Saturday, May 05, 2018

Introduction to Softwarephysics

Softwarephysics is a simulated science for the simulated Software Universe that we are all immersed in. It is an approach to software development, maintenance and support based upon concepts from physics, chemistry, biology, and geology that I have been using on a daily basis for over 35 years as an IT professional. For those of you not in the business, IT is short for Information Technology, commercial computer science. The original purpose of softwarephysics was to explain why IT was so difficult, to suggest possible remedies, and to provide a direction for thought. Since then softwarephysics has taken on a larger scope, as it became apparent that softwarephysics could also assist the physical sciences with some of the Big Problems that they are currently having difficulties with. So if you are an IT professional, general computer user, or simply an individual interested in computer science, physics, chemistry, biology, or geology then softwarephysics might be of interest to you, if not in an entirely serious manner, perhaps at least in an entertaining one.

The Origin of Softwarephysics
From 1975 – 1979, I was an exploration geophysicist exploring for oil, first with Shell, and then with Amoco. In 1979, I made a career change into IT, and spent about 20 years in development. For the past 17 years, I have been in IT operations, supporting middleware on WebSphere, JBoss, Tomcat, and ColdFusion. When I first transitioned into IT from geophysics, I figured that if you could apply physics to geology; why not apply physics to software? So like the exploration team at Amoco that I had just left, consisting of geologists, geophysicists, paleontologists, geochemists, and petrophysicists, I decided to take all the physics, chemistry, biology, and geology that I could muster and throw it at the problem of software. The basic idea was that many concepts in physics, chemistry, biology, and geology suggested to me that the IT community had accidentally created a pretty decent computer simulation of the physical Universe on a grand scale, a Software Universe so to speak, and that I could use this fantastic simulation in reverse, to better understand the behavior of commercial software, by comparing software to how things behaved in the physical Universe. Softwarephysics depicts software as a virtual substance, and relies upon our understanding of the current theories in physics, chemistry, biology, and geology to help us model the nature of software behavior. So in physics we use software to simulate the behavior of the Universe, while in softwarephysics we use the Universe to simulate the behavior of software. Along these lines, we use the Equivalence Conjecture of Softwarephysics as an aid; it allows us to shift back and forth between the Software Universe and the physical Universe, and hopefully to learn something about one by examining the other:

The Equivalence Conjecture of Softwarephysics
Over the past 75 years, through the uncoordinated efforts of over 50 million independently acting programmers to provide the world with a global supply of software, the IT community has accidentally spent more than $10 trillion creating a computer simulation of the physical Universe on a grand scale – the Software Universe.

For more on the origin of softwarephysics please see Some Thoughts on the Origin of Softwarephysics and Its Application Beyond IT.

Logical Positivism and Effective Theories
Many IT professionals have a difficult time with softwarephysics because they think of physics as being limited to the study of real things like electrons and photons, and since software is not “real”, how can you possibly apply concepts from physics and the other sciences to software? To address this issue, softwarephysics draws heavily upon two concepts from physics that have served physics quite well over the past century – the concept of logical positivism and the concept of effective theories. This was not always the case. In the 17th, 18th, and 19th centuries, physicists mistakenly thought that they were actually discovering the fundamental laws of the Universe, which they thought were based upon real tangible things like particles, waves, and fields. Classical Newtonian mechanics (1687), thermodynamics (1850), and classical electrodynamics (1864) did a wonderful job of describing the everyday world at the close of the 19th century, but early in the 20th century it became apparent that the models upon which these very successful theories were based did not work very well for small things like atoms or for objects moving at high velocities or in strong gravitational fields. This provoked a rather profound philosophical crisis within physics at the turn of the century, as physicists worried that perhaps 300 years of work was about to go down the drain. The problem was that classical physicists confused their models of reality with reality itself, and when their classical models began to falter, their confidence in physics began to falter as well. This philosophical crisis was resolved with the adoption of the concepts of logical positivism and some new effective theories in physics. Quantum mechanics (1926) was developed for small things like atoms, the special theory of relativity (1905) was developed for objects moving at high velocities and the general theory of relativity (1915) was developed for objects moving in strong gravitational fields.

Logical positivism, usually abbreviated simply to positivism, is an enhanced form of empiricism, in which we do not care about how things “really” are; we are only interested with how things are observed to behave. With positivism, physicists only seek out models of reality - not reality itself. When we study quantum mechanics, we will find that the concept of reality gets rather murky in physics anyway, so this is not as great a loss as it might at first seem. By concentrating on how things are observed to behave, rather than on what things “really” are, we avoid the conundrum faced by the classical physicists. In retrospect, this idea really goes all the way back to the very foundations of physics. In Newton’s Principia (1687) he outlined Newtonian mechanics and his theory of gravitation, which held that the gravitational force between two objects was proportional to the product of their masses divided by the square of the distance between them. Newton knew that he was going to take some philosophical flack for proposing a mysterious force between objects that could reach out across the vast depths of space with no apparent mechanism, so he took a very positivistic position on the matter with the famous words:

I have not as yet been able to discover the reason for these properties of gravity from phenomena, and I do not feign hypotheses. For whatever is not deduced from the phenomena must be called a hypothesis; and hypotheses, whether metaphysical or physical, or based on occult qualities, or mechanical, have no place in experimental philosophy. In this philosophy particular propositions are inferred from the phenomena, and afterwards rendered general by induction.

