[Paleopsych] is evolutionary change stockpiled?

Val Geist kendulf at shaw.ca
Sat Nov 27 04:39:02 UTC 2004

Dear Howard,

How I am itching to enter the debate and am not able to do so as I am reading mountains of evidence in order to appear in a Montana court on Dec. 2nd. This is total occupation as you will know if you have faced hostile cross examinations. 

Very best regards from a quiet admirer!


Val Geist
  ----- Original Message ----- 
  From: HowlBloom at aol.com 
  To: paleopsych at paleopsych.org 
  Sent: Friday, November 26, 2004 5:47 PM
  Subject: Re: [Paleopsych] is evolutionary change stockpiled?

  In a message dated 11/24/2004 9:31:36 AM Eastern Standard Time, shovland at mindspring.com writes:

  It could be that the accretion of microscopic changes 
  in the genes without external implementation does in 
  fact represent a period of testing the changes to see 
  if they are appropriate.  '

  Software enhancements are done this way.  We get
  feedback from users of the existing version, we build
  their perceptions into the system, we test it, and
  eventually we go live.

  the whole concept of natural selection gets very iffy if something like this is true.  A genetic suite can extend the skin of a small mammal, can give the mammal wings, and can turn a tree-climbing mammal into a bat.  But if that genetic suite can only test its viability to survive in the team of a genome and in the environment of a nucleus, if the gene suite remains hidden--or cryptic, to use the term applied by researchers on this topic, how can it test the viability of its product—the skin flaps connecting front limbs to hind limbs that are wings?


  How can that suite of genes be "certain" that it will turn out a malformation of skin that's aerodynamically sound?  How can it be sure it will turn out a malformation that will serve any useful purpose, much less one that gives rodents the ability can fly an edge?


  How, for that matter, does a suite of genes for a body segment of an insect "learn" how to produce a head if it shows up in one place, a thorax if the gene suite shows up in another, and an abdomen if it shows up third in line?


  How could gene suites possibly learn to produce these things without trial and error, without testing, and without practice?


  Or, to put it in Stephen Jay Gould's terms, if Darwin's gradualism is right, why do we not see a plethora of "hopeful monsters"--random experiments that don't work out?


  Is it possible that when animals—including humans—are exposed to stress or to opportunity, gene suites that have never been tried out before suddenly appear, we have a flood of hopeful monsters, and those that are able to find or to invent a new way of making a living, a new niche, become fruitful and multiply?


  If so, do we have any evidence for this among multicellular creatures?  We DO have evidence of this sort of body-plasticity among bacteria.  When bacteria are exposed to stress they become more open to new genetic inserts from phages and from bacterial sex.


  In the ancient days when John Skoyles was among us, he pointed to research on heat-shock genes demonstrating that there are gene police that keep the genome rigidly in order under normal circumstances, but that loosen their grip when life gets tough and open the genome to new solutions to old problems, including solutions that turn old problems into new forms of food.


  But is there plasticity of this sort in the bodies of multicellular organisms?  There’s some that comes from shifting the amount of time an embryo stays in the womb.  Eject your infant when it’s still highly plastic and you get neoteny, you get a lot of extra wiggle room.  And the brain is extremely plastic…at least in humans.  But how far can bodies stretch and bend without trial and error?


  The two papers that relate to this issue are Eshel’s on “Meaning-Based Natural Intelligence” and Greg’s on “When Genes Go Walkabout”, so I’ll append them below.







  By Greg Bear



  I’m pleased and honored to be asked to appear before the American Philosophical Society, and especially in such august company. Honored... and more than a little nervous! I am not, after all, a scientist, but a writer of fiction--and not just of fiction, but of science fiction. That means humility is not my strong suit. Science fiction writers like to be provocative. That’s our role. What we write is far from authoritative, or final, but science fiction works best when it stimulates debate.

  I am an interested amateur, an English major with no degrees in science. And I am living proof that you don’t have to be a scientist to enjoy deep exploration of science. So here we go--a personal view.

  A revolution is under way in how we think about the biggest issues in biology--genetics and evolution. The two are closely tied, and viruses--long regarded solely as agents of disease--seem to play a major role.

  For decades now, I’ve been skeptical about aspects of the standard theory of evolution, the neo-Darwinian Modern Synthesis. But without any useful alternative--and since I’m a writer, and not a scientist, and so my credentials are suspect--I have pretty much kept out of the debate. Nevertheless, I have lots of time to read--my writing gives me both the responsibility and the freedom to do that, to research thoroughly and get my facts straight. And over ten years ago, I began to realize that many scientists were discovering key missing pieces of the evolutionary puzzle. 

  Darwin had left open the problem of what initiated variation in species. Later scientists had closed that door and locked it. It was time to open the door again.

  Collecting facts from many sources--including papers and texts by the excellent scientists speaking here today--I tried to assemble the outline of a modern appendix to Darwin, using ideas derived from disciplines not available in Darwin’s time: theories of networks, software design, information transfer and knowledge, and social communication--lots of communication. 

  My primary inspiration and model was variation in bacteria. Bacteria initiate mutations in individuals and even in populations through gene transfer, the swapping of DNA by plasmids and viruses. 

  Another inspiration was the hypothesis of punctuated equilibrium, popularized by Stephen Jay Gould and Niles Eldredge. In the fossil record--and for that matter, in everyday life--what is commonly observed are long periods of evolutionary stability, or equilibrium, punctuated by sudden change over a short span of time, at least geologically speaking--ten thousand years or less. And the changes seem to occur across populations.  

  Gradualism--the slow and steady accumulation of defining mutations, a cornerstone of the modern synthesis--does not easily accommodate long periods of apparent stability, much less  rapid change in entire populations. If punctuated equilibrium is a real phenomenon, then it means that evolutionary change can be put on hold. How is that done? How is the alleged steady flow of mutation somehow delayed, only to be released all at once? 

  I was fascinated by the possibility that potential evolutionary change could be stored up. Where would it be kept? Is there a kind of genetic library where hypothetical change is processed, waiting for the right moment to be expressed? Does this imply not only storage, but a kind of sorting, a critical editing function within our DNA, perhaps based on some unknown genetic syntax and morphology? 

  If so, then what triggers the change? 

  Most often, it appears that the trigger is either environmental challenge or opportunity. Niches go away, new niches open up. Food and energy becomes scarce. New sources of food and energy become available. Lacking challenge or change, evolution tends to go to sleep--perhaps to dream, and sometimes to rumple the covers, but not to get out of bed and go for coffee.

  Because bacteria live through many generations in a very short period of time, their periods of apparent stability are not millennia, but years or months or even days.

  The most familiar mutational phenomenon in bacteria--resistance to antibiotics--can happen pretty quickly. Bacteria frequently exchange plasmids that carry genes that counteract the effects of antibiotics. Bacteria can also absorb and incorporate raw fragments of DNA and RNA, not packaged in nice little chromosomes. The members of the population not only sample the environment, but exchange formulas, much as our grandmothers might swap recipes for soup and bread and cookies. How these recipes initially evolve can in many instances be attributed to random mutation--or to the fortuitous churning of gene fragments--acting through the filter of natural selection.  Bacteria do roll the dice, but recent research indicates that they roll the dice more often when they’re under stress--that is, when mutations will be advantageous. Interestingly, they also appear to roll the dice predominantly in those genetic regions where mutation will do them the most good! Bacteria, it seems, have learned how to change more efficiently.

  Once these bacterial capabilities evolve, they spread rapidly. However, they spread only when a need arises--again, natural selection. No advantage, no proliferation. No challenge, no change.

  But gene swapping is crucial. And it appears that bacteria accept these recipes not just through random action, but through a complicated process of decision-making. Bacterial populations are learning and sharing. In short, bacteria are capable of metaevolution--self-directed change in response to environmental challenges. 

  Because of extensive gene transfer, establishing a strict evolutionary tree of bacterial types has become difficult, though likely not impossible. We’re just going to have to be clever, like detectives solving crimes in a town where everyone is a thief. 

  Perhaps the most intriguing method of gene swapping in bacteria is the bacteriophage, or bacterial virus. Bacteriophages--phages for short--can either kill large numbers of host bacteria, reproducing rapidly, or lie dormant in the bacterial chromosome until the time is right for expression and release. Lytic phages almost invariably kill their hosts. But these latter types--known as lysogenic phages--can actually transport useful genes between hosts, and not just randomly, but in a targeted fashion. In fact, bacterial pathogens frequently rely on lysogenic phages to spread toxin genes throughout a population. Cholera populations become pathogenic in this fashion. In outbreaks of E. coli that cause illness in humans, lysogenic phages have transported genes from shigella--a related bacterial type--conferring the ability to produce shiga toxin, a potent poison. 

  Thus, what at first glance looks like a disease--viral infection--is also an essential method of communication--FedEx for genes.

  When genes go walkabout, bacteria can adapt quickly to new opportunities. In the case of bacterial pathogens, they can rapidly exploit a potential marketplace of naïve hosts. In a way, decisions are made, quorums are reached, genes are swapped, and behaviors change.

  What lies behind the transfer of bacterial genes? Again, environmental challenges and opportunities. While some gene exchange may be random, bacterial populations overall appear to practice functions similar to education, regimentation, and even the execution of uncooperative members. When forming bacterial colonies, many bacteria--often of different types--group together and exchange genes and chemical signals to produce an organized response to environmental change. Often this response is the creation of a biofilm, a slimy polysaccharide construct complete with structured habitats, fluid pathways, and barriers that discourage predators. Biofilms can even provide added protection against antibiotics. Bacteria that do not go along with this regimen can be forced to die--either by being compelled to commit suicide or by being subjected to other destructive measures. If you don’t get with the picture, you break down and become nutrients for those bacterial brothers who do, thus focusing and strengthening the colony.

  A number of bacteriologists have embraced the notion that bacteria can behave like multicellular organisms. Bacteria cooperate for mutual advantage. Today, in the dentist’s office, what used to be called plaque is now commonly referred to as a biofilm. They’re the same thing--bacterial cities built on your teeth.

  In 1996, I proposed to my publishers a novel about the coming changes in biology and evolutionary theory. The novel would describe an evolutionary event happening in real-time--the formation of a new sub-species of human being. What I needed, I thought, was some analog to what happens in bacteria. And so I would have to invent ancient viruses lying dormant in our genome, suddenly reactivated to ferry genes and genetic instructions between humans. 

  To my surprise, I quickly discovered I did not have to invent anything. Human endogenous retroviruses are real, and many of them have been in our DNA for tens of millions of years. Even more interesting, some have a close relationship to the virus that causes AIDS, HIV. 

  The acronym HERV--human endogenous retrovirus--became my mantra. In 1997 and 1998, I searched the literature (and the internet) for more articles about these ancient curiosities--and located a few pieces here and there, occasional mention in monographs, longer discussions in a few very specialized texts. I was especially appreciative of the treatment afforded to HERV in the Cold Spring Harbor text Retroviruses, edited by Drs. Coffin, Varmus, and Hughes. But to my surprise, the sources were few, and there was no information about HERV targeted to the general layman.

  As a fiction writer, however, I was in heaven--ancient viruses in our genes! And hardly anyone had heard of them. 

  If I had had any sense, I would have used that for what it seemed at face value--a ticking time bomb waiting to go off and destroy us all. But I had different ideas. I asked, what do HERV do for us? Why do we allow them to stay in our genome?  

  In fact, even in 1983, when I was preparing my novel Blood Music, I asked myself--what do viruses do for us? Why do we allow them to infect us? I suspected they were part of a scheme involving computational DNA, but could not fit them in...not just then. HIV was just coming into the public consciousness, and retroviruses were still controversial. 

  I learned that HERV express in significant numbers in pregnant women, producing defective viral particles apparently incapable of passing to another human host. So what were they--useless hangers-on? Genetic garbage? Instinctively, I could not believe that. I’ve always been skeptical of the idea of junk DNA, and certainly skeptical of the idea that the non-coding portions of DNA are deserts of slovenly and selfish disuse. 

  HERV seemed to be something weird, something wonderful and counter-intuitive--and they were somehow connected with HIV, a species-crossing retrovirus that had become one of the major health scourges on the planet. I couldn’t understand the lack of papers and other source material on HERV. Why weren’t they being investigated by every living biologist?

  In my rapidly growing novel, I wrote of Kaye Lang, a scientist who charts the possible emergence of an HERV capable of producing virions--particles that can infect other humans. To her shock, the HERV she studies is connected by investigators at the CDC with a startling new phenomenon, the apparent mutation and death of infants.  The infectious HERV is named SHEVA. But SHEVA turns out to be far more than a disease. It’s a signal prompting the expression of a new phenotype, a fresh take on humanity--a signal on Darwin’s Radio.

  In 1999, the novel was published. To my gratified surprise, it was reviewed in Nature and other science journals. Within a very few months, news items about HERV became far more common. New scientific papers reported that ERV-related genes could help human embryos implant in the womb--something that has recently been given substantial credence. And on the web, I encountered the fascinating papers of Dr. Luis P. Villarreal.

  I felt as if I had spotted a big wave early, and jumped on board just in time. Still, we have not found any evidence of infectious HERV--and there is certainly no proof that retroviruses do everything I accuse them of in Darwin’s Radio. But after four years, the novel holds up fairly well. It’s not yet completely out of date. 

  And the parallel of HERV with lysogenic phages is still startling.  

  But back to the real world of evolution and genetics.

  The picture we see now in genetics is complex. Variation can occur in a number of ways. DNA sequence is not fate; far from it. The same sequence can yield many different products. Complexes of genes lie behind most discernible traits. Genes can be turned on and off at need. Non-coding DNA is becoming extremely important to understanding how genes do their work. 

  As well, mutations are not reliable indicators of irreversible change. In many instances, mutations are self-directed responses to the environment. Changes can be reversed and then reenacted at a later time--and even passed on as reversible traits to offspring.

  Even such neo-Darwinian no-nos as the multiple reappearances of wings in stick insects points toward the existence of a genetic syntax, a phylogenetic toolbox, rather than random mutation. Wings are in the design scheme, the bauplan. When insects need them, they can be pulled from the toolbox and implemented once again.

  We certainly don’t have to throw out Mr. Darwin. Natural selection stays intact. Random variation is not entirely excised. But the neo-Darwinian dogma of random mutation as a cause of all variation, without exception, has been proven wrong.

  Like genetics, evolution is not just one process, but a collaboration of many processes and techniques. And evolution is not entirely blind. Nor must evolution be directed by some outside and supernatural intelligence to generate the diversity and complexity we see. Astonishing creativity, we’re discovering, can be explained by wonderfully complicated internal processes.

  These newer views of evolution involve learning and teamwork. Evolution is in large part about communication--comparing notes and swapping recipes, as it were.

  It appears that life has a creative memory, and knows when and how to use it. 

  Let’s take a look at what the scientists have discovered thus far. 

  Viruses can and do ferry useful genes between organisms. Viruses can also act as site-specific regulators of genetic expression. Within a cell, transposable elements--jumping genes similar in some respects to endogenous retroviruses--can also be targeted to specific sites and can regulate specific genes. Both viruses and transposable elements can be activated by stress-related chemistry, either in their capacity as selfish pathogens--a stressed organism may be a weakened organism--or as beneficial regulators of gene expression--a stressed organism may need to change its nature and behavior. 

  Viral transmission occurs not just laterally, from host to host (often during sex), but vertically through inherited mobile elements and endogenous retroviruses.

