Thursday, August 8, 2019


10 differences between artificial intelligence and human intelligence


Today I want to tell you what is artificial about artificial intelligence. There is of course, the obvious, which is that the brain is warm, wet, and wiggly, while a computer is not. But more importantly, there are structural differences between human and artificial intelligence, which I will get to in a moment.

Before we can talk about this though, I have to briefly tell you what “artificial intelligence” refers to.

What goes as “artificial intelligence” today are neural networks. A neural network is a computer algorithm that imitates certain functions of the human brain. It contains virtual “neurons” that are arranged in “layers” which are connected with each other. The neurons pass on information and thereby perform calculations, much like neurons in the human brain pass on information and thereby perform calculations.

In the neural net, the neurons are just numbers in the code, typically they have values between 0 and 1. The connections between the neurons also have numbers associated with them, and those are called “weights”. These weights tell you how much the information from one layer matters for the next layer.

The values of the neurons and the weights of the connections are essentially the free parameters of the network. And by training the network you want to find those values of the parameters that minimize a certain function, called the “loss function”.

So it’s really an optimization problem that neural nets solve. In this optimization, the magic of neural nets happens through what is known as backpropagation. This means if the net gives you a result that is not particularly good, you go back and change the weights of the neurons and their connections. This is how the net can “learn” from failure. Again, this plasticity mimics that of the human brain.

For a great introduction to neural nets, I can recommend this 20 minutes video by 3Blue1Brown.

Having said this, here are the key differences between artificial and real intelligence.

1. Form and Function

A neural net is software running on a computer. The “neurons” of an artificial intelligence are not physical. They are encoded in bits and strings on hard disks or silicon chips and their physical structure looks nothing like that of actual neurons. In the human brain, in contrast, form and function go together.

2. Size

The human brain has about 100 billion neurons. Current neural nets typically have a few hundred or so.

3. Connectivity

In a neural net each layer is usually fully connected to the previous and next layer. But the brain doesn’t really have layers. It instead relies on a lot of pre-defined structure. Not all regions of the human brain are equally connected and the regions are specialized for certain purposes.

4. Power Consumption

The human brain is dramatically more energy-efficient than any existing artificial intelligence. The brain uses around 20 Watts, which is comparable to what a standard laptop uses today. But with that power the brain handles a million times more neurons.

5. Architecture


In a neural network, the layers are neatly ordered and are addressed one after the other. The human brain, on the other hand, does a lot of parallel processing and not in any particular order.

6. Activation Potential

In the real brain neurons either fire or don’t. In a neural network the firing is mimicked by continuous values instead, so the artificial neurons can smoothly slide from off to on, which real neurons can’t.

7. Speed

The human brain is much, much slower than any artificially intelligent system. A standard computer performs some 10 billion operations per second. Real neurons, on the other hand, fire at a frequency of at most a thousand times per second.

8. Learning Technique

Neural networks learn by producing output, and if this output is of low performance according to the loss function, then the net responds by changing the weights of the neurons and their connections. No one knows in detail how humans learn, but that’s not how it works.

9. Structure

A neural net starts from scratch every time. The human brain, on the other hand, has a lot of structure already wired into its connectivity, and it draws on models which have proved useful during evolution.

10. Precision

The human brain is much more noisy and less precise than a neural net running on a computer. This means the brain basically cannot run the same learning mechanism as a neural net and it’s probably using an entirely different mechanism.

A consequence of these differences is that artificial intelligence today needs a lot of training with a lot of carefully prepared data, which is very unlike to how human intelligence works. Neural nets do not build models of the world, instead they learn to classify patterns, and this pattern recognition can fail with only small changes. A famous example is that you can add small amounts of noise to an image, so small amounts that your eyes will not see a difference, but an artificially intelligent system might be fooled into thinking a turtle is a rifle.

Neural networks are also presently not good at generalizing what they have learned from one situation to the next, and their success very strongly depends on defining just the correct “loss function”. If you don’t think about that loss function carefully enough, you will end up optimizing something you didn’t want. Like this simulated self-driving car trained to move at constant high speed, which learned to rapidly spin in a circle.

But neural networks excel at some things, such as classifying images or extrapolating data that doesn’t have any well-understood trend. And maybe the point of artificial intelligence is not to make it all that similar to natural intelligence. After all, the most useful machines we have, like cars or planes, are useful exactly because they do not mimic nature. Instead, we may want to build machines specialized in tasks we are not good at.