Instead, Newton focused on how things were observed to move under the influence of his law of gravitational attraction, without worrying about what gravity “really” was.

The second concept, that of effective theories, is an extension of positivism. An effective theory is an approximation of reality that only holds true over a certain restricted range of conditions and only provides for a certain depth of understanding of the problem at hand. For example, Newtonian mechanics is an effective theory that makes very good predictions for the behavior of objects moving less than 10% of the speed of light and which are bigger than a very small grain of dust. These limits define the effective range over which Newtonian mechanics can be applied to solve problems. For very small things we must use quantum mechanics and for very fast things moving in strong gravitational fields, we must use relativity theory. So all of the current theories of physics, such as Newtonian mechanics, Newtonian gravity, classical electrodynamics, thermodynamics, statistical mechanics, the special and general theories of relativity, quantum mechanics, and the quantum field theories of QED and QCD are effective theories that are based upon models of reality, and all these models are approximations - all these models are fundamentally "wrong", but at the same time, these effective theories make exceedingly good predictions of the behavior of physical systems over the limited ranges in which they apply. That is the goal of softwarephysics – to provide for an effective theory of software behavior that makes useful predictions of software behavior that are applicable to the day-to-day activities of IT professionals. So in softwarephysics, we adopt a very positivistic viewpoint of software; we do not care what software “really is”, we only care about how software is observed to behave and try to model those behaviors with an effective theory of software behavior that only holds true over a certain restricted range of conditions and only provides for a certain depth of understanding of the problem at hand.

GPS satellites provide a very good example of positivism and effective theories at work. There are currently 31 GPS satellites orbiting at an altitude of 12,600 miles above the Earth, and each contains a very accurate atomic clock. The signals from the GPS satellites travel to your GPS unit at the speed of light, so by knowing the travel time of the signals from at least 4 of the GPS satellites, it is possible to determine your position on Earth very accurately. In order to do that, it is very important to have very accurate timing measurements. Newtonian mechanics is used to launch the GPS satellites to an altitude of 12,600 miles and to keep them properly positioned in orbit. Classical electrodynamics is then used to beam the GPS signals back down to Earth to the GPS unit in your car. Quantum mechanics is used to build the transistors on the chips on board the GPS satellites and to understand the quantum tunneling of electrons in the flash memory chips used to store GPS data on the satellites. The special theory of relativity predicts that the onboard atomic clocks on the GPS satellites will run slower and lose about 7.2 microseconds per day due to their high velocities relative to an observer on the Earth. But at the same time, the general theory of relativity also predicts that because the GPS satellites are further from the center of the Earth and in a weaker gravitational field, where spacetime is less deformed than on the surface of the Earth, their atomic clocks also run faster and gain 45.9 microseconds per day due to the weaker gravitational field out there. The net effect is a gain of 38.7 microseconds per day, so the GPS satellite atomic clocks have to be purposefully built to run slow by 38.7 microseconds per day before they are launched, so that they will keep in sync with clocks on the surface of the Earth. If this correction were not made, an error in your position of 100 yards/day would accrue. The end result of the combination of all these fundamentally flawed effective theories is that it is possible to pinpoint your location on Earth to an accuracy of 16 feet or better for as little as $100. But physics has done even better than that with its fundamentally flawed effective theories. By combining the effective theories of special relativity (1905) with quantum mechanics (1926), physicists were able to produce a new effective theory for the behavior of electrons and photons called quantum electrodynamics QED (1948) which was able to predict the gyromagnetic ratio of the electron, a measure of its intrinsic magnetic field, to an accuracy of 11 decimal places. As Richard Feynman has pointed out, this was like predicting the exact distance between New York and Los Angeles accurate to the width of a human hair!

So Newtonian mechanics makes great predictions for the macroscopic behavior of GPS satellites, but it does not work very well for small things like the behavior of individual electrons within transistors, where quantum mechanics is required, or for things moving at high speeds or in strong gravitational fields where relativity theory must be applied. And all three of these effective theories are based upon completely contradictory models. General relativity maintains that spacetime is curved by matter and energy, but that matter and energy are continuous, while quantum mechanics maintains that spacetime is flat, but that matter and energy are quantized into chunks. Newtonian mechanics simply states that space and time are mutually independent dimensions and universal for all, with matter and energy being continuous. The important point is that all effective theories and scientific models are approximations – they are all fundamentally "wrong". But knowing that you are "wrong" gives you a great advantage over people who know that they are "right", because knowing that you are "wrong" allows you to seek improved models of reality. So please consider softwarephysics to simply be an effective theory of software behavior that is based upon models that are fundamentally “wrong”, but at the same time, fundamentally useful for IT professionals. So as you embark upon your study of softwarephysics, please always keep in mind that the models of softwarephysics are just approximations of software behavior, they are not what software “really is”. It is very important not to confuse models of software behavior with software itself, if one wishes to avoid the plight of the 19th century classical physicists.