  Chemical signals between organisms can also change genetic expression. As well, changes in the environment can lead to modification of genetic expression in both the individual and in later generations of offspring. These changes may be epigenetic--factors governing which genes are to be expressed in an organism can be passed on from parent to offspring--but also genetic, in the sequence and character of genes.

  Our immune system functions as a kind of personal radar, sampling the environment and providing information that allows us to adjust our immune response--and possibly other functions, as well.

  These pathways and methods of regulation and control point toward a massive natural network capable of exchanging information--not just genes themselves, but how genes should be expressed, and when. Each gene becomes a node in a genomic network that solves problems on the cellular level. Cells talk to each other through chemistry and gene transfer. And through sexual recombination, pheromonal interaction, and viruses, multicellular organisms communicate with each other and thus become nodes in a species-wide network.

  On the next level, through predation and parasitism, as well as through cross-species exchange of genes, an ecosystem becomes a network in its own right, an interlinking of species both cooperating and competing, often at the same time.

  Neural networks from beehives to brains solve problems through the exchange and the selective cancellation and modification of signals. Species and organisms in ecosystems live and die like signals in a network. Death--the ax of natural selection--is itself a signal, a stop-code, if you will.

  Networks of signals exist in all of nature, from top to bottom--from gene exchange to the kinds of written and verbal communication we see at this event. Changes in genes can affect behavior. Sometimes even speeches can affect behavior.

  Evolution is all about competition and cooperation--and communication. 

  Traditional theories of evolution emphasize the competitive aspect and de-emphasize or ignore the cooperative aspect. But developments in genetics and molecular biology render this emphasis implausible. 

  Genes go walkabout far too often. We are just beginning to understand the marvelous processes by which organisms vary and produce the diversity of living nature.

  For now, evolution is a wonderful mystery, ripe for further scientific exploration. The gates have been blown open once again. 

  And as a science fiction writer, I’d like to make two provocative and possibly ridiculous predictions.

  The first is that the more viruses may be found in an organism and its genome, the more rapid will be that organism’s rate of mutation and evolution.

  And the second: Bacteria are such wonderful, slimmed-down organisms, lacking introns and all the persiflage of eukaryotic biology. It seems to me that rather than bacteria being primitive, and that nucleated cells evolved from them, the reverse could be true. Bacteria may once have occupied large, primitive eukaryotic cells, perhaps similar to those seen in the fossil Vendobionts--or the xenophyophores seen on ocean bottoms today. There, they evolved and swam within the relative safety of the membranous sacs, providing various services, including respiration. They may have eventually left these sacs and become both wandering minstrels and predators, serving and/or attacking other sacs in the primitive seas. 

  Eventually, as these early eukaryotic cells advanced, and perhaps as the result of a particularly vicious cycle of bacterial predation, they shed nearly all their bacterial hangers-on in a protracted phase of mutual separation, lasting hundreds of millions or even billions of years. 

  And what the now trim and super-efficient bacteria--the sports cars of modern biology--left behind were the most slavish and servile members of that former internal community: the mitochondria.

  Which group will prove to have made the best decision, to have taken the longest and most lasting road?



  Meaning-Based Natural Intelligence


  Information-Based Artificial Intelligence


  Eshel Ben Jacob and Yoash Shapira

  School of Physics and Astronomy

  Raymond & Beverly Sackler Faculty of Exact Sciences

  Tel Aviv University, 69978 Tel Aviv Israel


  In this chapter, we reflect on the concept of Meaning-Based Natural Intelligence - a

  fundamental trait of Life shared by all organisms, from bacteria to humans, associated with:

  semantic and pragmatic communication, assignment and generation of meaning, formation of

  self-identity and of associated identity (i.e., of the group the individual belongs to),

  identification of natural intelligence, intentional behavior, decision-making and intentionally

  designed self-alterations. These features place the Meaning-Based natural Intelligence

  beyond the realm of Information-based Artificial Intelligence. Hence, organisms are beyond

  man-made pre-designed machinery and are distinguishable from non-living systems.

  Our chain of reasoning begins with the simple distinction between intrinsic and

  extrinsic contextual causations for acquiring intelligence. The first, associated with natural

  intelligence, is required for the survival of the organism (the biotic system) that generates it.

  In contrast, artificial intelligence is implemented externally to fulfill a purpose for the benefit

  of the organism that engineered the “Intelligent Machinery”. We explicitly propose that the

  ability to assign contextual meaning to externally gathered information is an essential

  requirement for survival, as it gives the organism the freedom of contextual decision-making.

  By contextual, we mean relating to the external and internal states of the organism and the

  internally stored ontogenetic knowledge it has generated. We present the view that contextual

  interpretation of information and consequent decision-making are two fundamentals of

  natural intelligence that any living creature must have.


  A distinction between extraction of information from data vs. extraction of meaning from

  information is drawn while trying to avoid the traps and pitfalls of the “meaning of meaning”

  and the “emergence of meaning” paradoxes. The assignment of meaning (internal

  interpretation) is associated with identifying correlations in the information according to the

  internal state of the organism, its external conditions and its purpose in gathering the

  information. Viewed this way, the assignment of meaning implies the existence of intrinsic

  meaning, against which the external information can be evaluated for extraction of meaning.

  This leads to the recognition that the organism has self-identity.

  We present the view that the essential differences between natural intelligence and

  artificial intelligence are a testable reality, untested and ignored since it had been wrongly

  perceived as inconsistent with the foundations of physics. We propose that the inconsistency

  arises within the current, gene-network picture of the Neo-Darwinian paradigm (that regards

  organisms as equivalent to a Turing machine) and not from in principle contradiction with

  physical reality. Once the ontological reality of organisms’ natural intelligence is verified, a

  paradigm shift should be considered, where inter- and intra-cellular communication and

  genome plasticity (based on junk DNA” and the abundance of transposable elements) play

  crucial roles. In this new paradigm, communication and gene plasticity might be able to

  sustain the organisms with regulated freedom of choice between different available


  There have been many attempts to attribute the cognitive abilities of organisms (e.g.,

  consciousness) to underlying quantum-mechanical mechanisms, which can directly affect the

  ”mechanical” parts of the organism (i.e., atomic and molecular excitations) despite thermal

  noise. Here, organisms are viewed as continuously self-organizing open systems that store

  past information, external and internal. These features enable the macroscopic organisms to

  have features analogous to some features in quantum mechanical systems. Yet, they are

  essentially different and should not be mistaken to be a direct reflection of quantum effects.

  On the conceptual level, the analogy is very useful as it can lead to some insights from the

  knowledge of quantum mechanics. We show, for example, how it enables to metaphorically

  bridge between the Aharonov-Vaidman and Aharonov-Albert-Vaidman concepts of

  Protective and Weak Measurements in quantum mechanics (no destruction of the quantum

  state) with Ben Jacob’s concept of Weak-Stress Measurements, (e.g., exposure to non-lethal

  levels of antibiotic) in the study of organisms. We also reflect on the metaphoric analogy


  between Aharonov-Anandan-Popescue-Vaidman Quantum Time-Translation Machine and

  the ability of an external observer to deduce on an organism’s decision-making vs. arbitrary

  fluctuations. Inspired by the concept of Quantum Non-Demolition measurements we propose

  to use biofluoremetry (the use of bio-compatible fluorescent molecules to study intracellular

  spatio-temporal organization and functional correlations) as a future methodology of

  Intracellular Non-Demolition Measurements. We propose that the latter, performed during

  Weak-Stress Measurements of the organism, can provide proper schemata to test the special

  features associated with natural intelligence.

  Prologue - From Bacteria Thou Art

  Back in 1943, a decade before the discovery of the structure of the DNA, Schrödinger, one of

  the founders of quantum mechanics, delivered a series of public lectures, later collected in a

  book entitled “What is Life? The Physical Aspects of Living Cells” [1]. The book begins

  with an “apology” and explanation why he, as a physicist, took the liberty to embark on a

  quest related to Life sciences.

  A scientist is supposed to have a complete and thorough I of knowledge, at first hand, of

  some subjects and, therefore, is usually expected not to write on any topic of which he is

  not a life master. This is regarded as a matter of noblesse oblige. For the present

  purpose I beg to renounce the noblesse, if any, and to be the freed of the ensuing

  obligation. …some of us should venture to embark on a synthesis of facts and theories,

  albeit with second-hand and incomplete knowledge of some of them -and at the risk of

  making fools of ourselves, so much for my apology.

  Schrödinger proceeds to discuss the most fundamental issue of Mind from Matter [1-3]. He

  avoids the trap associated with a formal definition of Life and poses instead more pragmatic

  questions about the special features one would associate with living organisms - to what

  extent these features are or can be shared by non-living systems.

  What is the characteristic feature of life? When is a piece of matter said to be alive?

  When it goes on 'doing something', moving, exchanging material with its environment,

  and so forth, and that for a much longer period than we would expect of an inanimate

  piece of matter to 'keep going' under similar circumstances.


  …Let me use the word 'pattern' of an organism in the sense in which the biologist calls

  it 'the four-dimensional pattern', meaning not only the structure and functioning of that

  organism in the adult, or in any other particular stage, but the whole of its ontogenetic

  development from the fertilized egg the cell to the stage of maturity, when the organism

  begins to reproduce itself.

  To explain how the organism can keep alive and not decay to equilibrium, Schrödinger

  argues from the point of view of statistical physics. It should be kept in mind that the

  principles of non-equilibrium statistical physics [4-6] with respect to organisms, and

  particularly to self-organization in open systems [7-12], were to be developed only a decade

  later, following Turing’s papers, “The chemical basis of morphogenesis”, “The morphogen

  theory of phyllotaxis” and “Outline of the development of the daisy” [13-15].

  The idea Schrödinger proposed was that, to maintain life, it was not sufficient for organisms

  just to feed on energy, like man-made thermodynamic machines do. To keep the internal

  metabolism going, organisms must absorb low-entropy energy and exude high-entropy waste


  How would we express in terms of the statistical theory the marvelous faculty of a living

  organism, by which it delays the decay into thermodynamic equilibrium (death)? We

  said before: 'It feeds upon negative entropy', attracting, as it was a stream of negative

  entropy upon itself, to compensate the entropy increase it produces by living and thus

  to maintain itself on a stationary and fairly low entropy level. Indeed, in the case of

  higher animals we know the kind of orderliness they feed upon well enough, viz. the

  extremely well-ordered state of matter in more or less complicated organic compounds,

  which serve them as foodstuffs. After utilizing it they return it in a very much degraded

  form -not entirely degraded, however, for plants can still make use of it.

  The idea can be continued down the line to bacteria - the most fundamental independent form

  of life on Earth [16-18]. They are the organisms that know how to reverse the second law of

  thermodynamics in converting high-entropy inorganic substance into low-entropy living

  matter. They do this cooperatively, so they can make use of any available source of lowentropy

  energy, from electromagnetic fields to chemical imbalances, and release highentropy

  energy to the environment, thus acting as the only Maxwell Demons of nature. The

  existence of all other creatures depends on these bacterial abilities, since no other organism

  on earth can do it on its own. Today we understand that bacteria utilize cooperatively the

  principles of self-organization in open systems [19-36]. Yet bacteria must thrive on


  imbalances in the environment; in an ideal thermodynamic bath with no local and global

  spatio-temporal structure, they can only survive a limited time.

  In 1943, the year Schrödinger delivered his lectures, Luria and Delbruck performed a

  cornerstone experiment to prove that random mutation exists [37]: non-resistant bacteria

  were exposed to a lethal level of bacteriophage, and the idea was that only those that

  happened to go through random mutation would survive and be observed. Their experiments

  were then taken as a crucial support for the claim of the Neo-Darwinian dogma that all

  mutations are random and can occur during DNA replication only [38-41]. Schrödinger

  proposed that random mutations and evolution can in principle be accounted for by the laws

  of physics and chemistry (at his time), especially those of quantum mechanics and chemical

  bonding. He was troubled by other features of Life, those associated with the organisms’

  ontogenetic development during life. The following are additional extracts from his original

  lectures about this issue:

  Today, thanks to the ingenious work of biologists, mainly of geneticists, during the last

  thirty or forty years, enough is known about the actual material structure of organisms

  and about their functioning to state that, and to tell precisely why present-day physics

  and chemistry could not possibly account for what happens in space and time within a

  living organism.

  …I tried to explain that the molecular picture of the gene made it at least conceivable

  that the miniature code should be in one-to-one correspondence with a highly

  complicated and specified plan of development and should somehow contain the means

  of putting it into operation. Very well then, but how does it do this? How are we going

  to turn ‘conceivability’ into true understanding?

  …No detailed information about the functioning of the genetic mechanism can emerge

  from a description of its structure as general as has been given above. That is obvious.

  But, strangely enough, there is just one general conclusion to be obtained from it, and

  that, I confess, was my only motive for writing this book. From Delbruck's general

  picture of the hereditary substance it emerges that living matter, while not eluding the

  'laws of physics' as established up to date, is likely to involve 'other laws of physics'

  hitherto unknown, which, however, once they have been revealed, will form just as

  integral a part of this science as the former. This is a rather subtle line of thought, open

  to misconception in more than one respect. All the remaining pages are concerned with

  making it clear.

  With the discovery of the structure of DNA, the evidence for the one-gene-one-protein

  scheme and the discoveries of the messenger RNA and transfer RNA led to the establishment

  of the gene-centered paradigm in which the basic elements of life are the genes. According to

  this paradigm, Schrödinger’s old dilemma is due to lack of knowledge at the time, so the new


  findings render it obsolete. The dominant view since has been that all aspects of life can be

  explained solely based on the information stored in the structure of the genetic material. In

  other words, the dominant paradigm was largely assumed to be a self-consistent and a

  complete theory of living organisms [38-41], although some criticism has been raised over

  the years [42-47], mainly with emphasis on the role of bacteria in symbiogenesis of species.

  The latter was proposed in (1926) by Mereschkovsky in a book entitled "Symbiogenesis and

  the Origin of Species" and by Wallin in a book entitled "Symbionticism and the Origins of

  Species". To quote Margulis and Sagan [44]:

  The pioneering biologist Konstantin S. Merezhkovsky first argued in 1909 that the little

  green dots (chloroplasts) in plant cells, which synthesize sugars in the presence of

  sunlight, evolved from symbionts of foreign origin. He proposed that “symbiogenesis”—

  a term he coined for the merger of different kinds of life-forms into new species—was a

  major creative force in the production of new kinds of organisms. A Russian anatomist,

  Andrey S. Famintsyn, and an American biologist, Ivan E. Wallin, worked

  independently during the early decades of the twentieth century on similar hypotheses.