Wednesday, August 7, 2019


'Spin' found in over half of clinical trial abstracts published in top psychiatry journals


'Spin'—exaggerating the clinical significance of a particular treatment without the statistics to back it up—is apparent in more than half of clinical trial abstracts published in top psychology and psychiatry journals, finds a review of relevant research in BMJ Evidence Based Medicine.
The findings raise concerns about the potential impact this might be having on treatment decisions, as the evidence to date suggests that abstract information alone is capable of changing doctors' minds, warn the study authors.
Randomised controlled trials serve as the gold standard of evidence, and as such, can have a major impact on clinical care. But although researchers are encouraged to report their findings comprehensively, in practice they are free to interpret the results as they wish.
In an abstract, which is supposed to summarise the entire study, researchers may be rather selective with the information they choose to highlight, so misrepresenting or 'spinning' the findings.
To find out how common spin might be in abstracts, the study authors trawled the research database PubMed for randomised controlled trials of psychiatric and behavioural treatments published between 2012 and 2017 in six top psychology and psychiatry journals.
They reviewed only those trials (116) in which the primary results had not been statistically significant, and used a previously published definition of spin to see how often researchers had 'spun' their findings.
They found evidence of spin in the abstracts of more than half (65; 56%) of the published trials. This included titles (2%), results sections (21%), and conclusion sections (49%).
In 17 trials (15%), spin was identified in both the results and conclusion sections of the abstract.
Spin was more common in trials that compared a particular drug/behavioural approach with a dummy (placebo) intervention or usual care.
Industry funding was not associated with a greater likelihood of spinning the findings: only 10 of the 65 clinical trials in which spin was evident had some level of industry funding.
The study authors accept that their findings may not be widely applicable to clinical trials published in all psychiatry and psychology journals, and despite the use of objective criteria to define spin, inevitably, their assessments would have been subjective.
Nevertheless, they point out: "Researchers have an ethical obligation to honestly and clearly report the results of their research. Adding spin to the abstract of an article may mislead physicians who are attempting to draw conclusions about a treatment for patients. Most physicians read only the article abstract the majority of the time."
They add: "Those who write clinical trial manuscripts know that they have a limited amount of time and space in which to capture the attention of the reader. Positive results are more likely to be published, and many manuscript authors have turned to questionable reporting practices in order to beautify their results."



Special Breakthrough Prize awarded for Supergravity
Sabine Hossenfelder

The Breakthrough Prize is an initiative founded by billionaire Yuri Milner, now funded by a group of rich people which includes, next to Milner himself, Sergey Brin, Anne Wojcicki, and Mark Zuckerberg. The Prize is awarded in three different categories, Mathematics, Fundamental Physics, and Life Sciences. Today, a Special Breakthrough Prize in Fundamental Physics has been awarded to Sergio Ferrara, Dan Freedman, and Peter van Nieuwenhuizen for the invention of supergravity in 1976. The Prize of 3 million US$ will be split among the winners.

Interest in supergravity arose in the 1970s when physicists began to search for a theory of everything that would combine all four known fundamental forces to one. By then, string theory had been shown to require supersymmetry, a hypothetical new symmetry which implies that all the already known particles have – so far undiscovered – partner particles. Supersymmetry, however, initially only worked for the three non-gravitational forces, that is the electromagnetic force and the strong and weak nuclear forces. With supergravity, gravity could be included too, thereby bringing physicists one step closer to their goal of unifying all the interactions.

In supergravity, the gravitational interaction is associated with a messenger particle – the graviton – and this graviton has a supersymmetric partner particle called the “gravitino”. There are several types of supergravitational theories, because there are different ways of realizing the symmetry. Supergravity in the context of string theory always requires additional dimensions of space, which have not been seen. The gravitational theory one obtains this way is also not the same as Einstein’s General Relativity, because one gets additional fields that can be difficult to bring into agreement with observation. (For more about the problems with string theory, please watch my video.)

To date, we have no evidence that supergravity is a correct description of nature. Supergravity may one day become useful to calculate properties of certain materials, but so far this research direction has not led to much.

The works by Ferrera, Freedman, and van Nieuwenhuizen have arguably been influential, if by influential you mean that papers have been written about it. Supergravity and supersymmetry are mathematically very fertile ideas. They lend themselves to calculations that otherwise would not be possible and that is how, in the past four decades, physicists have successfully built a beautiful, supersymmetric, math-castle on nothing but thin air.

Awarding a scientific prize, especially one accompanied by so much publicity, for an idea that has no evidence speaking for it, sends the message that in the foundations of physics contact to observation is no longer relevant. If you want to be successful in my research area, it seems, what matters is that a large number of people follow your footsteps, not that your work is useful to explain natural phenomena. This Special Prize doesn’t only signal to the public that the foundations of physics are no longer part of science, it also discourages people in the field from taking on the hard questions. Congratulations. 