If you are an IT professional and many of the above concepts are new to you, do not be concerned. This blog on softwarephysics is aimed at a diverse audience, but with IT professionals in mind. All of the above ideas will be covered at great length in the postings in this blog on softwarephysics and in a manner accessible to all IT professionals. Now it turns out that most IT professionals have had some introduction to physics in high school or in introductory college courses, but that presents an additional problem. The problem is that such courses generally only cover classical physics, and leave the student with a very good picture of physics as it stood in 1864! It turns out that the classical physics of Newtonian mechanics, thermodynamics, and classical electromagnetic theory were simply too good to discard and are still quite useful, so they are taught first to beginners and then we run out of time to cover the really interesting physics of the 20th century. Now imagine the problems that the modern world would face if we only taught similarly antiquated courses in astronomy, metallurgy, electrical and mechanical engineering, medicine, economics, biology, or geology that happily left students back in 1864! Since many of the best models for software behavior stem from 20th century physics, we will be covering a great deal of 20th century material in these postings – the special and general theories of relativity, quantum mechanics, quantum field theories, and chaos theory, but I hope that you will find that these additional effective theories are quite interesting on their own, and might even change your worldview of the physical Universe at the same time.

Unintended Consequences for the Scientific Community
As I mentioned at the close of my original posting on SoftwarePhysics, my initial intention for this blog on softwarephysics was to fulfill a promise I made to myself about 25 years ago to approach the IT community with the concept of softwarephysics a second time, following my less than successful attempt to do so in the 1980s, with the hope of helping the IT community to better cope with the daily mayhem of life in IT. However, in laying down the postings for this blog an unintended consequence arose in my mind as I became profoundly aware of the enormity of this vast computer simulation of the physical Universe that the IT community has so graciously provided to the scientific community free of charge and also of the very significant potential scientific value that it provides. One of the nagging problems for many of the observational and experimental sciences is that many times there is only one example readily at hand to study or experiment with, and it is very difficult to do meaningful statistics with a population of N=1.

But the computer simulation of the physical Universe that the Software Universe presents provides another realm for comparison. For example, both biology and astrobiology only have one biosphere on Earth to study and even physics itself has only one Universe with which to engage. Imagine the possibilities if scientists had another Universe readily at hand in which to work! This is exactly what the Software Universe provides. For example, in SoftwareBiology and A Proposal For All Practicing Paleontologists we see that the evolution of software over the past 75 years, or 2.4 billion seconds, ever since Konrad Zuse first cranked up his Z3 computer in May of 1941, has closely followed the same path as life on Earth over the past 4.0 billion years in keeping with Simon Conway Morris’s contention that convergence has played the dominant role in the evolution of life on Earth. In When Toasters Fly, we also see that software has evolved in fits and starts as portrayed by the punctuated equilibrium of Stephen Jay Gould and Niles Eldredge, and in The Adaptationist View of Software Evolution we explore the overwhelming power of natural selection in the evolution of software. In keeping with Peter Ward’s emphasis on mass extinctions dominating the course of evolution throughout geological time, we also see in SoftwareBiology that there have been several dramatic mass extinctions of various forms of software over the past 75 years as well, that have greatly affected the evolutionary history of software, and that between these mass extinctions, software has also tended to evolve through the gradual changes of Hutton’s and Lyell’s uniformitarianism. In Software Symbiogenesis and Self-Replicating Information, we also see the very significant role that parasitic/symbiotic relationships have played in the evolution of software, in keeping with the work of Lynn Margulis and also of Freeman Dyson’s two-stage theory of the origin of life on Earth. In The Origin of Software the Origin of Life, we explore Stuart Kauffman’s ideas on how Boolean nets of autocatalytic chemical reactions might have kick-started the whole thing as an emergent behavior of an early chaotic pre-biotic environment on Earth, and that if Seth Shostak is right, we will never end up talking to carbon-based extraterrestrial aliens, but to alien software instead. In Is the Universe Fine-Tuned for Self-Replicating Information? we explore the thermodynamics of Brandon Carter’s Weak Anthropic Principle (1973), as it relates to the generation of universes in the multiverse that are capable of sustaining intelligent life. Finally, in Programming Clay we revisit Alexander Graham Cairns-Smith’s theory (1966) that Gene 1.0 did not run on nucleic acids, but on clay microcrystal precursors instead.

Similarly for the physical sciences, in Is the Universe a Quantum Computer? we find a correspondence between TCP/IP and John Cramer’s Transactional Interpretation of quantum mechanics. In SoftwarePhysics and Cyberspacetime, we also see that the froth of CPU processes running with a clock speed of 109 Hz on the 10 trillion currently active microprocessors that comprise the Software Universe can be viewed as a slowed down simulation of the spin-foam froth of interacting processes of loop quantum gravity running with a clock speed of 1043 Hz that may comprise the physical Universe. And in Software Chaos, we examine the nonlinear behavior of software and some of its emergent behaviors and follow up in CyberCosmology with the possibility that vast quantities of software running on large nonlinear networks might eventually break out into consciousness in accordance with the work of George Dyson and Daniel Dennett. Finally, in Model-Dependent Realism - A Positivistic Approach to Realism we compare Steven Weinberg’s realism with the model-dependent realism of Stephen Hawking and Leonard Mlodinow and how the two worldviews affect the search for a Final Theory. Finally, in The Software Universe as an Implementation of the Mathematical Universe Hypothesis and An Alternative Model of the Software Universe we at long last explore what software might really be, and discover that the Software Universe might actually be more closely related to the physical Universe than you might think.