  Wallin further developed his unconventional view that all kinds of symbioses played a

  crucial role in evolution, and Famintsyn, believing that chloroplasts were symbionts,

  succeeded in maintaining them outside the cell. Both men experimented with the

  physiology of chloroplasts and bacteria and found striking similarities in their structure

  and function. Chloroplasts, they proposed, originally entered cells as live food—

  microbes that fought to survive—and were then exploited by their ingestors. They

  remained within the larger cells down through the ages, protected and always ready to

  reproduce. Famintsyn died in 1918; Wallin and Merezhkovsky were ostracized by their

  fellow biologists, and their work was forgotten. Recent studies have demonstrated,

  however, that the cell’s most important organelles—chloroplasts in plants and

  mitochondria in plants and animals—are highly integrated and well-organized former


  The main thesis is that microbes, live beings too small to be seen without the aid of

  microscopes, provide the mysterious creative force in the origin of species. The

  machinations of bacteria and other microbes underlie the whole story of Darwinian

  evolution. Free-living microbes tend to merge with larger forms of life, sometimes

  seasonally and occasionally, sometimes permanently and unalterably. Inheritance of

  «acquired bacteria» may ensue under conditions of stress. Many have noted that the

  complexity and responsiveness of life, including the appearance of new species from

  differing ancestors, can be comprehended only in the light of evolution. But the

  evolutionary saga itself is legitimately vulnerable to criticism from within and beyond

  science. Acquisition and accumulation of random mutations simply are, of course,

  important processes, but they do not suffice. Random mutation alone does not account

  for evolutionary novelty. Evolution of life is incomprehensible if microbes are omitted

  from the story. Charles Darwin (1809-1882), in the absence of evidence, invented

  «pangenes» as the source of new inherited variation. If he and the first evolutionist, the


  Frenchman Jean Baptiste de Lamarck, only knew about the sub visible world what we

  know today, they would have chuckled, and agreed with each other and with us.

  The Neo-Darwinian paradigm began to draw some additional serious questioning following

  the human genome project that revealed less than expected genes and more than expected

  transposable elements. The following is a quote from the Celera team [18].

  Taken together the new findings show the human genome to be far more than a

  mere sequence of biological code written on a twisted strand of DNA. It is a dynamic

  and vibrant ecosystem of its own, reminiscent of the thriving world of tiny Whos

  that Dr. Seuss' elephant, Horton, discovered on a speck of dust . . . One of the

  bigger surprises to come out of the new analysis, some of the "junk" DNA scattered

  throughout the genome that scientists had written off as genetic detritus apparently

  plays an important role after all.

  Even stronger clues can be deduced when social features of bacteria are considered: Eons

  before we came into existence, bacteria already invented most of the features that we

  immediately think of when asked to distinguish life from artificial systems: extracting

  information from data, assigning existential meaning to information from the environment,

  internal storage and generation of information and knowledge, and inherent plasticity and

  self-alteration capabilities [9].

  Let’s keep in mind that about 10% of our genes in the nucleus came, almost unchanged,

  from bacteria. In addition, each of our cells (like the cells of any eukaryotes and plans)

  carries its own internal colony of mitochondria - the intracellular multiple organelles that

  carry their own genetic code (assumed to have originated from symbiotic bacteria), inherited

  only through the maternal line. One of the known and well studied functions of mitochondria

  is to produce energy via respiration (oxidative phosphorylation), where oxygen is used to

  turn extracellular food into internally usable energy in the form of ATP. The present

  fluorescence methods allow video recording of the mitochondria dynamical behavior within

  living cells reveal that they play additional crucial roles for example in the generation of

  intracellular calcium waves in glia cells[48-50].

  Looking at the spatio-temporal behavior of mitochondria, it appears very much like that of

  bacterial colonies. It looks as if they all move around in a coordinated manner replicate and

  even conjugate like bacteria in a colony. From Schrödinger’s perspective, it seems that not


  only do they provide the rest of the cell with internal digestible energy and negative entropy

  but they also make available relevant information embedded in the spatio-temporal

  correlations of localized energy transfer. In other words, each of our cells carries hundreds to

  thousands of former bacteria as colonial Maxwell Demons with their own genetic codes, selfidentity,

  associated identity with the mitochondria in other cells (even if belong to different

  tissues), and their own collective self-interest (e.g., to initiate programmed death of their host


  Could it be, then, that the fundamental, causality-driven schemata of our natural intelligence

  have also been invented by bacteria [9,47], and that our natural intelligence is an ‘evolutionimproved

  version’, which is still based on the same fundamental principles and shares the

  same fundamental features? If so, perhaps we should also learn something from bacteria

  about the fundamental distinction between our own Natural Intelligence and the Artificial

  Intelligence of our created machinery.


  One of the big ironies of scientific development in the 20th century is that its burst of

  creativity helped establish the hegemony of a paradigm that regards creativity as an illusion.

  The independent discovery of the structure of DNA (Universal Genetic Code), the

  introduction of Chomsky’s notion about human languages (Universal Grammar – Appendix

  B) and the launching of electronic computers (Turing Universal Machines- Appendix C), all

  occurring during the 1950’s, later merged and together established the dominance of

  reductionism. Western philosophy, our view of the world and our scientific thought were

  under its influence ever since, to the extent that many hold the deep conviction that the

  Universe is a Laplacian, mechanical universe in which there is no room for renewal or

  creativity [47].

  In this Universe, concepts like cognition, intelligence or creativity are seen as mere

  illusion. The amazing process of evolution (from inanimate matter, through organisms of

  increasing complexity, to the emergence of intelligence) is claimed to be no more than a

  successful accumulation of errors (random mutations) enhanced by natural selection (the

  Darwinian picture). Largely due to the undeniable creative achievements of science,

  unhindered by the still unsolved fundamental questions, the hegemony of reductionism


  reached the point where we view ourselves as equivalent to a Universal Turing machine.

  Now, by the logical reasoning inherent in reductionism, we are not and can not be essentially

  different ‘beings’ from the machinery we can create like complex adaptive systems [51]. The

  fundamental assumption is of top-level emergence: a system consists of a large number of

  autonomous entities called agents, that individually have very simple behavior and that

  interact with each other in simple ways. Despite this simplicity, a system composed of large

  numbers of such agents often exhibits what is called emergent behavior that is surprisingly

  complex and hard to predict. Moreover, in principle, we can design and build machinery that

  can even be made superior to human cognitive abilities [52]. If so, we represent living

  examples of machines capable of creating machines (a conceptual hybrid of ourselves and

  our machines) ‘better” then themselves, which is in contradiction with the paradigmatic idea

  of natural evolution: that all organisms evolved according to a “Game of Random Selection”

  played between a master random-number generator (Nature) and a collection of independent,

  random number generators (genomes). The ontological reality of Life is perceived as a game

  with two simple rules – the second law of thermodynamics and natural selection. Inherent

  meaning and causality-driven creativity have no existence in such a reality – the only

  meaning of life is to survive. If true, how come organisms have inherent programming to

  stop living? So here is the irony: that the burst of real creativity was used to remove

  creativity from the accepted epistemological description of Nature, including life.

  The most intriguing challenge associated with natural intelligence is to resolve the

  difficulty of the apparent contradiction between its fundamental concepts of decision-making

  and creativity and the fundamental principle of time causality in physics. Ignoring the trivial

  notion, that the above concepts have no ontological reality, intelligence is assumed to reflect

  Top-Level-Emergence in complex systems. This commonly accepted picture represents the

  “More is Different” view [53], of the currently hegemonic reductionism-based

  constructivism paradigm. Within this paradigm, there are no primary differences between

  machinery and living systems, so the former can, in principle, be made as intelligent as the

  latter and even more. Here we argue that constructivism is insufficient to explain natural

  intelligence, and all-level generativism, or a “More is Different on All Levels” principle, is

  necessary for resolving the emergence of the meaning paradox [9]. The idea is the cogeneration

  of meaning on all hierarchical levels, which involves self-organization and

  contextual alteration of the constituents of the biotic system on all levels (down to the


  genome) vs. top-level emergence in complex systems with pre-designed and pre-prepared

  elements [51,52].

  We began in the prologue with the most fundamental organisms, bacteria,

  building the argument towards the conclusion that recent observations of bacterial collective

  self-identity place even them, and not only humans, beyond a Turing machine: Everyone

  agrees that even the most advanced computers today are unable to fully simulate even an

  individual, most simple bacterium of some 150 genes, let alone more advanced bacteria

  having several thousands of genes, or a colony of about 1010 such bacteria. Within the current

  Constructivism paradigm, the above state of affairs reflects technical or practical rather than

  fundamental limitations. Namely, the assumption is that any organelle, our brain included, as

  well as any whole organism, is in principle equivalent to, and thus may in principle be

  mapped onto a universal Turing Machine – the basis of all man-made digital information

  processing machines (Appendix C). We argue otherwise. Before doing so we will place

  Turing’s notions about “Intelligent Machinery” [54] and “Imitation Game” [55] within a new

  perspective [56], in which any organism, including bacteria, is in principle beyond machinery

  [9,47]. This realization will, in turn, enable us to better understand ourselves and the

  organisms our existence depends on – the bacteria.

  To make the argument sound, we take a detour and reflect on the philosophical

  question that motivated Turing to develop his conceptual computing machine: We present

  Turing’s universal machine within the causal context of its invention [57], as a manifestation

  of Gödel’s theorem [58-60], by itself developed to test Hilbert’s idea about formal axiomatic

  systems [61]. Then we continued to reexamine Turing’s seminal papers that started the field

  of Artificial Intelligence, and argue that his “Imitation Game”, perceived ever since as an

  “Intelligence Test”, is actually a “Self-Non-Self Identity Test”, or “Identity Game” played

  between two humans competing with a machine by rules set from machines perspective, and

  a machine built by another human to win the game by presenting a false identity.

  We take the stand that Artificial and Natural Intelligence are distinguishable, but not

  by Turing’s imitation game which is set from machines perspective - the rules of the game

  simply do not allow expression of the special features of natural intelligence. Hence, for

  distinction between the two versions of Intelligence, the rules of the game must be modified


  in various ways. Two specific examples are presented, and it is propose that it’s unlikely for

  machines to win these new versions of the game.

  Consequently, we reflect on the following questions about natural intelligence: 1. Is it a

  metaphor or overlooked reality? 2. How can its ontological reality be tested? 3. Is it

  consistent with the current gene-networks picture of the Neo-Darwinian paradigm? 4. Is it

  consistent with physical causal determinism and time causality? To answer the questions, we

  first present the current accepted picture of organisms as ‘watery Turing machines’ living in

  a predetermined Laplacian Universe. We then continue to describe the ‘creative genome’

  picture and a new perspective of the organism as a system with special built-in means to

  sustain ‘learning from experience’ for decision-making [47]. For that, we reflect on the

  analogy between the notions of the state of multiple options in organisms, the choice function

  in the Axiom of Choice in mathematics (Appendix D) and the superposition of states in

  quantum mechanics (Appendix E). According to the analogy, destructive quantum

  measurements (that involve collapse of the wave function) are equivalent to strong-stress

  measurements of the organisms (e.g., lethal levels of antibiotics) and to intracellular

  destructive measurements (e.g., gene-sequencing and gene-expression in which the organism

  is disassembled). Inspired by the new approach of protective quantum measurements, which

  do not involve collapse of the wave function (Appendix E), we propose new conceptual

  experimental methodologies of biotic protective measurements - for example, by exposing

  the organisms to weak stress, like non-lethal levels of antibiotic [62,63], and by using

  fluoremetry to record the intracellular organization and dynamics keeping the organism intact


  Formation of self-identity and of associated identity (i.e., of the group the individual belongs

  to), identification of natural intelligence in other organisms, intentional behavior, decisionmaking

  [67-75] and intentionally designed self-alterations require semantic and pragmatic

  communication [76-80], are typically associated with cognitive abilities and meaning-based

  natural intelligence of human. One might accept their existence in the “language of dolphins”

  but regard them as well beyond the realm of bacterial communication abilities. We propose

  that this notion should be reconsidered: New discoveries about bacterial intra- and intercellular

  communication [81-92], colonial semantic and pragmatic language [9,47,93,94], the

  above mentioned picture of the genome [45-47], and the new experimental methodologies

  led us to consider bacterial natural intelligence as a testable reality.


  Can Organisms be Beyond Watery Turing Machines

  in Laplace’s Universe?

  The objection to the idea about organisms’ regulated freedom of choice can be traced to the

  Laplacian description of Nature. In this picture, the Universe is a deterministic and

  predictable machine composed of matter parts whose functions obey a finite set of rules with

  specified locality [95-98]. Laplace has also implicitly assumed that determinism,

  predictability and locality go hand in hand with computability (using current terminology),

  and suggested that:

  “An intellect which at any given moment knew all the forces that animate Nature and

  the mutual positions of the beings that comprises it. If this intellect were vast enough to

  submit its data to analysis, could condense into a single formula the movement of the

  greatest bodies of the universe and that of the lightest atom: for such an intellect

  nothing could be uncertain: and the future just like the past would be present before its


  Note that this conceptual intellect (Lacplace’s demon) is assumed to be an external observer,

  capable, in principle, of performing measurements without altering the state of the system,

  and, like Nature itself, equivalent to a universal Turing machine.

  In the subsequent two centuries, every explicit and implicit assumption in the

  Laplacean paradigm has proven to be wrong in principle (although sometimes a good

  approximation on some scales). For example, quantum mechanics ruled out locality and the

  implicit assumption about simultaneous and non-destructive measurements. Studies in

  computer sciences illustrate that a finite deterministic system (with sufficient algorithmic

  complexity) can be beyond Turing machine computability (the size of the required machine

  should be comparable with that of the whole universe or the computation time of a smaller

  machine would be comparable with the time of the universe). Computer sciences, quantum

  measurements theory and statistical physics rule out backward computability even if the

  present state is accurately known.


  Consequently, systems’ unpredictability to an external observer is commonly

  accepted. Yet, it is still largely assumed that nature itself as a whole and any of its parts must

  in principle be predetermined, that is, subject to causal determinism [98],which must go hand

  in hand with time causality [96]:

  Causal determinism is the thesis that all events are causally necessitated by prior

  events, so that the future is not open to more than one possibility. It seems to be

  equivalent to the thesis that the future is in principle completely predictable (even if

  in practice it might never actually be possible to predict with complete accuracy).

  Another way of stating this is that for everything that happens there are conditions

  such that, given them, nothing else could happen, meaning that a completely

  accurate prediction of any future event could in principle be given, as in the famous

  example of Laplace’s demon.

  Clearly, a decomposable state of mixed multiple options and hence decision-making

  can not have ontological reality in a universe subject to ‘causal determinism’. Moreover, in

  this Neo-Laplacian Universe, the only paradigm that does not contradict the foundations of

  logic is the Neo-Darwinian one. It is also clear that in such clockwork universe there can not

  be an essential difference, for example, between self-organization of a bacterial colony and

  self-organization of a non living system such as electro-chemical deposition [99,100].

  Thus, all living organisms, from bacteria to humans, could be nothing but watery Turing

  machines created and evolved by random number generators. The conviction is so strong that

  it is pre-assumed that any claim regarding essential differences between living organisms and

  non living systems is an objection to the foundations of logic, mathematics, physics and

  biology. The simple idea, that the current paradigm in life sciences might be the source of the

  apparent inconsistency and hence should be reexamined in light of the new discoveries, is

  mostly rejected point-blank.

  In the next sections we present a logical argument to explain why the Neo-Laplacian

  Universe (with a built-in Neo-Darwinian paradigm) can not provide a complete and selfconsistent

  description of Nature even if random number generators are called for the rescue.

  The chain of reasoning is linked with the fact that formal axiomatic systems cannot provide

  complete bases for mathematics and the fact that a Universal Turing Machine cannot answer

  all the questions about its own performance.

  Hilbert’s Vision –


  Meaning-Free Formal Axiomatic Systems

  Computers were invented to clarify Gödel’s theorem, which by itself has been triggered by

  the philosophical question about the foundation of mathematics raised by Russell’s logical

  paradoxes [61]. These paradoxes attracted much attention, as they appeared to shatter the

  solid foundations of mathematics, the most elegant creation of human intelligence. The best

  known paradox has to do with the logical difficulty to include the intuitive concept of selfreference

  within the foundations of Principia Mathematica: If one attempts to define the set

  of all sets that are not elements of themselves, a paradox arises - that if the set is to be an

  element of itself, it shouldn’t, and vice versa.