Thursday, August 1, 2019


Back-to-Back, Failed Visions of the “Brain as a Supercomputer” 
July 25, 2019, 5:14
It’s a delicious failed prediction. As neuroscientist Henry Markham summarized at the end of a TED Talk, “I hope that you are at least partly convinced that it is not impossible to build a brain. We can do it within 10 years, and if we do succeed, we will send to TED, in 10 years, a hologram to talk to you. Thank you.” If he had asked anyone now gathered at Discovery Institute’s Walter Bradley Center, I think they would have advised him not to go out on that particular tree branch.
As Ed Yong points out at The Atlantic, Dr. Markham recorded his talk in July 2009, now just past a decade ago. “It’s been exactly 10 years,” Yong notes, adding perhaps superfluously, “He did not succeed.”

The Brain as a Supercomputer
The title of the talk was, “A brain in a supercomputer.” Well, maybe the prophecy failed because the brain is not just a computer, super- or otherwise, and because nothing like real consciousness will be available to a machine, now or perhaps ever. Sure, a machine can give a TED Talk — as you would have guessed if you’ve seen the Hall of Presidents attraction at Disneyland, introduced in 1971 — but whether it would understand what it was saying is the real question.
Another Anniversary

Over at Mind Matters, Walter Myers has an excellent post reflecting on another anniversary, this one the publication of an iconic book, Gödel, Escher, Bach: An Eternal Golden Braid (1979), 40 years old next month. I’d never read it and, out of curiosity, I picked up a copy for myself and started in on this huge work, which won a Pulitzer Prize for author Douglas Hofstadter. As Dr. Myers recalls, many readers got to the end and completely misunderstood Hofstadter’s point.
It’s not really about Kurt Gödel, M.C. Escher, or J.S. Bach, or about math, art, and music and their interplay. As Hofstadter clarified in a preface to the 20th anniversary edition, he was arguing in much the same vein as Henry Markham, that the brain can be understood in rules-bound machine terms, with consciousness dancing on top as an “emergent” property.

The book was intended to ask the fundamental question of how the animate can emerge from the inanimate, or more specifically, how does consciousness arise from inanimate, physical material? As philosopher and cognitivist scientist David Chalmers has eloquently asked, “How does the water of the brain turn into the wine of consciousness?”

Hofstadter believes he has the answer: the conscious “self” of the human mind emerges from a system of specific, hierarchical patterns of sufficient complexity within the physical substrate of the brain. The self is a phenomenon that rides on top of this complexity to a large degree but is not entirely determined by its underlying physical layers.

[[Gee – I taught that book in a Philosophy of Mathematics course at Johns Hopkins in the seventies. I had no clue the real hidden subject was consciousness. He hid it so well that the “real subject” had zero effect on the debates about consciousness….]]

In the 1999 preface, he notes an apparent contradiction. When we look at computers, we see inflexible, unintelligent, rule-following beasts with no internal desires, which he describes as “the epitome of unconsciousness.” Is it a contradiction that intelligent behavior can be programmed into unintelligent machines? Is there an “unbreachable gulf” between intelligence and non-intelligence?

Hofstadter believes that through large sets of formal rules and levels of rules generated by AI, we can finally program these inflexible computers to be flexible, thinking machines. If so, we were wrong in thinking that there is a marked difference between human minds and intelligent machines.
The Culture of Materialism
Or to put it another way, a brain is a supercomputer. Forty years later, that assertion remains just that, an assertion. Walter Myers concludes:

[T]he view that human consciousness is something unique is the most tenable philosophical position unless we learn definitively otherwise.
There is, quite simply, no mechanical explanation of how the human mind has emerged from brawling chimpanzees over the course of millions of years of evolution.

The idea of the mind as a “meat machine” retains its hold on smart people for reasons other than neuroscience. It’s not science but the culture of materialism speakingRead the rest at Mind Matters. And if you have not done so yet, watch Episode 2 of Science Uprising, which deals concisely with the issue:


Wednesday, July 24, 2019








“Were Rishonim wrong when they said elephant can be acquired by getting it to jump on his own. It’s known fact elephants can’t jump so this means the rishon is wrong”

A email I received recently. I answered: .