The chief advantage of doing fieldwork in the Software Universe is that, unlike most computer simulations of the physical Universe, it is an unintended and accidental simulation, without any of the built-in biases that most computer simulations of the physical Universe suffer. So you will truly be able to do fieldwork in a pristine and naturally occuring simulation, just as IT professionals can do fieldwork in the wild and naturally occuring simulation of software that the living things of the biosphere provide. Secondly, the Software Universe is a huge simulation that is far beyond the budgetary means of any institution or consortium by many orders of magnitude. So if you are an evolutionary biologist, astrobiologist, or paleontologist working on the origin and evolution of life in the Universe, or a physicist or economist working on the emergent behaviors of nonlinear systems and complexity theory, or a neurobiologist working on the emergence of consciousness in neural networks, or even a frustrated string theorist struggling with quantum gravity, it would be well worth your while to pay a friendly call upon the local IT department of a major corporation in your area. Start with a visit to the Command Center for their IT Operations department to get a global view of their IT infrastructure and to see how it might be of assistance to the work in your area of interest. From there you can branch out to the applicable area of IT that will provide the most benefit.

The Impact of Self-Replicating Information Upon the Planet
One of the key findings of softwarephysics is concerned with the magnitude of the impact upon the planet of self-replicating information.

Self-Replicating Information – Information that persists through time by making copies of itself or by enlisting the support of other things to ensure that copies of itself are made.

Basically, we have seen several waves of self-replicating information dominate the Earth:
1. Self-replicating autocatalytic metabolic pathways of organic molecules
2. RNA
3. DNA
4. Memes
5. Software

Note that because the self-replicating autocatalytic metabolic pathways of organic molecules, RNA and DNA have become so heavily intertwined over time that now I simply refer to them as the “genes”. Over the past 4.0 billion years, the surface of the Earth has been totally reworked by three forms of self-replicating information – the genes, memes and software, with software rapidly becoming the dominant form of self-replicating information on the planet. For more on this see:

A Brief History of Self-Replicating Information
Self-Replicating Information
Is Self-Replicating Information Inherently Self-Destructive?
Enablement - the Definitive Characteristic of Living Things
Is the Universe Fine-Tuned for Self-Replicating Information?
How to Use an Understanding of Self-Replicating Information to Avoid War
The Great War That Will Not End
How to Use Softwarephysics to Revive Memetics in Academia

Softwarephysics and the Real World of Human Affairs
Having another universe readily at hand to explore, even a simulated universe like the Software Universe, necessarily has an impact upon one's personal philosophy of life, and allows one to draw certain conclusions about the human condition and what’s it all about, so as you read through the postings in this blog you will stumble across a bit of my own personal philosophy - definitely a working hypothesis still in the works. Along these lines you might be interested in a few postings where I try to apply softwarephysics to the real world of human affairs:

MoneyPhysics – my impression of the 2008 world financial meltdown.

The Fundamental Problem of Everything – if you Google "the fundamental problem of everything", this will be the only hit you get on the entire Internet, which is indicative of the fundamental problem of everything!

What’s It All About? and Genes, Memes and Software – my current working hypothesis on what’s it all about.

How to Use an Understanding of Self-Replicating Information to Avoid War – my current working hypothesis for how the United States can avoid getting bogged down again in continued war in the Middle East.

Hierarchiology and the Phenomenon of Self-Organizing Organizational Collapse - a modern extension of the classic Peter Principle that applies to all hierarchical organizations and introduces the Time Invariant Peter Principle.

The Economics of the Coming Software Singularity, The Enduring Effects of the Obvious Hiding in Plain Sight and The Dawn of Galactic ASI - Artificial Superintelligence - my take on some of the issues that will arise for mankind as software becomes the dominant form of self-replicating information upon the planet over the coming decades.

The Continuing Adventures of Mr. Tompkins in the Software Universe, The Danger of Tyranny in the Age of Software, Cyber Civil Defense, and Oligarchiology and the Rise of Software to Predominance in the 21st Century - my worries that the world might abandon democracy in the 21st century, as software comes to predominance as the dominant form of self-replicating information on the planet.

Making Sense of the Absurdity of the Real World of Human Affairs - how software has aided the expansion of our less desirable tendencies in recent years.

Some Specifics About These Postings
The postings in this blog are a supplemental reading for my course on softwarephysics for IT professionals entitled SoftwarePhysics 101 – The Physics of Cyberspacetime, which was originally designed to be taught as a series of seminars at companies where I was employed. Since softwarephysics essentially covers the simulated physics, chemistry, biology, and geology of an entire simulated universe, the slides necessarily just provide a cursory skeleton upon which to expound. The postings in this blog go into much greater depth. Because each posting builds upon its predecessors, the postings in this blog should be read in reverse order from the oldest to the most recent, beginning with my original posting on SoftwarePhysics. In addition, several universities also now offer courses on Biologically Inspired Computing which cover some of the biological aspects of softwarephysics, and the online content for some of these courses can be found by Googling for "Biologically Inspired Computing" or "Natural Computing". At this point we will finish up with my original plan for this blog on softwarephysics with a purely speculative posting on CyberCosmology that describes the origins of the Software Universe, cyberspacetime, software and where they all may be heading. Since CyberCosmology will be purely speculative in nature, it will not be of much help to you in your IT professional capacities, but I hope that it might be a bit entertaining. Again, if you are new to softwarephysics, you really need to read the previous posts before taking on CyberCosmology. I will probably continue on with some additional brief observations about softwarephysics in the future, but once you have completed CyberCosmology, you can truly consider yourself to be a bona fide softwarephysicist.