  As an attempt to eliminate such paradoxes from the foundations of mathematics, Hilbert

  invented his meta-mathematics. The idea was to lay aside the causal development of

  mathematics as a meaningful ‘tool’ for our survival, and set up a formal axiomatic system so

  that a meaning-independent mathematics can be built starting from a set of basic postulates

  (axioms) and well-defined rules of deduction for formulating new definitions and theorems

  clean of paradoxes. Such a formal axiomatic system would then be a perfect artificial

  language for reasoning, deduction, computing and the description of nature. Hilbert’s vision

  was that, with the creation of a formal axiomatic system, the causal meaning that led to its

  creation could be ignored and the formal system treated as a perfect, meaning-free game

  played with meaning-free symbols on paper.

  His idea seemed very elegant - with “superior” rules, “uncontaminated” by meaning, at

  our disposal, any proof would not depend any more on the limitation of human natural

  language with its imprecision, and could be executed, in principle, by some advanced,

  meaning-free, idealized machine. It didn’t occur to him that the built-in imprecision of

  human linguistics (associated with its semantic and pragmatic levels) are not a limitation but

  rather provide the basis for the flexibility required for the existence of our creativity-based

  natural intelligence. He overlooked the fact that the intuitive (semantic) meanings of

  intelligence and creativity have to go hand in hand with the freedom to err – there is no room

  for creativity in a precise, clockwork universe.

  Gödel’s Incompleteness/Undecidability Theorem


  In 1931, in a monograph entitled “On Formally Undecidable Propositions of Principia

  Mathematica and Related Systems” [58-61], Gödel proved that Hilbert’s vision was in

  principle wrong - an ideal ‘Principia Mathematica’ that is both self-consistent and complete

  can not exist.

  Two related theorems are formulated and proved in Gödel’s paper: 1. The

  Undecidability Theorem - within formal axiomatic systems there exist questions that are

  neither provable nor disprovable solely on the basis of the axioms that define the system. 2.

  The Incompleteness Theorem - if all questions are decidable then there must exist

  contradictory statements. Namely, a formal axiomatic system can not be both self-consistent

  and complete.

  What Gödel showed was that a formal axiomatic system is either incomplete or

  inconsistent even if just the elementary arithmetic of the whole numbers 0,1,2,3, is

  considered (not to mention all of mathematics). He bridged between the notion of selfreferential

  statements like “This statement is false” and Number Theory. Clearly,

  mathematical statements in Number Theory are about the properties of whole numbers,

  which by themselves are not statements, nor are their properties. However, a statement of

  Number Theory could be about a statement of Number Theory and even about itself (i.e.,

  self-reference). To show this, he constructed one-to-one mapping between statements about

  numbers and the numbers themselves. In Appendix D, we illustrate the spirit of Gödel’s


  Gödel’s coding allows regarding statements of Number Theory on two different levels:

  (1) as statements of Number Theory, and (2) as statements about statements of Number

  Theory. Using his code, Gödel transformed the Epimenides paradox (“This statement is

  false”) into a Number Theory version: “This statement of Number Theory is improvable”.

  Once such a statement of Number Theory that describes itself is constructed, it proves

  Gödel’s theorems. If the statement is provable then it is false, thus the system is inconsistent.

  Alternatively, if the statement is improvable, it is true but then the system is incomplete.

  One immediate implication of Gödel’s theorem is that no man-made formal axiomatic

  system, no matter how complex, is sufficient in principle to capture the complexity of the

  simplest of all systems of natural entities – the natural whole numbers. In simple words, any


  mathematical system we construct can not be prefect (self-consistent and complete) on its

  own – some of its statements rely on external human intervention to be settled. It is thus

  implied that either Nature is not limited by causal determinism (which can be mapped onto a

  formal axiomatic system), or it is limited by causal determinism and there are statement

  about nature that only an external Intelligence can resolve.

  The implications of Gödel’s theorem regarding human cognition are still under

  debate [108]. According to the Lucas-Penrose view presented in “Minds, Machines and

  Gödel” by Lucas [101] and in “The emperor’s new mind: concerning computers, minds and

  the law of physics” by Penrose [73], Gödel’s theorems imply that some of the brain functions

  must act non-algorithmically. The popular version of the argumentation is: There exist

  statements in arithmetic which are undecidable for any algorithm yet are intuitively decidable

  for mathematicians. The objection is mainly to the notion of ‘intuition-based mathematical

  decidability’. For example, Nelson in “Mathematics and the Mind” [109], writes:

  For the argumentation presented in later sections, we would like to highlight the

  following: Russell’s paradoxes emerge when we try to assign the notion of self-reference

  between the system and its constituents. Unlike living organisms, the sets of artificial

  elements or Hilbert’s artificial systems of axioms are constructed from fixed components

  (they do not change due to their assembly in the system) and with no internal structure that

  can be a functional of the system as a whole as it is assembled. The system itself is also fixed

  in time or, more precisely, has no temporal ordering. The set is constructed (or the system of

  axioms is defined) by an external spectator who has the information about the system, i.e.,

  the system doesn’t have internally stored information about itself and there are no intrinsic

  causal links between the constituents.


  Turing’s Universal Computing Machine

  Gödel’s theorem, though relating to the foundations of mathematical philosophy, led Alan

  Turing to invent the concept of computing machinery in 1936. His motivation was to test the

  relevance of three possibilities for formal axiomatic systems that are left undecidable in

  Gödel’s theorems: 1. they can not be both self consistent and complete but can be either; 2.

  they can not be self-consistent; 3. they can not be complete. Turing proved that formal

  axiomatic systems must be at least incomplete.

  To prove his theorem, Gödel used his code to map both symbols and operations. The

  proof itself, which is quite complicated, utilizes many recursively defined functions. Turing’s

  idea was to construct mapping between the natural numbers and their binary representation

  and to include all possible transformations between them to be performed by a conceptual

  machine. The latter performs the transformation according to a given set of pre-constructed

  instructions (program). Thus, while Gödel used the natural numbers themselves to prove his

  theorems, Turing used the space of all possible programs, which is why he could come up

  with even stronger statements. For later reflections, we note that each program can be

  perceived as functional correlation between two numbers. In other words the inherent

  limitations of formal axiomatic systems are better transparent in the higher dimension space

  of functional correlations between the numbers.

  Next, Turing looked for the kind of questions that the machine in principle can’t

  solve irrespective of its physical size. He proved that the kinds of questions the machine can

  not solve are about its own performance. The best known is the ‘halting problem’: the only

  way a machine can know if a given specific program will stop within a finite time is by

  actually running it until it stops.

  The proof is in the spirit of the previous “self-reference games”: assume there is a

  program that can check whether any computer program will stop (Halt program). Prepare

  another program which makes an infinite loop i.e., never stops (Go program). Then, make a

  third Dual program which is composed of the first two such that a positive result of the Halt-

  Buster part will activate the Go-Booster part. Now, if the Dual program is fed as input to the

  Halt-Buster program it leads to a paradox: the Dual program is constructed so that, if it is to


  stop, the Halt-Buster part will activate the Go-Booster part so it shouldn’t stop and vice

  versa. In a similar manner it can be proven that Turing machine in principle can not answer

  questions associated with running a program backward in time.

  Turing’s proof illustrates the fact that the notion of self-reference can not be part of

  the space of functional correlations generated by Universal Turing machine. In this sense,

  Turing proved that if indeed Nature is equivalent to his machine (the implicit assumption

  associated with causal determinism), we, as parts of this machine, can not in principle

  generate a complete description of its functioning - especially so with regard to issues related

  to systems’ self-reference.

  The above argumentations appear as nothing more than, at best, an amusing game.

  Four years later (in 1940), Turing converted his conceptual machine into a real one – the first

  electronic computer The Enigma, which helped its human users decipher codes used by

  another machine. For later discussion we emphasize the following: The Enigma provided the

  first illustration, that while Turing machine is limited in answering on its own questions

  about itself, it can provide a useful tool to aid humans in answering questions about other

  systems, both artificial and natural. In other words, Turing machine can be a very useful tool

  to help humans design another, improved Turing machine, but it is not capable of doing so on

  its own - it can not answer questions about itself. In this sense, stand alone machines can not

  have in principle the features we proposed to associate with natural intelligence.

  The Birth of Artificial Intelligence –

  Turing’s Imitation Game

  In his 1936 paper [57], Turing claims that a universal computing machine of the kind he

  proposed can, in principle, perform any computation that a human being can carry out. Ten

  years later, he began to explore the potential range of functional capabilities of computing

  machinery beyond computing and in 1950 he published an influential paper, “Computing

  Machinery and Intelligence” [55], which led to the birth of Artificial Intelligence. The paper

  starts with a statement:

  “I propose to consider the question, ‘Can machine think?’ This should begin with

  definitions of the meaning of the terms ‘machine’ and ‘think’. The definitions might be


  framed so as to reflect so far as possible the normal use of the words, but this attitude is


  So, in order to avoid the pitfalls of definitions of terms like ‘think’ and

  ‘intelligence’, Turing suggested replacing the question by another, which he claimed

  “...is closely related to it and is expressed in relatively unambiguous words. The new

  form of the problem can be described in terms of a game which we call the ‘imitation


  This proposed game, known as Turing’s Intelligence Test, involves three players: a

  human examiner of identities I, and two additional human beings, each having a different

  associated identity. Turing specifically proposed to use gender identity: a man A and a

  woman B. The idea of the game is that the identifier I knows (A;B) as (X;Y) and he has to

  identify, by written communication, who is who, aided by B (a cooperator) against the

  deceiving communication received from A (a defector). The purpose of I and B is that I will

  be able to identify who is A. The identity of I is not specified in Turing’s paper saying that he

  may be of either sex.

  It is implicitly assumed that the three players have a common language, which can be used

  also by machines, and that I, A, and B also have a notion about the identity of the other

  players. Turing looked at the game from a machinery vs. human perspective, asking

  ‘What will happen when a machine takes the part of A in this game?’

  He proposed that a machine capable of causing I to fail in his identifications as often as a

  man would, should be regarded intelligent. That is, the rate of false identifications of A made

  by I with the aid of B is a measure of the intelligence of A.

  So, Turing’s intelligence test is actually about self identity and associated identity and

  the ability to identify non-self identity of different kinds! Turing himself referred to his game

  as an ‘imitation game’. Currently, the game is usually presented in a different version - an

  intelligent being I has to identify who the machine is, while the machine A attempts to

  imitated intelligent being. Moreover, it is perceived that the Inquirer I bases his identification

  according to which player appears to him more intelligent. Namely, the game is presented as


  an intelligence competition, and not about Self-Non-Self identity as was originally proposed

  by Turing.

  From Kasparov’s Mistake to Bacterial Wisdom

  Already in 1947, in a public lecture [15], Turing presented a vision that within 50 years

  computers will be able to compete with people in the chess game. The victory of Deep Blue

  over Kasparov exactly 50 years later is perceived today by many, scientists and layman alike,

  as clear proof for computers’ Artificial Intelligence [109,110]. Turing himself considered

  success in a chess game only a reflection of superior computational capabilities (the

  computer’s ability to compute very fast all possible configurations). In his view, success in

  the imitation game was a greater challenge. In fact, the connection between success in the

  imitation game and intelligence is not explicitly discussed in his 1950 paper. Yet, it has

  become to be perceived as an intelligence test and led to the current dominant view of

  Artificial Intelligence, that in principle any living organism is equivalent to a universal

  Turing machine [107-110].

  Those who view the imitation game as an intelligence test of the machine

  usually assume that the machine’s success in the game reflects the machine’s inherent talent.

  We follow the view that the imitation game is not about the machine’s talent but about the

  talent of the designer of the machine who ‘trained it’ to play the role of A.

  The above interpretation is consistent with Kasparov’s description of his chess

  game with Deep Blue. According to him, he lost because he failed to foresee that after the

  first match (which he won) the computer was rebuilt and reprogrammed to play positional

  chess. So Kasparov opened with the wrong strategy, thus losing because of wrong decisionmaking

  not in chess but in predicting the intentions of his human opponents (he wrongly

  assumed that computer designing still hasn’t reached the level of playing positional chess).

  Thus he lost because he underestimated his opponents. The ability to properly evaluate self

  intelligence in comparison to that of others is an essential feature of natural intelligence. It

  illustrates that humans with higher analytical skills can have lower skills associated with

  natural intelligence and vice versa: the large team that designed and programmed Deep Blue

  properly evaluated Kasparov’s superior talent relative to that of each one of them on its own.


  So, before the second match, they extended their team. Bacteria, being the most primordial

  organisms, had to adopt a similar strategy to survive when higher organisms evolved. The

  “Bacterial Wisdom” principle [9,47], is that proper cooperation of individuals driven by a

  common goal can generate a new group-self with superior collective intelligence. However,

  the formation of such a collective self requires that each of the individuals will be able to

  alter its own self and adapt it to that of the group’s (Appendix A).

  Information-Based Artificial Intelligence vs.

  Meaning-Based Natural Intelligence

  We propose to associate (vs. define) meaning-based, natural intelligence with: conduction of

  semantic and pragmatic communication, assignment and generation of meaning, formation of

  self-identity (distinction between intrinsic and extrinsic meaning) and of associated identity

  (i.e., of the group the individual belongs to), identification of natural intelligence in other

  organisms, intentional behavior, decision-making and intentionally designed self alterations.

  Below we explain why this features are not likely to be sustained by a universal Turing

  machine, irrespective of how advanced its information-based artificial intelligence might be.

  Turing set his original imitation game to be played by machine rules: 1. The selfidentities

  are not allowed to be altered during the game. So, for example, the cooperators can

  not alter together their associated identity - the strategy bacteria adopt to identify defectors. 2.

  The players use fixed-in-time, universal-machine-like language (no semantic and pragmatic

  aspects). In contrast, the strategy bacteria use is to modify their dialect to improve the

  semantic and pragmatic aspect of their communication. 3. The efficiency of playing the game

  has no causal drive, i.e., there is no reward or punishment. 4. The time frame within which

  the game is to be played is not specified. As a result, there is inherent inconsistency in the

  way Turing formulated his imitation game, and the game can not let the special features of

  natural intelligence be expressed.

  As Turing proved, computing machines are equivalent to formal axiomatic systems

  that are constructed to be clean of meaning. Hence, by definition, no computer can generate

  its own intrinsic meanings that are distinguishable from externally imposed ones. Which, in

  turn, implies the obvious – computers can not have inherent notions of identity and self22

  identity. So, if the statement: ‘When a machine takes the part of A in this game’ refers to the

  machine as an independent player, the game has to be either inconsistent or undecidable. By

  independent player we mean the use of some general-purpose machine (i.e., designed without

  specific task in mind, which is analogous to the construction of a meaning-free, formal

  axiomatic system). The other possibility is that Turing had in mind a specific machine,

  specially prepared for the specific game with the specific players in mind. In this case, the

  formulation of the game has no inconsistency/undecidability, but then the game is about the

  meaning-based, causality-driven creativity of the designer of the machine and not about the

  machine itself. Therefore, we propose to interpret the statement ‘When a machine takes the

  part of A’ as implying that ‘A sends a Pre-designed and Pre-programmed machine to play

  his role in the specific game’.