First look at this: 

https://www.google.com/search?q=elephants+can%27t+jump+and+here%27s+why&rlz=1C1GCEU_enIL819IL820&oq=elephants+can%27t+jump+and+here%27s+why&aqs=chrome..69i57j33.9050j0j7&sourceid=chrome&ie=UTF-8

Elephants can’t jump—and here’s why

Despite what you may have seen in your Saturday morning cartoons, elephants can’t jump, according to a video by Smithsonian. And there’s one simple reason: They don’t have to. Most jumpy animals—your kangaroos, monkeys, and frogs—do it primarily to get away from predators. Elephants keep themselves safe in other ways, relying on their huge size and protective social groups. And, as it turns out, it’s hard to get 4 tons of mammal off the ground all at once. In the case of the elephant, in fact, it’s impossible. Unlike most mammals, the bones in elephant legs are all pointed downwards, which means they don’t have the “spring” required to push off the ground.

By Patrick MonahanJan. 27, 2016 , 3:00 PM



[[Notice that the explanation only applies to adult elephants. Furthermore, no one has tried to induce them to jump. The fact that they have never been seen to jump is explained by the fact they don't need to, as the paragraph points out. And an engineer would say: your explanation of why they cannot jump is only a guess. Without thorough testing you cannot know that they are unable to jump. ]]

Second, in this book it says that Indian elephants can jump

https://www.amazon.com/Elephant-Bill-J-H-Williams/dp/1590480775/ref=sr_1_1?keywords=j+h+williams+elephant+bill&qid=1563801743&s=gateway&sr=8-1



And here is a full discussion of the subject:

https://www.cbsnews.com/news/do-elephants-jump/



THE FOLLOWING ARE EXCERPTS FROM "DO ELEPHANTS JUMP?"

All rights reserved. No part of this book may be used or reproduced without written permission from HarperCollins Publishers, 10 East 53rd Street, New York, NY 10022

DO ELEPHANTS JUMP?

We talked to a bunch of elephant experts and none of them has ever seen an elephant jump. Most think it is physiologically impossible for a mature elephant to jump, although baby elephants have been known to do so, if provoked. Not only do mature elephants weigh too much to support landing on all fours, but their legs are designed for strength rather than leaping ability. Mark Grunwald, who has worked with elephants for more than a decade at the Philadelphia Zoo, notes that elephant's bone structure makes it difficult for them to bend their legs sufficiently to derive enough force to propel the big lugs up.

Yet there are a few sightings of elephants jumping in the wild. Veterinarian Judy Provo found two books in her college library that illustrate the discrepancy. S. K. Ettingham's Elephant lays out the conventional thinking: "… because of its great weight, an elephant cannot jump or even run in the accepted sense since it must keep one foot on the ground at all times." But an account in J. J. William's Elephant Bill describes a cow elephant jumping a deep ravine "like a chaser over a brook."

Animals that are fast runners or possess great leaping ability have usually evolved these skills as a way of evading attackers. Elephants don't have any natural predators, according to the San Diego Wild Animal Park's manager of animals, Alan Rosscroft: "Only men kill elephants. The only other thing that could kill an elephant is a fourteen-ton tiger."

Most of the experts agree with zoologist Richard Landesman of the University of Vermont that there is little reason for an elephant to jump in its natural habitat. Indeed, Mike Zulak, an elephant curator at the San Diego Zoo, observes that pachyderms are rather awkward walkers, and can lose their balance easily, so they tend to be conservative in their movements.














Monday, July 22, 2019


Scientists Debate the Origin of Cell Types in the First Animals

Jordana Cepelewicz



From one came many. Some 700 million years ago, a single cell gave rise to the first animal, a multicellular organism that would eventually spawn the incredible complexity and diversity seen in animals today. New research is now offering scientists a fresh perspective on what that cell looked like, and how multicellularity could have emerged from it — a transition that marks one of the most pivotal events in the history of life on Earth.
For well over a century, it has been widely assumed that the ancestors from which the first animal evolved were simple blobs of identical cells. Only later, after the animals formed their own branch on the tree of life, did those cells start to differentiate into various cell types with specialized functions. But now, painstaking genomic analyses and comparisons between the most ancient animals alive today and their closest non-animal relatives are starting to overturn that theory.
The recent work paints a picture of ancestral single-celled organisms that were already amazingly complex. They possessed the plasticity and versatility to slip back and forth between several states — to differentiate as today’s stem cells do and then dedifferentiate back to a less specialized form. The research implies that mechanisms of cellular differentiation predated the gradual rise of multicellular animals.
Now, scientists are reporting the most compelling evidence yet for the new narrative. Their work, and the debate inspired by its publication inNature last month, also highlights how difficult it is to pin down definitive answers to these kinds of evolutionary questions — and how wide a net researchers have to cast in pursuit of those answers.
Looking for Close Relatives
In the 1860s, the biologists Henry James Clark and William Saville-Kent separately noted a striking resemblance between the cells of two organisms. Choanoflagellates are tiny spherical or egg-shaped cells crowned with a “collar” of fingerlike protrusions surrounding a single flagellum that whips back and forth. These protists stir up water currents with their flagella, sweeping their next meal (usually bacteria) into their collars to eat. Meanwhile, sponges are simple animals made up of many cell types, including choanocytes — collared, flagellated cells that line the chambers inside the sponge and capture its food. Choanocytes look and act remarkably like choanoflagellates, so much so that some scientists posited in the 1980s and ’90s that choanoflagellates might be animals that evolved from sponges and then simplified down to one cell.