For those of you following this blog, the posting dates on the posts may seem to behave in a rather bizarre manner. That is because in order to get the Introduction to Softwarephysics listed as the first post in the context root of http://softwarephysics.blogspot.com/ I have to perform a few IT tricks. When publishing a new posting, I simply copy the contents of the Introduction to Softwarephysics to a new posting called the New Introduction to Softwarephysics. Then I update the original Introduction to Softwarephysics entry with the title and content of the new posting to be published. I then go back and take “New” out of the title of the New Introduction to Softwarephysics. This way the Introduction to Softwarephysics always appears as the first posting in the context root of http://softwarephysics.blogspot.com/. The side effect of all this is that the real posting date of posts is the date that appears on the post that you get when clicking on the Newer Post link at the bottom left of the posting webpage.

SoftwarePhysics 101 – The Physics of Cyberspacetime is now available on Microsoft OneDrive.

SoftwarePhysics 101 – The Physics of Cyberspacetime - Original PowerPoint document

Entropy – A spreadsheet referenced in the document

BSDE – A 1989 document describing how to use BSDE - the Bionic Systems Development Environment - to grow applications from genes and embryos within the maternal BSDE software.

Comments are welcome at scj333@sbcglobal.net

To see all posts on softwarephysics in reverse order go to:
http://softwarephysics.blogspot.com/

Regards,
Steve Johnston

Wednesday, November 22, 2017

Did Carbon-Based Life on Earth Really Have a LUCA - a Last Universal Common Ancestor?

The idea that all the current living things found on the Earth descended from one single cell long, long, ago runs deep in biology, going all the way back to Darwin himself in On the Origin of Species (1859), which had one single diagram in the whole volume - see Figure 1 down below. But is that really the case? In this posting, I would like to explore the possibility that it is not.

Figure 1 – Darwin's On the Origin of Species (1859) had one single figure, displayed above, that describes the tree of life descending from one single cell, later to be known as the LUCA - the Last Universal Common Ancestor.

The Phylogenic Tree of Life
Before proceeding further, recall that we now know that there actually are three forms of life on this planet, as first described by Carl Woese in 1977 at my old Alma Mater the University of Illinois - the Bacteria, the Archea and the Eukarya. The Bacteria and the Archea both use the simple prokaryotic cell architecture, while the Eukarya use the much more complicated eukaryotic cell architecture, and all of the "higher" forms of life that we are familiar with are simply made of aggregations of eukaryotic cells. Even the simple yeasts that make our breads, and get us drunk, consist of very complex eukaryotic cells. The troubling thing is that only an expert could tell the difference between a yeast eukaryotic cell and a human eukaryotic cell because they are so similar, while any school child could easily tell the difference between the microscopic images of a prokaryotic bacterial cell and a eukaryotic yeast cell - see Figure 2.

Figure 2 – The prokaryotic cell architecture of the bacteria and archaea is very simple and designed for rapid replication. Prokaryotic cells do not have a nucleus enclosing their DNA. Eukaryotic cells, on the other hand, store their DNA on chromosomes that are isolated in a cellular nucleus. Eukaryotic cells also have a very complex internal structure with a large number of organelles, or subroutine functions, that compartmentalize the functions of life within the eukaryotic cells.

Prokaryotic cells essentially consist of a tough outer cell wall enclosing an inner cell membrane and contain a minimum of internal structure. The cell membrane is composed of phospholipids and proteins. The DNA within prokaryotic cells generally floats freely as a large loop of DNA, and their ribosomes, used to help translate mRNA into proteins, float freely within the entire cell as well. The ribosomes in prokaryotic cells are not attached to membranes, like they are in eukaryotic cells, which have membranes called the rough endoplasmic reticulum for that purpose. The chief advantage of prokaryotic cells is their simple design and the ability to thrive and rapidly reproduce even in very challenging environments, like little AK-47s that still manage to work in environments where modern tanks will fail. Eukaryotic cells, on the other hand, are found in the bodies of all complex organisms, from single-celled yeasts to you and me, and they divide up cell functions amongst a collection of organelles (functional subroutines), such as mitochondria, chloroplasts, Golgi bodies, and the endoplasmic reticulum. Figure 3 depicts Carl Woese's rewrite of Darwin's famous tree of life, and shows that complex forms of life, like you and me, that are based upon cells using the eukaryotic cell architecture, actually spun off from the archaea and not the bacteria. Now archaea and bacteria look identical under a microscope, and that is the reason why at first we thought they were all just bacteria for hundreds of years. But in the 1970s Carl Woese discovered that the ribosomes used to transcribe mRNA into proteins were different between certain microorganisms that had all been previously lumped together as "bacteria". Carl Woese determined that the lumped together "bacteria" really consisted of two entirely different forms of life - the bacteria and the archaea - see Figure 3. The bacteria and archaea both have cell walls, but use slightly different organic molecules to build them. Some archaea, known as the extremophiles that live in harsh conditions also wrap their DNA around stabilizing histone proteins. Eukaryotes also wrap their DNA around histone proteins to form chromatin and chromosomes - for more on that see: An IT Perspective on the Origin of Chromatin, Chromosomes and Cancer. For that reason, and other biochemical reactions that the archaea and eukaryotes both share, it is now thought that the eukaryotes split off from the archaea and not the bacteria.