  The performance of a specific machine in a specific game is information-based

  Artificial Intelligence. The machine can even perform better than some humans in the

  specific game with agreed-upon, fixed rules (time invariant); it has been designed to play.

  However, the machine is the product of the meaning-based Natural Intelligence and the

  causality-driven creativity of its designer. The designer can design different machines

  according to the causal needs he foresees. Moreover, by learning from his experience and by

  using purposefully gathered knowledge, he can improve his skills to create better machines.

  It seems that Turing did realize the essential differences between some of the features

  we associate here with Natural Intelligence vs. Artificial Intelligence. So, for example, he

  wouldn’t have classified Deep Blue as an Intelligent Machine. In an unpublished report from

  1948, entitled “Intelligent Machinery”, machine intelligence is discussed mainly from the

  perspective of human intelligence. In this report, Turing explains that intelligence requires

  learning, which in turn requires the machine to have sufficient flexibility, including selfalteration

  capabilities (the equivalent of today’s neuro-plasticity). It is further implied that

  the machine should have the freedom to make mistakes. The importance of reward and

  punishment in the machine learning is emphasized (see the report summary shown below).

  Turing also relates the machine’s learning capabilities to what today would be referred to as

  genetic algorithm, one which would fit the recent realizations about the genome (Appendix


  In this regard, we point out that organisms’ decision-making and creativity which are

  based on learning from experience (explained below) must involve learning from past


  mistakes. Hence, an inseparable feature of natural intelligence is the freedom to err with

  readiness to bear the consequences.

  Beyond Machinery - Games of Natural Intelligence

  Since the rules of Turing’s imitation game do not let the special features of natural

  intelligence be expressed the game can not be used to distinguish natural from artificial

  intelligence. The rules of the game must be modified to let the features of natural intelligence

  be expressed, but in a manner machines can technically imitate.

  First, several kinds of communication channels that can allow exchange of

  meaning-bearing messages should be included, in addition to the written messages. Clearly,

  all communication channels should be such that can be transferred and synthesized by a

  machine; speech, music, pictures and physiological information (like that used in polygraph

  tests) are some examples of such channels. We emphasize that a two-way communication is

  used so, for example, the examiner (I) can present to (B) a picture he asked (A) to draw and

  vice versa. Second, the game should be set to test the ability of human (I) vs. machine (I) to

  make correct identification of (A) and (B), instead of testing the ability of human (A) vs.

  machine (A) to cause human (I) false identifications. Third, the game should start after the


  examiner (I) has had a training period. Namely, a period of time during which he is let to

  communicate with (A) and (B) knowing who is who, to learn from his own experience about

  their identities. Both the training period and the game itself should be for a specified

  duration, say an hour each. The training period can be used by the examiners in various

  ways; for example, he can expose the players to pictures, music pieces, extracts from

  literature, and ask them to describe their impressions and feelings. He can also ask each of

  them to reflect on the response of the other one or explain his own response. Another

  efficient training can be to ask each player to create his own art piece and reflect on the one

  created by the other. The training period can also be used by the examiner (I) to train (B) in

  new games. For example, he could teach the other players a new game with built-in rewards

  for the three of them to play. What we suggest is a way to instill in the imitation game

  intrinsic meaning for the player by reward and decision-making.

  The game can be played to test the ability of machine (I) vs. human (I) to

  distinguish correctly between various kinds of identities: machine vs. human (in this case, the

  machine should be identical to the one who plays the examiner), or two associated human

  identities (like gender, age, profession etc).

  The above are examples of natural intelligence games we expect machinery to

  lose, and as such they can provide proper tests to distinguish their artificial intelligence from

  the natural intelligence of living systems.

  Let Bacteria Play the Game of Natural Intelligence

  We proposed that even bacteria have natural intelligence beyond machinery: unlike a

  machine, a bacterial colony can improve itself by alteration of gene expression, cell

  differentiation and even generation of new inheritable genetic ‘tools’. During colonial

  development, bacteria collectively use inherited knowledge together with causal information

  it gathers from the environment, including other organisms (Appendix A). For that, semantic

  chemical messages are used by the bacteria to conduct dialogue, to cooperatively assess their

  situation and make contextual decisions accordingly for better colonial adaptability

  (Appendix B). Should these notions be understood as useful metaphors or as disregarded



  Another example of natural intelligence game could be a Bridge game between a

  machine and human team playing the game against a team of two human players. This

  version of the game is similar to the real life survival ‘game’ between cooperators and

  cheaters (cooperative behavior of organisms goes hand in hand with cheating, i.e., selfish

  individuals who take advantage of the cooperative effort). An efficient way cooperators can

  single out the defectors is by using their natural intelligence - semantic and pragmatic

  communication for collective alteration of their own identity, to outsmart the cheaters who

  use their own natural intelligence for imitating the identity of the cooperators [111-114].

  In Appendix A we describe how even bacteria use communication to generate

  evolvable self-identity together with special “dialect”, so fellow bacteria can find each one in

  the crowd of strangers (e.g., biofilms of different colonies of the same and different species).

  For that, they use semantic chemical messages that can initiate specific alteration only with

  fellow bacteria and with shared common knowledge (Appendix C). So in the presence of

  defectors they modify their self-identity in a way unpredictable to an external observer not

  having the same genome and specific gene-expression state. The external observer can be

  other microorganisms, our immune system or our scientific tools.

  The experimental challenge to demonstrate the above notions is to devise an identity

  game bacteria can play to test if bacteria can conduct a dialogue to recognize self vs. non-self

  [111-114]. Inspired by Turing’s imitation game, we adopted a new conceptual methodology

  to let the bacteria tell us about their self-identity, which indeed they do: Bacterial colonies

  from the same culture are grown under the same growth conditions to show that they exhibit

  similar-looking patterns (Fig 1), as is observed during self-organization of azoic systems

  [7,8,99,100]. However, unlike for azoic systems, each of the colonies develops its own self

  identity in a manner no azoic system is expected to do.


  Fig 1. Observed level of reproducibility during colonial developments: Growth of two

  colonies of the Paenibacillus vortex taken from the same parent colony and under the same growth conditions.

  For that, the next stage is to growth of four colonies on the same plate. In one case all are

  taken from the same parent colony and in the other case they are taken from two different yet

  similar-looking colonies (like those shown in Fig 1). In preliminary experiments we found

  that the growth patterns in the two cases are significantly different. These observations imply

  that the colonies can recognize if the other colonies came from the same parent colony or

  from a different one. We emphasize that this is a collective phenomenon, and if the bacteria

  taken from the parent colonies are first grown as isolated bacteria in fluid, the effect is

  washed out.

  It has been proposed that such colonial self-identity might be generated during the

  several hours of stationary ‘embryonic stage’ or collective training duration of the colonies

  between the time they are placed on the new surface and start to expand. During this

  duration, they collectively generate their own specific colonial self identity [62,63]. These

  findings revive Schrödinger’s dilemma, about the conversion of genetic information

  (embedded in structural coding) into a functioning organism. A dilemma largely assumed to

  be obsolete in light of the new experimental findings in life sciences when combined with the

  Neo-Darwinian the Adaptive Complex Systems paradigms [51,115-120]. The latter, currently

  the dominant paradigm in the science of complexity is based on the ‘top-level emergence’

  principle which has evolved from Anderson’s constructivism (‘More is Different’ [53]).


  Beyond Neo-Darwinism – Symbiogenesis on All Levels

  Accordingly it is now largely assumed that all aspects of life can in principle be explained

  solely on the basis of information storage in the structure of the genetic material. Hence, an

  individual bacterium, bacterial colony or any eukaryotic organism is in principle analogous

  to a pre-designed Turing machine. In this analogy, the environment provides energy (electric

  power of the computer) and absorbs the metabolic waste products (the dissipated heat), and

  the DNA is the program that runs on the machine. Unlike in an ordinary Turing machine, the

  program also has instructions for the machine to duplicate and disassemble itself and

  assemble many machines into an advanced machine – the dominant Top-Level Emergence

  view in the studies of complex systems and system-biology based on the Neo-Darwinian


  However, recent observations during bacterial cooperative self-organization show features

  that can not be explained by this picture (Appendix A). Ben Jacob reasoned that Anderson’s

  constructivism is insufficient to explain bacterial self-organization. Hence, it should be

  extended to a “More is Different on All Levels” or all-level generativism [9]. The idea is that

  biotic self-organization involves self-organization and contextual alteration of the

  constituents of the biotic system on all levels (down to the genome). The alterations are based

  on stored information, external information, information processing and collective decisionmaking

  following semantic and pragmatic communication on all levels. Intentional

  alterations (neither pre-designed nor due to random changes) are possible, however, only if

  they are performed on all levels. Unlike the Neo-Darwinian based, top-level emergence, alllevel

  emergence can account for the features associated with natural intelligence. For

  example, in the colony, communication allows collective alterations of the intracellular state

  of the individual bacteria, including the genome, the intracellular gel and the membrane. For

  bacterial colony as an organism, all-level generativism requires collective ‘natural genetic

  engineering’ together with ‘creative genomic webs’ [45-47]. In a manuscript entitled:

  “Bacterial wisdom, Gödel’s theorem and Creative Genomic Webs”, Ben Jacob refers to the

  following special genomic abilities of individual bacteria when being the building agents of a



  In the prologue we quoted Margulis’ and Sagan’s criticisms of the incompleteness of the

  Neo-Darwinian paradigm and the crucial role of symbiogenesis in the transition from

  prokaryotes to eukaryotes and the evolution of the latter. With regard to eukaryotic

  organisms, an additional major difficulty arises from the notion that all the required

  information to sustain the life of the organism is embedded in the structure of its genetic

  code: this information seems useless without the surrounding cellular machinery [123,124].

  While the structural coding contains basic instructions on how to prepare many components

  of the machinery – namely, proteins – it is unlikely to contain full instructions on how to

  assemble them into multi-molecular structures to create a functional cell. We mentioned

  mitochondria that carry their own genetic code. In addition, membranes, for example, contain

  lipids, which are not internally coded but are absorbed from food intake according to the

  functional state of the organism.

  Thus, we are back to Schrödinger’s chicken-and-egg paradox – the coding parts of the DNA

  require pre-existing proteins to create new proteins and to make them functional. The

  problem may be conceptually related to Russell’s self-reference paradoxes and Gödel’s

  theorems: it is possible in principle to construct mapping between the genetic information

  and statements about the genetic information. Hence, according to a proper version of

  Gödel’s theorem (for finite system [47]), the structural coding can not be both complete and

  self-consistent for the organism to live, replicate and have programmed cell death. In this

  sense, the Neo-Darwinian paradigm can not be both self-consistent and complete to describe


  the organism’s lifecycle. In other words, within this paradigm, the transition from the coding

  part of the DNA to the construction of a functioning organism is metaphorically like the

  construction of mathematics from a formal axiomatic system. This logical difficulty is

  discussed by Winfree [125] in his review on Delbruck’s book “Mind from Matter? An Essay

  on Evolutionary Epistemology”.


  New discoveries about the role of transposable elements and the abilities of the Junk DNA to

  alter the genome (including generation of new genes) during the organism’s lifecycle support

  the new picture proposed in the above mentioned paper. So, it seems more likely now that

  indeed the Junk DNA and transposable elements provide the necessary mechanisms for the

  formation of creative genomic webs. The human genome project provided additional clues

  about the functioning of the genome, and in particular the Junk DNA in light of the

  unexpectedly low number of coding genes together with equally unexpectedly high numbers

  of transposable elements, as described in Appendix B. These new findings on the genomic

  level together with the new understanding about the roles played by mitochondria [126-132]

  imply that the current Neo-Darwinian paradigm should be questioned. Could it be that

  mitochondria – the intelligent intracellular bacterial colonies in eukaryotic cells, provide a

  manifestation of symbiogenesis on all levels?

  Learning from Experience –

  Harnessing the Past to Free the Future

  Back to bacteria, the colony as a whole and each of the individual bacteria are continuously

  self-organized open systems: The colonial self-organization is coupled to the internal selforganization

  process each of the individual bacteria. Three intermingled elements are

  involved in the internal process: 1. genetic components, including the chromosomal genetic

  sequences and additional free genetic elements like transposons and plasmids. 2. the


  membrane, including the integrated proteins and attached networks of proteins, etc. 3. The

  intracellular gel, including the machinery required to change its composition, to reorganize

  the genetic components, to reorganize the membrane, to exchange matter, energy and

  information with the surrounding, etc. In addition, we specifically follow the assumption that

  usable information can be stored in its internal state of spatio-temporal structures and

  functional correlations. The internal state can be self-altered, for example via alterations of

  the part of the genetic sequences which store information about transcription control. Hence,

  the combination of the genome and the intra-cellular gel is a system with self reference.

  Hence, the following features of genome cybernetics [9,50] can be sustained.

  1. storage of past external information and its contextual internal interpretation.

  2. storage of past information about the system’s past selected and possible states.

  3. hybrid digital-analog processing of information.

  4. hybrid hardware-software processing of information.

  The idea is that the hardware can be self-altered according to the needs and outcome of the

  information processing, and part of the software is stored in the structure of the hardware

  itself, which can be self-altered, so the software can have self reference and change itself.

  Such mechanisms may take a variety of different forms. The simplest possibility is by

  ordinary genome regulation – the state of gene expression and communication-based

  collective gene expression of many organisms. For eukaryotes, the mitochondria acting like a

  bacterial colony can allow such collective gene expression of their own independent genes.

  In this regard, it is interesting to note that about 2/3 of the mitochondria’s genetic material is

  not coding for proteins.

  Genome cybernetics has been proposed to explain the reconstruction of the coding DNA

  nucleus in ciliates [133,134]. The specific strains studied have two nuclei, one that contains

  only DNA coded for proteins and one only non-coding DNA. Upon replication, the coding

  nucleus disintegrates and the non-coding is replicated. After replication, the non-coding

  nucleus builds a new coding nucleus. It has been shown that it is done using the transposable

  elements in a computational process.

  More recent work shows that transposable elements can effectively re-program the genome

  between replications [135]. In yeast, these elements can insert themselves into messenger


  RNA and give rise to new proteins without eliminating old ones[136]. These findings

  illustrate that rather than wait for mutations to occur randomly, cells can apparently keep

  some genetic variation on tap and move them to ‘hard disk’ storage in the coding part of the

  DNA if they turn out to be beneficial over several life cycles. Some observations hint that the

  collective intelligence of the intracellular mitochondrial colonies play a crucial role in these

  processes of self-improvement [128-132].

  Here, we further assume the existence of the following features:

  5. storage of the information and the knowledge explicitly in its internal spatiotemporal

  structural organizations.

  6. storage of the information and the knowledge implicitly in functional organizations

  (composons) in its corresponding high dimensional space of affinities.

  7. continuous generation of models of itself by reflection forward (in the space of

  affinities) its stored knowledge.

  The idea of high dimensional space of affinities (renormalized correlations) has been

  developed by Baruchi and Ben Jacob [137], for analyzing multi-channel recorded activity

  (from gene expression to human cortex). They have shown the coexistence of functional

  composons (functional sub-networks) in the space of affinities for recorded brain activity.