The structural similarities prompted experts to think that the cells shared an ancestor, and that the single-celled choanoflagellates might be the key to understanding how the multicellular sponge came about. Building on this, the famed marine biologist Ernst Haeckel put forth a theory for the evolution of animal multicellularity in 1874, which researchers have since elaborated on: A choanoflagellate-like ancestral cell started it all. Many such cells came together to form a colony, a hollow ball of identical cells that, in turn, gradually differentiated into cell types and tissues with various functions. This eventually led to the first animal, the sponge — and the rest is history.
All the signs indicated that this was the right way to think about animal evolution. In the 2000s, more than a century after Haeckel proposed his theory, genomic evidence confirmed that choanoflagellates were animals’ closest living relatives. “Out of the many single-cell eukaryotes out there, 150 years ago choanoflagellates had been proposed as a close relative of animals,” said Pawel Burkhardt, a molecular biologist at the Sars International Center for Marine Molecular Biology in Norway. “Then the first genome was sequenced, and bam! It actually was really true.”

 “Scientists, including myself, have for a long time enjoyed this choanoflagellate-choanocyte connection,” said David Gold, a geobiologist at the University of California, Davis, “because it tells a clear and elegant story.”
Besides, said Douglas Erwin, a paleobiologist at the Smithsonian Institution’s National Museum of Natural History in Washington, D.C., “You’re going to question Haeckel? How do you question Haeckel? It’s almost like questioning Darwin.”  [[!!??!!]]
[[And in spite of all the signs confirming the theory, and its calirity and elegance – and acceptance in the scientific community – it is now seen to be wrong. Is the moral obvious for those contemporary theories that are supported by all the evidence and are clear and elegant and accepted?]]
The First Seeds of Doubt
But uncertainty about that clear and elegant story has been growing over the past decade. The idea that animals arose from a colony of choanoflagellate-like cells implies that cell differentiation evolved after multicellularity did. But “the data is demonstrating that it’s not like that,” said Iñaki Ruiz-Trillo, an evolutionary biologist at the Institute of Evolutionary Biology in Barcelona.
The first complication came in 2008, when a group of scientists, in an effort to more precisely map out the evolutionary relationships among animals on the tree of life, identified comb jellies rather than sponges as the earliest animals. [[Hmmm – what dating methods did they use first? And what methods second? And why did they not agree?]] The finding generated controversy. “It’s still very much a heated question,” Gold said, “but I think it forced the community to reappraise the classic narrative.”

Subsequent discoveries continued to fuel the debate over which animal group came first. And some studies uncovered overlooked differences  [[Hmm – overlooked differences – so they were there but no one paid attention….could that be happening again today?]]  between choanoflagellates and sponge choanocytes. The cells’ shared ancestry began to look less like a foregone conclusion.
Scientists also began to realize that choanoflagellates and two closely related unicellular groups all have complex life cycles that proceed through various cell states. These states essentially act as different cell types — but rather than all existing side by side as in a multicellular organism, they arise sequentially in a single cell. “They have temporal cell differentiation,” Burkhardt said.
And during those life cycles, all three of these protists spend part of their lives in a form that borders on something like primitive multicellularity. Choanoflagellates have a colonial form; the second protist group has amoeba-like cells that aggregate; the cells of the third group grow to have hundreds of nuclei.
This prompted a paper in 2009 that rejuvenated an old alternative to Haeckel’s hypothesis. Back in 1949, the Russian biologist Alexey Zakhvatkin had proposed that multicellular animals evolved when temporally differentiating cells formed colonies and began to commit to particular stages in their life cycles, allowing a few cell types to exist at once. Ruiz-Trillo and his colleagues provided further evidence for this so-called temporal-to-spatial transition. In a series of studies, they showed that certain families of regulatory proteins supposedly unique to animals, including those involved in cell differentiation, were actually already present in their far more ancient unicellular relatives.
Now, a team of researchers led by Sandie Degnan and Bernard Degnan, a married pair of marine biologists at the University of Queensland in Australia, have provided additional support for this view of animal evolution while also taking a swing at the traditional theory’s foundation: the evolutionary link between the choanoflagellates and the sponge choanocytes.
A More Flexible Ancestor
When the team started their project, they “really just wanted to put some meat on the bones of the [traditional] theory,” Bernard Degnan said. To do so, they examined the gene expression in choanocytes and other kinds of sponge cells, then compared those findings with published data on choanoflagellates and two other protists.

