evidence no longer matters as much as it used to?

Cosmologists sense the problem and strive to rescue their multiverse from the nagging demands for evidence. Pop science media offer a window into major trends.

One is cosmic Darwinism. Lee Smolin has advocated a cosmic version of Darwinian natural selection in which the most common universes will be those most suitable for producing black holes, as our universe does. Is Darwinism the cause? In “The Logic and Beauty of Cosmological Natural Selection” (Scientific American, 2014), Lawrence Rifkin admitted that the main problem with the hypothesis is lack of direct evidence:

But keep in mind that from a direct evidence perspective, cosmological natural selection is no worse off at this point than proposed scientific alternatives. There is no direct evidence that universes are created by quantum fluctuations in a quantum vacuum, that we live in a multiverse, that there is a theory of everything, or that string theory, cyclic universes or- brane cosmology even exist.

Then why should we not set all such speculations aside? There is no obvious need for hurry.

Darwinism, as in natural selection acting on random mutations, is a theory developed by Darwin and his followers to account for complex, specified information in life forms on this planet. Whether it is correct or not when used as intended, if it is applied to an undetected multiverse, it becomes philosophy (metaphysics).

An anecdote suffices.  Michael Egnor has observed here, philosopher Joseph P. Carter told us in the New York Times that the universe does not care about purpose. Evolutionary psychologist Michael E. Price disputes that view at ,Psychology Today insisting that in a multiverse natural selection can create purpose. His position is denied by most of natural selection’s advocates in biology. But, riffing on Smolin, Price explains that “life is more likely than black holes (or anything else) to be a mechanism of universe replication.” If this kind of ungrounded assertion is the best naturalism can do for us now, why do we encourage it?

Physicist Ethan Siegel counsels at Forbes that we must not “doubt the Multiverse’s existence without considering the very good, scientific reasons that motivate it.” But “very good scientific reasons” are precisely what we lack, unless the term “scientific reasons” now includes immunity to “experimental and observational tests.”  Similarly, physicist Brian Cox told us in 2016 that the “idea of multiverses is not too big a leap” from cosmic inflation. But he is dealing with leaps of the imagination, not of physics discoveries.

Earlier this year, skeptical mathematician Peter Woit fretted with science writer  John Horgan at Scientific American, “The problem with such things as string-theory multiverse theories is that ‘the multiverse did it’ is not just untestable, but an excuse for failure.” Commenting elsewhere on Zeeya Merali’s  A Big Bang in a Little Room (2017),  he noted that she contemplates “the possibility that “string theory and inflation may be conspiring against us in such a way that we may never find evidence for them, and just have to trust in them as an act of faith.” He would  describe it as “a scientifically worthless idea.”

With a clash of world views, where to begin? Woit and Horgan assume that post-modern science is a quest to understand reality, just as traditional science has been. It is not.

For many people today, post-modern science is more of a quest to express an identity as believer in science, irrespective of evidence. Cosmologist Paul Steinhardt  got a sense of this  in 2014, when he reported that some proponents of early rapid cosmic inflation “already insist that the theory is equally valid whether or not gravitational waves are detected.” It fulfilled their needs. In 2017, cosmologist George Ellis, long a foe of post-modern cosmology, summed it up: “Scientific theories have since the seventeenth century been held tight by an experimental leash. In the last twenty years or so, both string theory and theories of the multiverse have slipped the leash.”

We have so much more data now. But it provides no evidence for a multiverse. That’s nothing unusual historically (think phlogiston and ether for great ideas that did not work). We used to just adjust. But today, increasing numbers of science-minded people demand a post-modern science that adapts to their needs.  After all, we evolved to survive and pass on our genes, not to understand reality.

As a result, many cosmologists and science writers speak as if the multiverse merely awaits routine administrative clearance to morph into textbook science, absent evidence. Characteristically, they see themselves as fighting a conservative (fuddy-duddy) establishment which clings to a role for mere evidence.

Fine tuning of our planet and our universe for life sets limits on mere belief by challenging us to calculate probabilities. The multiverse is deeply attractive by comparison because it dissipates evidence. It conjures unimaginably infinite, unproven, and incalculable probabilities. As New Scientist  puts it, “We merely inhabit one out of the infinite selection.” That feels so right just now.

The multiverse has only ever existed, so far as we know, in the mind of man. Its most promising research programs, string theory and early rapid cosmic inflation theory, have bounced along on enthusiasm alone, prompting ever more arcane speculations for which there may never be any possibility of evidence.

But like so many other empty ideas, the multiverse has consequences. If we accept it, we abandon the view that science deals with the observed facts of nature. We adopt the view that it tells us what we want to believe about ourselves. In other words, the multiverse is science’s assisted suicide.