  With this picture in mind, the system’s models of itself are not necessarily

  dedicated ‘units’ of the system in the real space but in the space of affinities, so the models

  should be understood as a caricature of the system in real space including themselves -

  caricature in the sense that maximal meaningful information is represented. In addition, the

  system’s hierarchical organization enables the smaller scales to contain information about the

  larger scale they themselves form – metaphorically, like the formation of meanings of words

  in sentences as we explain in Appendix B. The larger scale, the analog of the sentence and

  the reader’s previous knowledge, selects between the possible lower scale organizations. The

  system’s real time is represented in the models by a faster internal time, so at every moment

  in real time the system has information about possible caricatures of itself at later times.


  The reason that internal multiple composons (that serve as models) can coexist has to do

  with the fact that going backward in time is undecidable for external observer (e.g., solving

  backward reaction-diffusion equations is undetermined). So what we suggest is that, by

  projecting the internally stored information about the past (which can not be reconstruct by

  external observer), living organisms utilize the fact that going backward in time is

  undetermined for regulated freedom of response: to have a range of possible courses of future

  behavior from which they have the freedom to select intentionally according to their past

  experience, present circumstances, and inherent predictions of the future. In contrast, the

  fundamental assumption in the studies of complex adaptive systems according to Gell-Mann

  [115], is that the behavior of organisms is determined by accumulations of accidents.

  Any entity in the world around us, such as an individual human being, owes its

  existence not only to the simple fundamental law of physics and the boundary condition

  on the early universe but also to the outcomes of an inconceivably long sequence of

  probabilistic events, each of which could have turned out differently. Now a great many

  of those accidents, for instance most cases of the bouncing of a particular molecule in a

  gas to the right rather than the left in a molecular collision, have few ramifications for

  the future coarse-grained histories. Sometimes, however, an accident can have

  widespread consequences for the future, although those are typically restricted to

  particular regions of space and time. Such a "frozen accident" produces a great deal of

  mutual algorithmic information among various parts or aspects of a future coarsegrained

  history of the universe, for many such histories and for various ways of

  dividing them up.

  We propose that organisms use stored relevant information to generate an internal

  mixed yet decomposable (separable) state of multiple options analogous to quantum

  mechanical superposition of states .In this sense the process of decision-making to select a

  specific response to external stimuli is conceptually like the projection of the wave function

  in quantum mechanical measurement. There are two fundamental differences, though: 1. In

  quantum measurement, the external observer directly causes the collapse of the system on a

  specific eigenstate he pre-selects. Namely, the eigenstate is predetermined while its

  corresponding eigenvalue is not. In the organism’s decision-making, the external stimuli

  initiate the selection of a specific state (collapse on a specific response). The selected state is

  in principle unknown directly to an external observer. The initiated internal decomposition of

  the mixed states and the selection of a specific one are performed according to stored past

  information. 2. In quantum measurement, the previous possible (expected) eigenvalues of the

  other eigenstates are erased and assigned new uncertainties. In the organism’s decision


  making the process is qualitatively different: the external stimuli initiate decomposition of

  the mixed states by the organism itself. The information about the other available options is

  stored after the selection of the specific response. Therefore, the unselected past options are

  expected to affect consequent decision-making.

  Decomposable Mixed State of Multiple-Options –

  A Metaphor or Testable Reality?

  The above picture is rejected on the grounds that in principle the existence of a mixed and

  decomposable state of multiple options can not be tested experimentally. In this sense, the

  objection is similar in spirit to the objections to the existence of the choice function in

  mathematics (Appendix D), and the wave function in physics (Appendix E).

  The current experimental methodology in life science (disintegrating the organism

  or exposing it to lethal stress), is conceptually similar to the notion of ”strong measurements”

  or “destructive measurements” in quantum mechanics in which the wave function is forced to

  collapse. Therefore, the existence of an internal state decomposable only by the organism

  itself can not be tested by that approach. A new conceptual methodology is required, of

  protective biotic measurements. For example, biofluoremetry can be used to measure the

  intracellular spatio-temporal organization and functional correlations in a living organism

  exposed to weak stress. Conceptually, fluoremetry is similar to quantum non-demolition and

  weak stress is similar to the notion of weak quantum measurements. Both allow the

  measurement of the quantum state of a system without forcing the wave function to collapse.

  Bacterial collective learning when exposed to non-lethal levels of antibiotics provide an

  example of protective biotic measurements (Appendix E).


  Fig 2. Confocal image of mitochondria within a single cultured rat cortical astrocyte

  stained with the calcium-sensitive dye rhod-2 which partitions into mitochondria, permitting

  direct measurements of intramitochondrial calciuum concentration (curtsey of Michael


  It should be kept in mind that the conceptual analogy with quantum mechanics is subtle and

  can be deceiving rather than inspiring if not properly used. For clarification, let us consider

  the two-slit experiment for electrons. When the external observer measures through which of

  the slits the electron passes, the interference pattern is washed out - the measurement causes

  the wave function of the incoming electron to collapse on one of the two otherwise available


  Imagine now an equivalent two-slit experiment for organisms. In this thought

  experiment, the organisms arrive at a wall with two closely located narrow open gates.

  Behind the wall there are many bowls of food placed along an arc so that they are all at equal

  distance from the gates. The organisms first choose through which of the two gates to pass

  and then select one bowl of food. The experiment is performed with many organisms, and the

  combined decisions are presented in a histogram of the selected bowls. In the control

  experiment, two independent histograms are measured, for each door separately (no decisionmaking

  is required). The distribution when the two gates are open is compared with the sum

  of the distributions for the single gates. A statistically significant difference will indicate that

  past unselected options can influence consequent decision-making even if the following

  decision involves a different choice altogether (gates vs. food bowls).


  Upon completion of this monograph, the development of a Robot-Scientist has just been

  reported [138]. The machine was given the problem of discovering the function of different

  genes in yeast, to demonstrate its ability to generate a set of hypotheses from what is known

  about biochemistry and then design experiments and interpret the results (assign meaning)

  without human help. Does this development provide the ultimate proof that there is no

  distinction between Artificial Intelligence and Natural Intelligence? Obviously, advanced

  automated technology interfaced with learning software can have important contribution. It

  may replace human researchers from doing what machines can do, thus freeing them to be

  more creative and to devote more effort to their beyond-machinery thinking. We don’t

  expect, however, that a robot scientist will be able to design experiments to test, for example,

  self-identity and decision-making, for the simple reason that it could not grasp these


  Epilogue – From Bacteria Shalt Thou Learn

  Mutations as the causal driving force for the emergence of the diversity and complexity of

  organisms and biosystems became the most fundamental principle in life sciences ever since

  Darwin gave mutations a key role in natural selection.

  Consequently, research in life sciences has been guided by the assumption that the

  complexity of life can become comprehensible if we accumulate sufficient amounts of

  detailed information. The information is to be deciphered with the aid of advanced

  mathematical method within the Neo-Darwinian schemata. To quote Gell-Mann,

  Life can perfectly well emerge from the laws of physics plus accidents, and mind, from

  neurobiology. It is not necessary to assume additional mechanisms or hidden causes.

  Once emergence is considered, a huge burden is lifted from the inquiring mind. We don't

  need something more in order to get something more.

  This quote represents the currently, dominant view of life as a unique physical phenomenon

  that began as a colossal accident, and continues to evolve via sequences of accidents selected

  by random number generators – the omnipotent idols of science. We reason that, according to


  this top-level emergence picture, organisms could not have evolved to have meaning-based,

  natural intelligence beyond that of machinery.

  Interestingly, Darwin himself didn’t consider mutations to be necessarily random, and

  thought the environment can trigger adaptive changes in organisms – a notion associated

  with Lamarckism. Darwin did comment, however, that it is reasonable to treat alterations as

  random, so long as we do not know their origin. He says:

  “I have hitherto sometimes spoken as if the variations were due to chance. This, of

  course, is a wholly incorrect expression, but it serves to acknowledge plainly our

  ignorance of the cause of each particular variation… lead to the conclusion that

  variability is generally related to the conditions of life to which each species has been

  exposed during several successive generations”.

  In 1943, Luria and Delbruck performed a cornerstone experiment to prove that random

  mutation exist by exposing bacteria to lethal conditions – bacteriophage that immediately

  kills non-resistant bacteria. Therefore, only cells with pre-existing specific mutations could

  survive. The other cells with didn’t have the chance to alter their self - a possibility that could

  not be ruled out by the experiments. Nevertheless, these experiments were taken as a crucial

  support for the Neo-Darwinian dogma which states that all mutations are random, and can

  occur only during DNA replication. To bridge between these experiments, Turing’s imitation

  game and the notion of weak measurements in quantum mechanics, we suggest to test natural

  intelligence by first giving the organisms a chance to learn from hard but non-lethal

  conditions. We also proposed to let the bacteria play identity game proper for testing their

  natural intelligence, similar in spirit to the real life games played between different colonies

  and even with other organisms [139].

  In Turing’s footsteps, we propose to play his imitation game with the reverse goal in

  mind. Namely, human players participate in the game to learn about themselves. By playing

  this reverse game with bacteria, - Nature’s fundamental organisms from which all life

  emerged - we should be able to learn about the very essence of our self. This is especially so

  when keeping in mind that the life, death and well being of each of our cells depend on the

  cooperation of its own intelligent bacterial colony – the mitochondria. Specifically, we

  believe that understanding bacterial natural intelligence as manifested in mitochondria might

  be crucial for understanding the meaning-based natural intelligence of the immune system


  and the central nervous system, the two intelligent systems we use for interacting with other

  organisms in the game of life. Indeed, it has recently been demonstrated that mice with

  identical nuclear genomes can have very different cognitive functioning if they do not have

  the same mitochondria in their cytoplasm. The mitochondria are not transferred with the

  nucleus during cloning procedures [140].

  To quote Schrödinger,

  Democritus introduces the intellect having an argument with the senses about what is

  'real'. The intellect says; 'Ostensibly there is color, ostensibly sweetness, ostensibly

  bitterness, actually only atoms and the void.' To which the senses retort; 'Poor intellect,

  do you hope to defeat us while from us you borrow your evidence? Your victory is your



  We thank Ben Jacob’s student, Itay Baruchi, for many conversations about the potential

  implications of the space of affinities, the concept he and Eshel have recently developed

  together. Some of the ideas about bacterial self-organization and collective intelligence were

  developed in collaboration with Herbert Levine. We benefited from enlightening

  conversations, insights and comments by Michal Ben-Jacob, Howard Bloom, Joel Isaacson,

  Yuval Neeman and Alfred Tauber. The conceptual ideas could be converted into concrete

  observations thanks to the devoted and precise work of Inna Brainis. This work was

  supported in part by the Maguy-Glass Chair in Physics of Complex Systems.

  Personal Thanks by Eshel Ben-Jacob

  About twenty-five years ago, when I was a physics graduate student, I read the book “The

  Myth of Tantalus” and discovered there a new world of ideas. I went to seek the author, and

  found a special person with vast knowledge and human approach. Our dialogue led to the

  establishment of a unique, multidisciplinary seminar, where themes like “the origin of

  creativity” and “mind and matter” were discussed from different perspectives. Some of the

  questions have remained with me ever since, and are discussed in this monograph.


  Over the years I have had illuminating dialogues with my teacher Yakir Aharonov about the

  foundations of quantum mechanics and with my friend Adam Tenenbaum about logic and


  In my Post-Doctoral years, I was very fortunate to meet the late Uri Merry, who introduced

  me to the world of social science and linguistics and to Buber’s philosophy. Among other

  things, we discussed the role of semantic and pragmatic communication in the emergence of

  individual and group self.


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  Appendix A: Bacterial Cooperation – The Origin of Natural Intelligence

  Under natural conditions, bacteria tend to cooperatively self-organize into hierarchically

  structured colonies (109-1013 bacteria each), acting much like multi-cellular organisms

  capable of coordinated gene expressions, regulated cell differentiation, division of tasks, and

  more. Moreover, the colony behaves as a new organism with its own new self, although the

  building blocks are living organisms, each with its own self, as illustrated in the figure below.

  To achieve the proper balance of individuality and cooperation, bacteria communicate using

  sophisticated communication methods which include a broad repertoire of biochemical

  agents, such as simple molecules, polymers, peptides, proteins, pheromones, genetic

  materials, and even “cassettes” of genetic information like plasmids and viruses. At the same

  time, each bacterium has equally intricate intracellular communication means (signal

  transduction networks and genomic plasticity) of generating intrinsic meaning for contextual

  interpretation of the chemical messages and for formulating its appropriate response.

  Collective decision-making: When the growth conditions become too stressful, bacteria can

  transform themselves into inert, enduring spores. Sporulation is executed collectively and

  begins only after "consultation" and assessment of the colonial stress as a whole by the

  individual bacteria. Simply put, starved cells emit chemical messages to convey their stress.

  Each of the other bacteria uses the information for contextual interpretation of the state of the

  colony relative to its own situation. Accordingly, each of the cells decides to send a message

  for or against sporulation. After all the members of the colony have sent out their decisions

  and read all the other messages, if the “majority vote” is pro-sporulation, sporulation occurs.

  Thus, sporulation illustrates semantic and pragmatic levels in bacterial communication, i.e.,

  bacteria can transmit meaning-bearing messages to other bacteria to conduct a dialogue for

  collective decision making (Appendix B).

  Although spores can endure extreme conditions (e.g., high temperatures, toxic

  materials, etc.), all they need for germination is to be placed under mild growth conditions.

  How can they sense the environment so accurately while in almost non living state,

  surrounded by a very solid membrane, is an unsolved and very little studied enigma.

  Exchange of genetic information: Another example of bacterial special abilities has to do

  with the rapid development of bacterial resistance to antibiotic: The emergence of bacterial

  strains with multiple drug resistance has become one of the major health problems

  worldwide. Efficient resistance rapidly evolves through the cooperative response of bacteria,

  utilizing their sophisticated communication capabilities. Bacteria exchange resistance

  information within the colony and between colonies, thus establishing a “creative genomic

  web”. Maintenance and exchange of the resistance genetic information is costly and might be

  hazardous to the bacteria. Therefore, the information is given and taken on a “need to know”

  basis. In other words, the bacteria prepare, send and accept the genetic message when the

  information is relevant to their existence.


  One of the tools for genetic communication is via direct physical transfer of conjugal

  plasmids. These bacterial mating events, that can also include inter-colonial and even

  interspecies conjugations, follow chemical courtship played by the potential partners.

  Naively presented, bacteria with valuable information (say, resistance to antibiotic) emit

  chemical signals to announce this fact. Bacteria in need of that information, upon receiving

  the signal, emit pheromone-like peptides to declare their willingness to mate. Sometimes, the

  decision to mate is followed by an exchange of competence factors (peptides). This preconjugation

  communication modifies the membrane of the partner cell into a penetrable state

  needed for conjugation, allowing the exchange of genetic information.

  Hierarchical organization of vortices: Some bacteria cope with hazards by generating

  module structures - vortices, which then become building blocks used to construct the colony

  as a higher entity (Fig 2). To maintain the integrity of the module while it serves as a higherorder

  building block of the colony requires an advanced level of communication. Messages

  must be passed to inform each cell in the vortex that it is now playing a more complex role,

  being a member of the specific module and the colony as a whole, so it can adjust its

  behavior accordingly.