They expected to establish that sponge choanocytes had gene expression profiles most like those of choanoflagellates. Instead, they found that another type of sponge cell did.
That cell type, called an archaeocyte, acts like a stem cell for the sponge: It can differentiate into any other cell type the animal might need. Some of the gene expression patterns in archaeocytes are significantly similar to those of the protists during particular life cycle stages, according to Bernard Degnan. “They’re expressing genes that suggest that they have an ancestral regulatory system,” he said. “All animals are just variations on that theme that was created a long time ago.”
Moreover, the choanocytes seemed to be unexpectedly transient. “The choanocytes, which are supposed to be the bedrock of all animal origins … are almost ephemeral,” he said. “They don’t stay stably in that state, but kind of quickly dedifferentiate into these stem cells, the archaeocytes.”
To Gold, who was not involved in the study, this result is the strongest evidence yet that sponge choanocytes should not necessarily be used as “some sort of proxy for the origin of animals.”
Bernard Degnan thinks it’s possible that choanoflagellates and sponge choanocytes arrived independently at their collared, flagellated architecture. In the shared ancestry of choanoflagellates and sponges there could have been something like an archaeocyte or a pluripotent stem cell. “It transited between different cell types, and those cell types then became stable,” he said. “And essentially that’s what gave rise to true multicellularity.” Later, as animals got bigger and more complex, their cells had to become more precise, specialized and fixed in their identities, but they lost a lot of their versatility in the process.

In retrospect, this version of multicellularity’s origin makes a lot of sense. According to some experts, we can think of the single-celled organisms that came before animals as stem cells of sorts: They could go on dividing forever, and they could perform a variety of functions, including reproduction. Other early animals, such as jellyfish, show a great deal of that seemingly ancestral plasticity as well.
“Stem cells are something people have been working on for years” in studies of development, wound healing and cancer, Ruiz-Trillo said. Now, it’s becoming clear that they will be “interesting for understanding evolution as well,” for discovering how animals came to be.
A Path Toward Reconciliation
Not everyone agrees entirely with the Degnans’ conclusions. Drawing inferences from gene expression profiles isn’t so straightforward. “Dig into [it], and you could interpret some data completely differently,” Burkhardt said. Differences in gene expression don’t necessarily preclude two cell types from sharing ancestry.

This choanoflagellate, extracted from a colony, uses its signature collar and flagellum to trap food — much like choanocyte cells do in sponges.
Erwin agreed. Such data, he said, “is a snapshot [taken] at a particular point in time.” Given that choanoflagellates and sponge choanocytes have been evolving on their own for the past 700 million years, it makes sense that they express very different genes.
In any comparison of modern organisms, “you are looking at animals that have a history of loss and gain,” said Maja Adamska, an evolutionary developmental biologist at the Australian National University who did not participate in the Degnans’ study. “You risk that you will oversimplify your findings.” [[Hmm – could some of today’s accepted “findings” be oversimplified?]]
Other sponge species, she added, don’t have archaeocytes at all. Instead, their choanocytes perform those stem cell-like roles. “I suspect that if we did a comparison in [those choanocytes],” Adamska said, “we would have found higher similarity to choanoflagellates.”
Adamska thinks that the first animal could very well have been a pancake of stemlike cells that often shifted their identities. She also thinks that the gene expression comparison doesn’t rule out the evolutionary ties between choanoflagellates and the first multicellular animal cells. “In fact, I strongly believe that my ancestors did have choanocytes,” she said.
The two theories about the origins of animal multicellularity aren’t mutually exclusive. “I think there’s a place for both choanoflagellate-like features and [temporal differentiation] features in the last common ancestor we are trying to paint,” Adamska said. “I don’t see the contradiction there.” She and her colleagues are now working on profiling gene expression in sponges without archaeocytes to test this idea further.
Hints of a combined theory are already emerging from Burkhardt’s lab. In a preprint they posted on biorxiv.org in May, Burkhardt and his colleagues found that the cells in a choanoflagellate colony are not all identical: They differ in their morphology and in the ratio of their organelles. These observations, he said, suggest that spatial cell differentiation was already happening in the choanoflagellate lineage, and perhaps even earlier — a possibility that blends the new ideas (that the capacity for differentiation is ancient and the transition to animal multicellularity was gradual) with the old (that this could happen with choanoflagellate-like cells).
So while there’s still no definitive answer on what exactly the first animal looked like, the picture is getting clearer. “We are getting closer to understanding where we came from in the depths of time,” Adamska said. “And I think that is so cool.”