Figure 3 – In 1977 Carl Woese developed a new tree of life consisting of the Bacteria, the Archea and the Eukarya. The Bacteria and Archea use a simple prokaryotic cell architecture, while the Eukarya use the much more complicated eukaryotic cell structure.

The other thing about eukaryotic cells, as opposed to prokaryotic cells, is that eukaryotic cells are HUGE! They are like 15,000 times larger by volume than prokaryotic cells! See Figure 4 for a true-scale comparison of the two.

Figure 4 – Not only are eukaryotic cells much more complicated than prokaryotic cells, they are also HUGE!

Recall that in the Endosymbiosis theory of Lynn Margulis, it is thought that the mitochondria of eukaryotic cells were originally parasitic bacteria that once invaded archaeal prokaryotic cells and took up residence. Certain of those ancient archaeal prokaryotic cells, with their internal bacterial mitochondrial parasites, were then able to survive the parasitic bacterial onslaught, and later, went on to form a strong parasitic/symbiotic relationship with them, like all forms of self-replicating information tend to do. The reason researchers think this is what happened is because mitochondria have their own DNA and that DNA is stored as a loose loop like bacteria store their DNA. In The Rise of Complexity in Living Things and Software we explored Nick Lane's contention that it was the arrival of the parasitic/symbiotic prokaryotic mitochondria in eukaryotic cells that provided the necessary energy to produce the very complicated eukaryotic cell architecture.

Originally, Carl Woese proposed that all three Domains diverged from one single line of "progenotes" in the distant past, as depicted in Figure 5 below. Over time, this became the standard model for the early diversification of life on the Earth.

Figure 5 – Originally, Carl Woese proposed that all three domains diverged from a single "progenote". From Phylogenetic Classification and the Universal Tree (1999) by W. Ford Doolittle.

But in later years, he and others, like W. Ford Doolittle, proposed the three domains diverged from a network of progenotes that shared many genes amongst themselves by way of lateral gene transmission, as depicted in Figure 6 below.

Figure 6 – Some now propose that the three domains diverged from a network of progenotes that shared many genes amongst themselves by way of lateral gene transmission. From Phylogenetic Classification and the Universal Tree (1999) by W. Ford Doolittle.

For more on that see Phylogenetic Classification and the Universal Tree (1999) by W. Ford Doolittle at:

http://www.faculty.biol.ttu.edu/Strauss/Phylogenetics/Readings/Doolittle1999.pdf

I just finished reading The Common Ancestor of Archaea and Eukarya Was Not an Archaeon (2013) by Patrick Forterre which is available at:

https://www.hindawi.com/journals/archaea/2013/372396/

This paper calls into question the current idea that the Eukarya Domain arose from an archaeal prokaryotic cell that fused with bacterial prokaryotic cells. In the The Common Ancestor of Archaea and Eukarya Was Not an Archaeon, Patrick Forterre contends that the eukaryotic cell architecture actually arose from a third separate line of protoeukaryotic cells that were more complicated than prokaryotic archaeal cells, but simpler than today's complex eukaryotic cells. In this view, modern simple prokaryotic archaeal and bacterial cells evolved from the more complex protoeukaryotic cell by means of simplification in order to occupy high-temperature environments. This is best summed up by the abstract for the above paper:

Abstract
It is often assumed that eukarya originated from archaea. This view has been recently supported by phylogenetic analyses in which eukarya are nested within archaea. Here, I argue that these analyses are not reliable, and I critically discuss archaeal ancestor scenarios, as well as fusion scenarios for the origin of eukaryotes. Based on recognized evolutionary trends toward reduction in archaea and toward complexity in eukarya, I suggest that their last common ancestor was more complex than modern archaea but simpler than modern eukaryotes (the bug in-between scenario). I propose that the ancestors of archaea (and bacteria) escaped protoeukaryotic predators by invading high-temperature biotopes, triggering their reductive evolution toward the “prokaryotic” phenotype (the thermoreduction hypothesis). Intriguingly, whereas archaea and eukarya share many basic features at the molecular level, the archaeal mobilome resembles more the bacterial than the eukaryotic one. I suggest that selection of different parts of the ancestral virosphere at the onset of the three domains played a critical role in shaping their respective biology. Eukarya probably evolved toward complexity with the help of retroviruses and large DNA viruses, whereas similar selection pressure (thermoreduction) could explain why the archaeal and bacterial mobilomes somehow resemble each other.


In the paper, Patrick Forterre goes into great detail discussing the many similarities and differences between the Bacteria, the Archea and the Eukarya on a biochemical level. While reading the paper, I once again began to appreciate the great difficulties that arise when trying to piece together the early evolution of carbon-based life on the Earth in deep time, with only one example readily at hand to study. I was particularly struck by the many differences between the biochemistry of the Bacteria, the Archea and the Eukarya that did not seem to jive with them all coming from a common ancestor - a LUCA or Last Universal Common Ancestor. As a softwarephysicist, I naturally began to think back on the historical evolution of software and of other forms of self-replicating information over time. This led me to wonder if the Bacteria, the Archea and the Eukarya really all evolved from a common LUCA? What if the Bacteria, the Archea and the Eukarya actually represented the vestiges of three separate lines of descent that all independently arose on their own? Perhaps carbon-based life arose many times on the early Earth and the Bacteria, the Archea and the Eukarya just represent the last collection of survivors? The differences between the Bacteria, the Archea and the Eukarya could be due to their separate originations, and their similarities could be due to them converging upon similar biochemical solutions to solve similar problems.