  Once the vortex is recognized as a possible spatial structure, it becomes easy to

  understand that vortices can be used as subunits in a more complex colonial structure for

  elevated colonial plasticity. In Fig 3, we demonstrate how the P. vortex bacteria utilize their

  cooperative, complexity-based plasticity to alter the colony structure to cope with antibiotic

  stress, making use of some simple yet elegant solutions. The bacteria simply increase

  cooperation (by intensifying both attractive and repulsive chemical signaling), leading to

  larger vortices (due to stronger attraction) that move faster away from the antibiotic stress

  (due to stronger repulsion by those left behind). Moreover, once they’ve encountered the

  antibiotic, the bacteria seem to generate a collective memory so that in the next encounter

  they can respond even more efficiently.

  Fig. A1: Hierarchical colonial organization: Patterns formed during colonial development of the

  swarming and lubricating Paenibacillus vortex bacteria. (Left) The vortices (modules) are the leading dots seen

  on a macro-scale (~10cm2). The picture shows part of a circular colony composed of about 1012 bacteria - ~ the

  number of cells of our immune system, ten times the number of neurons in the brain and hundred times the

  human population on earth. Each vortex is composed of many cells that swarm collectively around their


  common center. These vortices vary in size from tens to millions of bacteria, according to their location in the

  colony and the growth conditions. The vortex shown on the right (magnification x500, hence each bar is a

  single bacterium) is a relatively newly formed one. After formation, the cells in the vortex replicate, the vortex

  expands in size and moves outward as a unit, leaving behind a trail of motile but usually non replicating cells –

  the vortex tail. The vortices dynamics is quite complicated and includes attraction, repulsion, merging and

  splitting of vortices. Yet, from this complex, seemingly chaotic movement, a colony with complex but nonarbitrary

  organization develops (left). To maintain the integrity of the vortex while it serves as a higher-order

  building block of the colony requires an advanced level of communication. Messages must be passed to inform

  each cell in the vortex that it is now playing a more complex role, being a member of the specific vortex and the

  colony as a whole, so it can adjust its behavior accordingly. New vortices emerge in the trail behind a vortex

  following initiation signals from the parent vortex. The entire process proceeds as a continuous dialogue: a

  vortex grows and moves, producing a trail of bacteria and being pushed forward by the very same bacteria

  behind. At some point the process stalls, and this is the signal for the generation of a new vortex behind the

  original one, that leaves home (the trail) as a new entity which serves a living building block of the colony as a


  Fig. A2: Collective memory and learning: Self-organization of the P.vortex bacteria in the

  presence of non-lethal levels of antibiotic added to the substrate. In the picture shown, bacteria were exposed to

  antibiotic before the colonial developments. Note that it resulted in a more organized pattern (in comparison

  with Fig 1.

  From multi-cellularity to sociality: In fact, bacteria can go a step higher; once an entire

  colony becomes a new multi-cellular being with its own identity, colonies functioning as

  organisms cooperate as building blocks of even more complex organizations of bacterial

  communities or societies, such as species-rich biofilms. In this situation, cells should be able

  to identify their own self, both within the context of being part of a specific colony-self and

  part of a higher entity - a multi-colonial community to which their colony belongs. Hence, to

  maintain social cooperation in such societies with species diversity, the bacteria need “multilingual”

  skills for the identification and contextual interpretation of messages received from

  colony members and from other colonies of the same species and of other species, and to

  have the necessary means to sustain the highest level of dialogue within the “chattering” of

  the surrounding crowed.

  Incomprehensible complexity: For perspective, the oral cavity, for example, hosts a

  large assortment of unicellular prokaryotic and various eukaryotic microorganisms. Current

  estimates suggest that sub-gingival plaque contains 20 genera of bacteria representing


  hundreds of different species, each with its own colony of ~1010 bacteria, i.e., together

  ~thousand times the human population on earth. Thus, the level of complexity of such

  microbial system far exceeds that of the computer networks, electric networks, transportation

  and all other man-made networks combined. Yet bacteria of all those colonies communicate

  for tropism in shared tasks, coordinated activities and exchange of relevant genetic bacterial

  information using biochemical communication of meaning-bearing, semantic messages. The

  current usage of “language” with respect to intra- and inter-bacteria communication is mainly

  in the sense that one would use in, for example, “computer language” or “language of

  algebra”. Namely, it refers to structural aspects of communication, corresponding to the

  structural (lexical and syntactic) linguistic motifs. Higher linguistic levels - assigning

  contextual meaning to words and sentences (semantic) and conducting meaningful dialogue

  (pragmatic) - are typically associated with cognitive abilities and intelligence of human.

  Hence, currently one might accept their existence in the “language of dolphins” but regard

  them as well beyond the realm of bacterial communication abilities. We propose that this

  notion should be reconsidered.

  Appendix B: Clues and Percepts Drawn from Human Linguistics

  Two independent discoveries the 1950’s latter bridged linguistics and genetics: Chomsky’s

  proposed universal grammar of human languages [141] and the discovery of the structural

  code of the DNA. The first suggested universal structural motifs and combinatorial principles

  (syntactic rules) at the core of all natural languages, and the other provided analogous

  universals for the genetic code of all living organisms. A generation later, these paradigms

  continue to cross-pollinate these two fields. For example, Neo-Darwinian and population

  genetics perspectives as well as phylogenetic methods are now used for the understanding the

  structure, learning, and evolution of human languages. Similarly, Chomsky’s meaningindependent

  syntactic grammar view combined with computational linguistic methods are

  widely used in biology, especially in bioinformatics and structural biology but increasingly in

  biosystemics and even ecology.

  The focus has been on the formal, syntactic structural levels, which are also applicable to

  “machine languages”: Lexical – formation of words from their components (e.g., characters

  and phonemes); Syntactic – organization of phrases and sentences in accordance with wellspecified

  grammatical rules [142,143].

  Linguistics also deals with a higher-level framework, the semantics of human language.

  Semantics is connected to contextual interpretation, to the assignment of context-dependent

  meaning to words, sentences and paragraphs. For example, one is often able to capture the

  meaning of a text only after reading it several times. At each such iteration, words, sentences

  and paragraphs may assume different meanings in the reader's mind; iteration is necessary,

  since there is a hierarchical organization of contextual meaning. Namely, each word

  contributes to the generation of the meaning of the entire sentence it is part of, and at the

  same time the generated whole meaning of the sentence can change the meaning of each of

  the words it is composed of. By the same token, the meanings of all sentences in a paragraph

  are co-generated along with the created meaning of the paragraph as a whole, and so on, for

  all levels.


  Readers have semantic plasticity, i.e., a reader is free to assign individualistic contextual and

  causal meanings to the same text, according to background knowledge, expectations, or

  purpose; this is accomplished using combined analytical and synthetic skills. Beyond this,

  some linguists identify the conduction of a dialogue among converser using shared semantic

  meaning as pragmatics. The group usage of a dialogue can vary from activity coordination

  through collective decision-making to the emergence of a new group self. To sustain such

  cognitive abilities might require analogous iterative processes of self-organization based

  generation of composons of meaning within the brain which will be discussed elsewhere

  Drawing upon human linguistics with regard to bacteria, semantics would imply

  contextual interpretation of chemical messages, i.e., each bacterium has some freedom

  (plasticity) to assign meaning according to its own specific, internal and external, contextual

  state. For that, a chemical message is required to initiate an intra-cellular response that

  involves internal restructuring - self-organization of the intracellular gel and/or the genenetwork

  or even the genome itself. To sustain a dialogue based on semantic messages, the

  bacteria should have a common pre-existing knowledge (collective memory) and abilities to

  collectively generate new knowledge that is transferable upon replication. Thus, the ability to

  conduct a dialogue implies that there exist some mechanisms of collective gene expression,

  analogous to that of cell differentiation during embryonic development of multi-cellular

  organisms, in which mitochondria might play an important role.

  Appendix C: Gödel’s Code and the Axiom of Choice

  Hilbert’s second problem

  Gödel’s theorems provided an answer to the second of the 23 problems posed by Hilbert.

  2. Can it be proven that the axioms of logic are consistent?

  Gödel’s theorems say that the answer to Hilbert’s second question is negative. For that he has

  invented the following three steps code:

  1. Gödel assigned a number to each logical symbol, e.g.,

  Not ≡ 1

  Or ≡ 2

  If then ≡ 3

  ∃ ≡ 4

  2. He assigned prime numbers to variables, e.g.,

  x ≡ 11

  y ≡ 13

  3. He assigned a number to any statement according to the following example: “There is a

  number not equal to zero”.

  In logic symbols ( ∃ x ) ( x ∼ = 0 )

  In Gödel’s numbers 8 4 11 9 8 11 1 5 6 9

  The statement’s number is 28.34.511.79.118.1311.


  Note that it is a product of the sequence of prime numbers, each to the power of the

  corresponding Gödel’s number. This coding enables one-to-one mapping between statements

  and the whole numbers.

  Hilbert’s first problem and the Axiom of Choice

  Gödel also studied the first of the 23 essential problems posed by Hilbert.

  1.a Is there a transfinite number between that of a denumerable set and the numbers of

  the continuum? 1.b Can the continuum of numbers be considered a well ordered set?

  In 1940, Gödel proved that a positive answer to 1.a is consistent with the axioms of von

  Neumann-Bernays-Gödel set theory. However, in 1963, Cohen demonstrated that it is

  inconsistent with the Zermelo-Frankel set theory. Thus, the answer is undecidable – it

  depends on the particular set theory assumed. The second question is related to an important

  and fundamental axiom in set sometimes called Zermelo's Axiom of Choice. It was

  formulated by Zermelo in 1904 and states that, given any set of mutually exclusive nonempty

  sets, there exists at least one set that contains exactly one element in common with each of

  the nonempty sets. The axiom of choice can be demonstrated to be independent of all other

  axioms in set theory. So the answer to 1.b is also undecidable.

  The popular version of the Axiom of Choice is that [144]:

  Let C be a collection of nonempty sets. Then we can choose a member from each set in

  that collection. In other words, there exists a choice function f defined on C with the

  property that, for each set S in the collection, f(S) is a member of S.

  There is an ongoing controversy over how to interpret the words "choose" and

  "exists" in the axiom: If we follow the constructivists, and "exists" means “to find," then the

  axiom is false, since we cannot find a choice function for the nonempty subsets of the real

  numbers. However, most mathematicians give "exists" a much weaker meaning, and they

  consider the Axiom to be true: To define f(S), just arbitrarily "pick any member" of S.

  In effect, when we accept the Axiom of Choice, this means we are agreeing to the convention

  that we shall permit ourselves to use a choice function f in proofs, as though it "exists" in

  some sense, even though we cannot give an explicit example of it or an explicit algorithm for


  The choice function merely exists in the mental universe of mathematics. Many different

  mathematical universes are possible. When we accept or reject the Axiom of Choice, we are

  specifying which universe we shall work in. As was shown by Gödel and Cohen, both

  possibilities are feasible – i.e., neither accepting nor rejecting AC yields a contradiction.

  The Axiom of Choice implies the existence of some conclusions which seem to be counterintuitive

  or to contradict "ordinary" experience. One example is the Banach-Tarski

  Decomposition, in which the Axiom of Choice is assumed to prove that it is possible to take

  the 3-dimensional closed unit ball,


  B = {(x,y,z) ∈ R3 : x2 + y2 + z2 < 1}

  and partition it into finitely many pieces, and move those pieces in rigid motions (i.e.,

  rotations and translations, with pieces permitted to move through one another) and

  reassemble them to form two copies of B.

  At first glance, the Banach-Tarski Decomposition seems to contradict some of our intuition

  about physics – e.g., the Law of Mass Conservation from classical Newtonian physics.

  Consequently, the Decomposition is often called the Banach-Tarski Paradox. But actually, it

  only yields a complication, not a contradiction. If we assume a uniform density, only a set

  with a defined volume can have a defined mass. The notion of "volume" can be defined for

  many subsets of R3, and beginners might expect the notion to apply to all subsets of R3, but it

  does not. More precisely, Lebesgue measure is defined on some subsets of R3, but it cannot

  be extended to all subsets of R3 in a fashion that preserves two of its most important

  properties: the measure of the union of two disjoint sets is the sum of their measures, and

  measure is unchanged under translation and rotation. Thus, the Banach-Tarski Paradox does

  not violate the Law of Conservation of Mass; it merely tells us that the notion of "volume" is

  more complicated than we might have expected.

  We emphasize that the sets in the Banach-Tarski Decomposition cannot be described

  explicitly; we are merely able to prove their existence, like that of a choice function. One or

  more of the sets in the decomposition must be Lebesgue unmeasurable; thus a corollary of

  the Banach-Tarski Theorem is the fact that there exist sets that are not Lebesgue measurable.

  The idea we lean toward is that in the space of affinities the composons represent similar

  decomposition but of information which is the extensive functional in this space which

  corresponds to the volume in the system real space.

  Appendix D: Description of Turing’s Conceptual Machinery

  To support our view of the limits of Artificial Intelligence or Machines Intelligence, we

  present here a relatively detailed description of Turing’s Universal Machine. Turing proved that any

  discrete, finite state with fixed in time finite set of instructions can be mapped onto his conceptual

  machine. Note that there can be self-reference in the execution of the instructions but not in their

  logical structure.

  The process of computation was graphically depicted in Turing's paper when he asked the reader to

  consider a device that can read and write simple symbols on a paper tape that is divided into

  squares. The "reading/writing head" can move in either direction along the tape, one square at a

  time, and a control unit that directs the actions of the head can interpret simple instructions about

  reading and writing symbols in squares. The single square that is "scanned" or "read" at each stage

  is known as the Active Square. Imagine that new sections can be added at either end of the existing

  tape, so it is potentially infinite.

  Suppose the symbols are "X" and "O". Suppose that the device can erase either symbol when it

  reads it in the Active Square and replace it with the other symbol (i.e., erase an X and replace it with

  an O, and vice versa). The device also has the ability to move left or right, one square at a time,

  according to instructions interpreted by the control unit. The instructions cause a symbol to be

  erased, written, or left the same, depending on which symbol is read.


  Any number of games can be constructed using these rules, but they would not all necessarily be

  meaningful. One of the first things Turing demonstrated was that some of the games constructed

  under these rules can be very sophisticated, considering how crude and automaton-like the primitive

  operations seem to be. The following example illustrates how this game can be used to perform a

  simple calculation.

  The rules of the game to be played by this Turing machine are simple: Given a starting position in

  the form of a section of tape with some Xs and Os on it, and a starting square indicated, the device

  is to perform the actions dictated by a list of instructions and follows the succeeding instructions

  one at a time until it reaches an instruction that forces it to stop. (If there is no explicit instruction in

  the table of instructions for a particular tape configuration, there is nothing that the machine can do

  when it reaches that configuration, so it has to stop.)

  Each instruction specifies a particular action to be performed if there is a certain symbol on the

  active square at the time it is read. There are four different actions; they are the only legal moves of

  this game. They are:

  Replace O with X.

  Replace X with O.

  Go one square to the right.

  Go one square to the left.

  An example of an instruction is: "If there is an X on the active square replace it with O." This

  instruction causes the machine to perform the second action listed above. In order to create a

  "game," we need to make a list that specifies the number of the instruction that is being followed at

  every step as well as the number of the instruction that is to be followed next. That is like saying

  "The machine is now following (for example) instruction seven, and the instruction to be followed

  next is (for example) instruction eight" (as is illustrated in appendix 3).

  Here is a series of instructions, given in coded form and the more English-like translation.

  Taken together, these instructions constitute an "instruction table" or a "program" that tells a

  Turing machine how to play a certain kind off game:

  1XO2 (Instruction #1:if an X is on the active square, replace it with O, then execute instruction #2.)