Thursday, July 18, 2019


From Chernobyl Disaster Site, a Boost for Intelligent Design 

July 11, 2019, 4:50
מה רבו מעשיך!
To evolutionists, radiation is like manna from heaven. It feeds the engine of Darwinian evolution — random mutation — providing variations that evolution’s Tinkerer, natural selection, can use to build new watches blindfolded. Well, the Chernobyl disaster of 1986 gave evolutionary biologists an unexpected natural lab to test their view, and this experiment has been going on for two years longer than Richard Lenski’s Long-Term Evolution Experiment with E. coli. 
The recent HBO miniseries Chernobyl brought back memories of the event that seems synonymous with “disaster.” Experts had predicted a high death toll on all life as a result of the radiation bath. People were quickly evacuated from a 3500-km area, and the cities closest to the nuclear plant quickly became ghost towns (see the video “Postcards from Pripyat”). A 30-km Chernobyl Exclusion Zone (CEZ) was enforced. To everyone’s surprise, though, life in the CEZ is thriving 33 years later. Therein is a story worth investigating: which view of biology scored, Darwin or intelligent design?

Some Considerations

Analyzing the situation requires some knowledge about nuclear radiation. Even though the CEZ will remain contaminated to some degree for thousands of years, not all the “hot” isotopes will last that long, and not all are equally dangerous. Toxicity depends on the emitted particles (alpha, beta, or gamma rays), the ratios emitted, and their respective energies. One of the most toxic radioisotopes of all, polonium-210, which was used to kill the former Russian spy Alexander Litvinenko in London in 2006, is only deadly when ingested; it is safe to hold in the hand. It also has a relatively short half-life, and its particles have such low energy they can be blocked by a sheet of paper. Inside the body, however, they make cells undergo apoptosis (cell suicide) as the hot particles are transported through the blood, tissues, and organs (Medical News Today).
The Chernobyl reactor released many radioisotopes into the atmosphere, some with relatively short half-lives. One of the biggest risks for humans from Chernobyl was radioactive iodine, which concentrates in the thyroid gland and can cause thyroid cancer. Its half-life is on the order of eight days, however, so within four years after the disaster, levels had dropped enough to make dairy products safe again for consumption. Cesium-137 and strontium-90 have half-lives of around thirty years, so they will remain a concern, but some of these can leach into the soil by rain and be transported by wind, and thus dissipate sooner. A United Nations report twenty years after the disaster says, “Although plutonium isotopes and americium 241 [half-life 432 years] will persist perhaps for thousands of years, their contribution to human exposure is low.” 
One other consideration is that the biosphere is bombarded with ionizing radiation all the time, from radon in the soil, carbon-14 in the air, gamma rays from space, and other sources. It’s the increment above what experts consider safe levels, therefore, that determines the risk, and that diminishes with distance from the source.
We should not think of the CEZ as glowing hot for 20,000 years, therefore. But without doubt, the area received a highly dangerous dose of radiation at first. A few dozen people died within the immediate aftermath of the explosion. Experts estimate that about 4,000 people “could” die from cancer, but as years go by, it’s increasingly hard to attribute the cause to Chernobyl as radiation levels decrease. Many more owe their lives to the heroes who died to entomb the reactor shortly after the accident. Pine trees died, and animals within the hot zone died — but not all of them. And now, to the experts’ surprise, the area is doing remarkably well. Stuart Thompson, a plant biochemist, writes for The Conversation:
Life is now thriving around Chernobyl. Populations of many plant and animal species are actually greater than they were before the disaster.
Given the tragic loss and shortening of human lives associated with Chernobyl, this resurgence of nature may surprise you. Radiation does have demonstrably harmful effects on plant life, and may shorten the lives of individual plants and animals. But if life-sustaining resources are in abundant enough supply and burdens are not fatal, then life will flourish. [Emphasis added.]