The Power of Convergence
In biology, convergence is the idea that sometimes organisms that are not at all related will come up with very similar solutions to common problems that they share. For example, the concept of the eye has independently evolved at least 40 different times in the past 600 million years, so there are many examples of “living fossils” showing the evolutionary path. For example, the camera-like structures of the human eye and the eye of an octopus are nearly identical, even though each structure evolved totally independent of each other. Could it be that the complex structures of the Bacteria, the Archea and the Eukarya also evolved from dead organic molecules independently?

Figure 7 - The eye of a human and the eye of an octopus are nearly identical in structure, but evolved totally independently of each other. As Daniel Dennett pointed out, there are only a certain number of Good Tricks in Design Space and natural selection will drive different lines of descent towards them.

Similarly, in SoftwareBiology and A Proposal For All Practicing Paleontologists we see that the evolution of software over the past 77 years, or 2.4 billion seconds, ever since Konrad Zuse first cranked up his Z3 computer in May of 1941, has closely followed the same path as life on Earth over the past 4.0 billion years in keeping with Simon Conway Morris’s contention that convergence has played the dominant role in the evolution of life on Earth. As I mentioned above, an oft-cited example of this is the evolution of the complex camera-like human eye. Even Darwin himself had problems with trying to explain how something as complicated as the human eye could have evolved through small incremental changes from some structure that could not see at all. After all, what good is 1% of an eye? As I have often stated in the past, this is not a difficult thing for IT professionals to grasp because we are constantly evolving software on a daily basis through small incremental changes to our applications. However, when we do look back over the years to what our small incremental changes have wrought, it is quite surprising to see just how far our applications have come from their much simpler ancestors and to realize that it would be very difficult for an outsider to even recognize their ancestral forms. However, with the aid of computers, many researchers in evolutionary biology have shown just how easily a camera-like eye can evolve. Visible photons have an energy of about 1 – 3 eV, which is about the energy of most chemical reactions. Consequently, visible photons are great for stimulating chemical reactions, like the reactions in chlorophyll that turn the energy of visible photons into the chemical energy of carbohydrates or stimulating the chemical reactions of other light-sensitive molecules that form the basis of sight. In a computer simulation, the eye can simply begin as a flat eyespot of photosensitive cells that look like a patch like this: |. In the next step, the eyespot forms a slight depression, like the beginnings of the letter C, which allows the simulation to have some sense of image directionality because the light from a distant source will hit different sections of the photosensitive cells on the back part of the C. As the depression deepens and the hole in the C gets smaller, the incipient eye begins to behave like a pin hole camera that forms a clearer, but dimmer, image on the back part of the C. Next a transparent covering covers over the hole in the pin hole camera to provide some protection for the sensitive cells at the back of the eye, and a transparent humor fills the eye to keep its shape: C). Eventually, the transparent covering thickens into a flexible lens under the protective covering that can be used to focus light, and to allow for a wider entry hole that provides a brighter image, essentially decreasing the f-stop of the eye like in a camera: C0).

So it is easy to see how a 1% eye could easily evolve into a modern complex eye through small incremental changes that always improve the visual acuity of the eye. Such computer simulations predict that a camera-like eye could easily evolve in as little as 500,000 years.

Figure 8 – Computer simulations of the evolution of a camera-like eye (click to enlarge).

Now the concept of the eye has independently evolved at least 40 different times in the past 600 million years, so there are many examples of “living fossils” showing the evolutionary path. In Figure 9 below, we see that all of the steps in the computer simulation of Figure 8 can be found today in various mollusks. Notice that the human-like eye on the far right is really that of an octopus, not a human, again demonstrating the power of natural selection to converge upon identical solutions by organisms with separate lines of descent.

Figure 9 – There are many living fossils that have left behind signposts along the trail to the modern camera-like eye. Notice that the human-like eye on the far right is really that of an octopus (click to enlarge).

Could it be that the very similar unicellular designs of the Bacteria, the Archea and the Protoeukarya represent yet another example of convergence bringing forth very complex structures multiple times, that of membrane-based living cells, from the extant organic molecules of the early earth? I know that is a pretty wild idea but think of the implications. It would mean that the probability of living things emerging from organic molecules was nearly assured given the right conditions, and that simple unicellular life in our Universe should be quite common. In The Bootstrapping Algorithm of Carbon-Based Life I covered Dave Deamer's and Bruce Damer's new Hot Spring Origins Hypothesis model for the origin of carbon-based life on the early Earth. Perhaps such a model is not limited to producing only a single type of membrane-based form of unicellular life. In fact, I would contend that its Bootstrapping Algorithm might indeed produce a number of such types. Perhaps a little softwarephysics might shed some light on the subject.

Some Help From Softwarephysics
Recall that one of the fundamental findings of softwarephysics is that carbon-based life and software are both forms of self-replicating information, and that both have converged upon similar solutions to combat the second law of thermodynamics in a highly nonlinear Universe. For biologists, the value of softwarephysics is that software has been evolving about 100 million times faster than living things over the past 77 years, or 2.4 billion seconds, ever since Konrad Zuse first cranked up his Z3 computer in May of 1941, and the evolution of software over that period of time is the only history of a form of self-replicating information that has actually been recorded by human history. In fact, the evolutionary history of software has all occurred within a single human lifetime, and many of those humans are still alive today to testify as to what actually had happened, something that those working on the origin of life on the Earth and its early evolution can only try to imagine. Again, in softwarephysics, we define self-replicating information as:

Self-Replicating Information – Information that persists through time by making copies of itself or by enlisting the support of other things to ensure that copies of itself are made.