  2OR3 (Instruction #2: if an O is on the active square, go right one square and then execute instruction #3.)

  3XR3 (Instruction #3: if an X is on the active square, go right one square execute instruction #3;

  3OR4 but if an O is on the active square, go right one square and then execute instruction #4.)

  4XR4 (Instruction #4: if an X is on the active square, go right one square and then execute instruction #4;

  4OX5 but if an O is on the active square, replace it with X and then execute instruction #5.)

  5XR5 (Instruction #5: if an X is on the active square, go right one square and then execute instruction #5;

  5OX6 but if an O is on the active square, replace it with X and then execute instruction #6.)

  6XL6 (Instruction #6: if an X is on the active square, go left one square and then execute instruction #6

  6OL7 but if an O is on the active square, go left one square and then execute instruction #7.)

  7XL8 (Instruction #7: if an X is on the active square, go left one square and then execute instruction #8.)

  8XL8 (Instruction #8: if an X is on the active square, go left one square and then execute instruction #8;

  8OR1 but if an O is on the active square, go right one square and then execute instruction #1.)

  Note that if there is an O on the active square in instruction #1 or #7, or if there is an X on the active square in

  instruction #2, the machine will stop.

  In order to play the game (run the program) specified by the list of instructions, one more

  thing must be provided: a starting tape configuration. For our example, let us consider a tape

  with two Xs on it, bounded on both sides by an infinite string of Os. The changing states of a

  single tape are depicted here as a series of tape segments, one above the other. The Active


  Square for each is denoted by a capital X or O. When the machine is started it will try to

  execute the first available instruction, instruction #1. The following series of actions will then


  Instruction Tape What the Machine Does

  #1 ...ooXxooooooo... One (of two) Xs is erased.

  #2 ...ooOxooooooo...

  #3 ...oooXooooooo... Tape is scanned to the right

  #3 ...oooxOoooooo...

  #4 ...oooxoOooooo...

  #5 ...oooxoXooooo... Two Xs are written.

  #5 ...oooxoxOoooo...

  #6 ...oooxoxXoooo...

  #6 ...oooxoXxoooo... Scanner returns to the other original X

  #6 ...oooxOxxoooo...

  #7 ...oooXoxxoooo...

  #8 ...ooOxoxxoooo... Scanner moves to the right and execute #1

  #1 ...oooXoxxoooo...

  #2 ...oooOoxxoooo...

  #3 ...ooooOxxoooo... Scanner moves to the right of the two Xs that were written earlier.

  #4 ...oooooXxoooo...

  #4 ...oooooxXoooo...

  #4 ...oooooxxOooo...

  #5 ...oooooxxXooo... Two more Xs are written.

  #5 ...oooooxxxOoo...

  #6 ...oooooxxxXoo...

  #6 ...oooooxxXxoo... Scanner looks for any more original Xs

  #6 ...oooooxXxxoo...

  #6 ...oooooXxxxoo...

  #6 ...ooooOxxxxoo...

  #7 ...oooOoxxxxoo... The machine stops because there is no instruction for #7 if O is being scanned.

  This game may seem rather mechanical. The fact that it is mechanical was one of the points

  Turing was trying to make. If you look at the starting position, note that there are two

  adjacent Xs. Then look at the final position and note that there are four Xs. If you were to use

  the same instructions, but start with a tape that had five Xs, you would wind up with ten Xs.

  This list of instructions is the specification for a calculating procedure that can double the

  input and display the output. It can, in fact, be done by a machine.

  (This Appendix is edited with author’s permission from “Tools for Thoughts: The People and

  Ideas of the Next Computer Revolution” by Howard Rheingold 1985)

  Appendix E: Non-Destructive Quantum Measurements

  Protective Quantum Measurements and Hardy’s Paradox

  The debate about the existence of the choice function in the Axiom of choices is in the same

  spirit as the debated questions about the reality of the wave function and paradoxes

  connected with quantum entanglement like the one proposed by Hardy (see references in the

  extract below). It has been proven by Aharonov and his collaborators[145-148 ]that it is

  possible in principle to perform quantum measurements to extract information beyond


  quantum uncertainty while the wave function is protected (for the case of eigenstate with

  discrete spectrum of eigenvalue they refer to it as protective measurements, and for

  continuous spectrum as weak measurements). The protective, weak and non-demolition

  (described latter) quantum measurements provide different methods for non-destructive

  measurements of quantum systems – there is no destruction of the quantum state of the

  system due to externally imposed measurement. These kinds of measurements enable the

  observations of unexpected quantum phenomena. For example, the thought experiment

  proposed in Hardy’s paradox can be tested as illustrated in [Quantum Physics, abstract quantph/



  As with a multiple-options state for organism, Hardy’s paradox is usually assumed to be resolved on

  the grounds that the thought experiment doesn't correspond to any possible real experiment and is

  therefore meaningless. The only way to find out what really happens to the particles in the

  experiment would be to measure their routes, rather than simply inferring them from the final result.

  But, as soon as a particle detector is placed in any of the paths, standard strong quantum

  measurement will cause the collapse of its wave function and wash out any possible future

  interference between the electron and positron states.

  However, Hardy’s thought experiment can be converted into a real one if the assumed strong

  quantum measurement is replaced with weak measurements. The idea is to exploit quantum

  uncertainty by using a quantum detector which is weakly coupled to the measured system to the

  degree that it reads eigenvalues smaller than the expected quantum uncertainty. It was proved that

  by doing so quantum superposition of states can be preserved (i.e., there is no collapse of the wave

  function). Clearly, a single weak measurement can not, on its own, provide any meaningful

  information. However, it was proved theoretically that, when repeated many times, the average of

  these measurements approximates to the true eigenvalue that would be obtained by a single strong

  measurement involving a collapse of the wave function [145-148]..

  Therefore, when weak measurements are assumed, not only does the original paradox remain, but

  an additional difficulty arises. The theoretical investigations imply that two pairs of electronpositron

  can coexist in the apparatus at the same time: A detector located in the part of the

  interferometer in which the particle trajectories are non-overlapping can yield a final reading of -1,

  i.e., a "negative presence" of a pair of particles! To quote Aharonov:

  The -1 result illustrates that there is a way to carry out experiments on the counter-intuitive

  predictions of quantum theory without destroying all the interesting results. A single quantum

  particle could have measurable effects on physical systems in two places at once, for instance.

  Moreover, when you get a good look inside, quantum theory is even more bizarre than we

  thought. Quantum particles can assume far more complex identities than simply being in two

  places at once: pairs of particles are fundamentally different from single particles and they

  can assume a negative presence. And the fact that weak measurements transform the paradox

  from a mere technicality into an unavoidable truth suggests that they could provide a

  springboard for new understanding of quantum mechanics. There are extraordinary things

  within ordinary quantum mechanics; the negative presence result might be just the tip of the

  iceberg: every paradox in quantum theory may simply be a manifestation of other strange

  behaviors of quantum objects that we have not yet detected - or even thought of.


  The Quantum Time-Translation Machine

  Another unexpected quantum reality about the concept of time [149], can be viewed as being

  metaphorically related to the organism’s internal model of itself, which acts on different time scales

  for educated decision-making. We refer to the Aharonov, Anandan, Popescue and Vaidman

  (AAPV) Quantum Time-Translation Machine [150,151]:


  Quantum Non-Demolition Measurements

  Another approach to measure the eigenvalue of a specific observable without demolition of the

  quantum state of the observed system is referred to as QND measurements used mainly in quantum

  optics [152,153]. The idea can be traced back to the Einstein, Podolsky and Rosen paradox [154],

  presented in their 1935 paper entitled "Can quantum-mechanical description of physical reality be

  considered complete?” They have shown that, according to quantum mechanics, if two systems in a

  combined state (e.g., two half-spin particles in a combined-spin state) are at a large distance from

  each other, a measurement of the state of one system can provide information about that of the other

  one. The conceptual idea of the QND measurements is to first prepare the observed system and a

  quantum detector (e.g., Polarized light) in an entangled state and then to extract information about

  the observed system by using ordinary destructive measurement on the quantum detector. This way,

  the state of the detector is demolished but that of the system of interest is protected. In this sense,

  the newly developed biofluoremetry method for studying the intracellular spatio-temporal

  organization and functional correlations is actually a version of QND measurements and not just an


  Proceeding with the same metaphor, bacterial colonies enable to perform new real

  experiments in analogy with Aharonov’s ‘back from the future’ notion about the backward

  propagation of the wave function. For example, several colonies taken from the same culture

  in a stationary phase, or even better, from spores, can be grown at successive intervals of

  time while exposed to the same constraints. The new concept is to let, for example, bacteria

  taken from the future (the older colonies) to communicate with colonies at the present and

  compare their consequent development with those who were not exposed to their own future.

  Albeit simple, the detailed setup and interpretations of the experiments should be done

  keeping in mind that (as we have shown), even similar colonies grown at the same time

  develop distinguishable self-identities.

  To Be is to Change


  The picture of the decomposable mixed state of multiple options is also metaphorically

  analogous to t’Hooft’s Universe [155,156], composed of underlying Be-able and Changeable

  non-commuting observables at the Planck length scales (10-35meter). His motivation was

  the paradox posed by the in principle contradiction of simulating backward in time a unified

  theory composed of gravity and quantum mechanics based on the current Copenhagen

  interpretation: There is no deeper reality, hidden variables do not exist and the world is

  simply probabilistic. It holds that we are not ignorant about quantum objects; it's just that

  there is nothing further to be known. This is in contradiction with Einstein’s picture later

  named ‘hidden variables’. The EPR paradox mentioned earlier was an attempt to illustrate

  that, unless the existence of unknown and non-measurable variables is assumed, one runs

  into contradiction with our intuitive perception of reality. Simply phrased, according to the

  ‘hidden variable’ picture, quantum uncertainty reflects some underlying deterministic reality

  that in principle can be measured. Following the EPR paradox, Bell proposed a specific

  inequality that, if measured, can distinguish between the Copenhagen and hidden variables

  interpretations of quantum mechanics. The consequent experiments were in agreement with

  the Copenhagen interpretation. In 2002, t’Hooft presented a new approach to the problem

  that most perceived as being resolved. His answer to the Copenhagen interpretation is [155]:


  To solve the paradox, he proposed a third approach based on the idea that, on the Planckian level,

  reality might be essentially different from that on the larger scales of interest. The idea is to define

  equivalence classes of states. Two states are defined as equivalent if and only if they evolve in the

  near future to the same state. We emphasize that this is the analogy (in reverse) to our picture of

  ‘harnessing the past to free the future’ during internal self-organization of organisms.

  Metaphorically, for similar reasons (in reverse) why loss of information leads to the quantum

  uncertainty for an external observer, the storage of past information by the organism affords it an

  internal state of multiple options inaccessible to an external observer. To take into consideration the

  crucial role of information loss, t’Hooft proposes that two kinds of observables exist on the

  Planckian scale. The ones that describe the equivalent classes are the be-able ones:

  With regard to organisms, the corresponding observables are those connected with information

  registered in the structural organization or statistically averaged dynamics (e.g., gene-expression

  measurements from several organisms under the same conditions). According to t’Hooft all other

  operators are the change-able ones that do not commute with the be-able operators. So that,

  In this picture, reality on the very fundamental level is associated with information rather than



  This picture of nature is metaphorically similar to the picture we propose for organisms – a balance

  between intrinsic and extrinsic flow of information. The essential difference is that organisms are

  self-organizing open system that can store information, including about their self.

  Appendix F: Turing’s Child Machine

  In the 1950’s the three interchangeable terms ‘Machine Intelligence’, ‘Artificial Intelligence’

  and ‘Machine learning’ referred to the causal (goal) of learning about humans by building

  machines to exhibit behavior which, if performed by humans, would be assumed to involve

  the use of intelligence. In the next five decades, “Machine Intelligence” and its associated

  terms evolved away from their original causal meanings. These terms are now primarily

  associated with particular methodologies for attempting to achieve the goal of getting

  computers to automatically solve problems. Thus, the term “artificial intelligence” is

  associated today primarily with the efforts to design and utilize computers to solve problems

  using methods that rely on knowledge, logic, and various analytical and mathematical

  methods. Only in some spin-off branches of research, such as genetic programming and

  evolvable hardware, does Turing’s term still communicate the broad goal of getting

  computers to automatically solve problems in a human-like or even broader biological-like


  In his 1948 paper, Turing identified three strategies by which human-competitive machine

  intelligence might be achieved. The first is a logic-driven search which is the causal reason

  (described earlier) that led Turing to develop the idea of his conceptual machine, i.e., to learn

  about the foundations of mathematics and logics. The second reason for generating machine

  intelligence is what he called a “cultural search” in which previously acquired knowledge is

  accumulated, stored in libraries, and used in problem solving a - the approach taken by

  modern knowledge-based expert systems. These first two approaches of Turing’s have been

  pursued over the past 50 years by the vast majority of researchers using the methodologies

  that are today primarily associated with the term “artificial intelligence.”


  Turing also identified a third approach to machine intelligence in his 1948 paper,

  saying: “There is the genetical or evolutionary search by which a combination of genes is

  looked for, the criterion being the survival value.” Note that this remarkable realization

  preceded the discovery of the DNA and modern genetics. So Turing could not have specified

  in 1948 how to conduct the “genetical or evolutionary search” for solutions to problems and

  could not mention concepts like population genetics and recombination. However, he did

  point out in his 1950 paper that:

  We cannot expect to find a good child-machine at the first attempt. One must

  experiment with teaching one such machine and see how well it learns. One can then try

  another and see if it is better or worse. There is an obvious connection between this

  process and evolution, by the identifications

  “Structure of the child machine = Hereditary material”;

  “Changes of the child machine = Mutations”;

  “Natural selection = Judgment of the experimenter”.

  Thus, Turing correctly perceived in 1948 and 1950 that machine intelligence can only be

  achieved by an evolutionary process in which a description of a computer hardware and

  software (the hereditary material) undergoes progressive modification (mutation) under the

  guidance of natural selection (i.e., selective pressure in the form of what is now usually

  called “fitness”). The measurement of fitness in modern-day genetics and evolutionary

  computation is usually performed by automated means, as opposed to a human passing

  judgment on each individual candidate, as suggested by Turing.

  From this perspective, Turing’s vision is actually closer to our view about organisms’

  intelligence, provided that the external “teacher” is replaced by an inner one, and the

  organism has freedom of response to the external information gathered, rather than forced to

  follow specific instructions.

  Howard Bloom
  Author of The Lucifer Principle: A Scientific Expedition Into the Forces of History and Global Brain: The Evolution of Mass Mind From The Big Bang to the 21st Century
  Visiting Scholar-Graduate Psychology Department, New York University; Core Faculty Member, The Graduate Institute
  Founder: International Paleopsychology Project; founding board member: Epic of Evolution Society; founding board member, The Darwin Project; founder: The Big Bang Tango Media Lab; member: New York Academy of Sciences, American Association for the Advancement of Science, American Psychological Society, Academy of Political Science, Human Behavior and Evolution Society, International Society for Human Ethology; advisory board member: Youthactivism.org; executive editor -- New Paradigm book series.
  For information on The International Paleopsychology Project, see: www.paleopsych.org
  for two chapters from 
  The Lucifer Principle: A Scientific Expedition Into the Forces of History, see www.howardbloom.net/lucifer
  For information on Global Brain: The Evolution of Mass Mind from the Big Bang to the 21st Century, see www.howardbloom.net


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