Why Life Is Resilient

The subject of his article is, “Why plants don’t die from cancer.” Unlike animals, he explains, plants can work around damaged tissue. They can also grow most tissues they need anywhere. “This is why a gardener can grow new plants from cuttings, with roots sprouting from what was once a stem or leaf.” Additionally, plant cell walls act as a barrier to metastasis, should tumors arise. Even though dying trees near the accident created a “Red Forest,” the local ecology did not collapse. 
Thompson retreats into Darwinism briefly, but he points out reasons why plants proved so resilient to the Chernobyl disaster. Are these not better explained by intelligent design?
Interestingly, in addition to this innate resilience to radiation, some plants in the Chernobyl exclusion zone seem to be using extra mechanisms to protect their DNA, changing its chemistry to make it more resistant to damage, and turning on systems to repair it if this doesn’t work. Levels of natural radiation on the Earth’s surface were much higher in the distant past when early plants were evolving, so plants in the exclusion zone may be drawing upon adaptations dating back to this time in order to survive.
Where did those extra mechanisms come from? Where did the “systems to repair” come from? Radiation has no power to bring forth complex systems. This is like saying a hail of bullets generates armor! No; if the systems were not already present, they could do nothing.

A Thriving Ecosystem

With plants rebounding (which presupposes the presence of worms, fungi and other ecological partners), mammals and birds quickly returned in force. Wolves, boars, and bears are now back in larger numbers than ever, and birds can be seen flying in and out of the sarcophagus built over the reactor, and even nesting in its cracks. Thompson shares another surprise: with the humans mostly gone, Chernobyl has become a thriving wildlife refuge!
Crucially, the burden brought by radiation at Chernobyl is less severe than the benefits reaped from humans leaving the area. Now essentially one of Europe’s largest nature preserves, the ecosystem supports more life than before, even if each individual cycle of that life lasts a little less.
Another surprise is that the people who refused to evacuate appear to be doing better than those who left. Forced resettlement wore evacuees down with anxiety, fear, and personal conflicts. The U.N. report says, “Surveys show that those who remained or returned to their homes coped better with the aftermath than those who were resettled.” 
For more astonishment, read “What Bikini Atoll Looks Like Today,” at Stanford Magazine. The spot where a hydrogen bomb exploded 62 years ago is once again a tropical paradise, complete with “big healthy coral communities” in the surrounding waters, and schools of fish swimming through the hulks of sunken warships. Despite 23 atomic bomb tests at the atoll, “Ironically, Bikini reefs look better than those in many places she’s dived,” writes Sam Scott about scuba diver Elora Lopez. “It didn’t look like this nightmare-scape that you might expect,” she says. “And that’s still something that’s weird to process.”

Designed Resilience

The lesson from Chernobyl is this: radiation kills, but life comes prepared to defend itself. No newly evolved organisms emerged at Chernobyl. Billions of mutations were not naturally selected to originate new species. The same organisms rebounded because DNA repair systems, involving exquisite machinery, were prepared to find mutations and fix them. The systems might be overwhelmed temporarily, but will rebound as soon as the threat diminishes. Machines do not make themselves in the presence of threats. They have to be prepared in advance. Think of it: the DNA code includes instructions on how to build machines that can repair DNA! 
The resilience of some life forms is truly remarkable. Common “water bears,” aka tardigrades, are some of the most durable animals known. These nearly microscopic arthropods might be found in your garden as well as in polar ice. They can survive the vacuum of space with no oxygen for days, endure temperatures from near absolute zero to boiling water, and survive radiation a thousand times stronger than levels at the surface of the earth. Some have been revived after a century in a dehydrated state! It wasn’t the conditions that produced these abilities; tardigrades had to already have these robust systems before the conditions arrived. Tardigrades never had to “evolve” in space; how did they pass that test? The answer is design.
Even some one-celled organisms are fantastically durable. A preprint at bioRxiv speaks of “Extreme tolerance of Paramecium to acute injury induced by γ rays,” due to “DNA protection and repair” genes. Some archaea and bacteria (thought to be the simplest life forms) can survive hot water above the boiling point in Yellowstone hot springs. Another ubiquitous microbe named Deinococcus radiodurans, “the world’s toughest bacterium,” is amazing. According to Genome News Network, “The microbe can survive drought conditions, lack of nutrients, and, most important, a thousand times more radiation than a person can.” It was discovered doing just fine in ground meat that had been irradiated for sterilization. How does it do it? 
An efficient system for repairing DNA is what makes the microbe so tough. High doses of radiation shatter the D. radiodurans genome, but the organism stitches the fragments back together, sometimes in just a few hours. The repaired genome appears to be as good as new.
“The organism can put its genome back together with absolute fidelity,” says Claire M. Fraser, of The Institute for Genome Research (TIGR) in Rockville, Maryland. She was the leader of the TIGR team that sequenced D. radiodurans in 1999.
The fantastic resilience of life to threats, whether from ionizing radiation, temperature, or deprivation, shouts design. As stated in a recent post about homeostasis, only intelligence builds machines that can maintain the state of other machines. The recovery of Chernobyl’s ecosystem offers powerful evidence for life’s pre-programmed resilience.