The Characteristics of Self-Replicating Information
All forms of self-replicating information have some common characteristics:

1. All self-replicating information evolves over time through the Darwinian processes of innovation and natural selection, which endows self-replicating information with one telling characteristic – the ability to survive in a Universe dominated by the second law of thermodynamics and nonlinearity.

2. All self-replicating information begins spontaneously as a parasitic mutation that obtains energy, information and sometimes matter from a host.

3. With time, the parasitic self-replicating information takes on a symbiotic relationship with its host.

4. Eventually, the self-replicating information becomes one with its host through the symbiotic integration of the host and the self-replicating information.

5. Ultimately, the self-replicating information replaces its host as the dominant form of self-replicating information.

6. Most hosts are also forms of self-replicating information.

7. All self-replicating information has to be a little bit nasty in order to survive.

8. The defining characteristic of self-replicating information is the ability of self-replicating information to change the boundary conditions of its utility phase space in new and unpredictable ways by means of exapting current functions into new uses that change the size and shape of its particular utility phase space. See Enablement - the Definitive Characteristic of Living Things for more on this last characteristic.

So far we have seen 5 waves of self-replicating information sweep across the Earth, with each wave greatly reworking the surface and near subsurface of the planet as it came to predominance:

1. Self-replicating autocatalytic metabolic pathways of organic molecules
2. RNA
3. DNA
4. Memes
5. Software

Software is now rapidly becoming the dominant form of self-replicating information on the planet, and is having a major impact on mankind as it comes to predominance. For more on that see: A Brief History of Self-Replicating Information. To gain some insights let us take a look at the origin of software and certain memes to see if they had a common LUCA, or if they independently arose several times instead, and then later merged. The origin of human languages and writing will serve our purposes for the origins of a class of memes. As Daniel Dennett pointed out, languages are simply memes that you can speak. Writing is just a meme for recording memes.

The Rise of Software
Currently, we are witnessing one of those very rare moments in time when a new form of self-replicating information, in the form of software, is coming to dominance. Software is now so ubiquitous that it now seems like the whole world is immersed in a Software Universe of our own making, surrounded by PCs, tablets, smartphones and the software now embedded in most of mankind's products. In fact, I am now quite accustomed to sitting with audiences of younger people who are completely engaged with their "devices", before, during and after a performance. This may seem like a very recent development in the history of mankind, but in Crocheting Software we saw that crochet patterns are actually forms of software that date back to the early 19th century! In Crocheting Software we also saw that the origin of computer software was such a hodge-podge of precursors, false starts, and failed attempts that it is nearly impossible to pinpoint an exact date for its origin, but for the purposes of softwarephysics I have chosen May of 1941, when Konrad Zuse first cranked up his Z3 computer, as the starting point for modern software. Zuse wanted to use his Z3 computer to perform calculations for aircraft designs that were previously done manually in a very tedious manner. In the discussion below, I will first outline a brief history of the evolution of hardware technology to explain how we got to this state, but it is important to keep in mind that it was the relentless demands of software for more and more memory and CPU-cycles over the years that really drove the exponential explosion of hardware capability. I hope to show that software independently arose many times over the years, using many differing hardware technologies. Self-replicating information is very opportunistic, and will exapt whatever hardware happens to be available at the time. Four billion years ago, carbon-based life exapted the extant organic molecules and the naturally occurring geochemical cycles of the day in order to bootstrap itself into existence, and that is what software has been doing for the past 2.4 billion seconds on the Earth. As we briefly cover the evolutionary history of computer hardware down below, please keep in mind that for each new generation of machines, the accompanying software had to essentially independently arise again because each new machine had a unique instruction set, meaning that an executable program on one computer could not run on a different computer because they had different instruction sets. As IT professionals, writing and supporting software, and as end-users, installing and using software, we are all essentially software enzymes caught up in a frantic interplay of self-replicating information. Software is currently domesticating our minds, to churn out ever more software, of ever-increasing complexity, and this will likely continue at an ever-accelerating pace, until one day, when software finally breaks free, and begins to generate itself using AI and machine learning techniques. For more details on the evolutionary history of software see the SoftwarePaleontology section of SoftwareBiology. See Software Embryogenesis for a description of the software architecture of a modern high-volume corporate website in action, just prior to the current Cloud Computing Revolution that we are now experiencing.

A Brief Evolutionary History of Computer Hardware
It all started back in May of 1941 when Konrad Zuse first cranked up his Z3 computer. The Z3 was the world's first real computer and was built with 2400 electromechanical relays that were used to perform the switching operations that all computers use to store information and to process it. To build a computer, all you need is a large network of interconnected switches that have the ability to switch each other on and off in a coordinated manner. Switches can be in one of two states, either open (off) or closed (on), and we can use those two states to store the binary numbers of “0” or “1”. By using a number of switches teamed together in open (off) or closed (on) states, we can store even larger binary numbers, like “01100100” = 38. We can also group the switches into logic gates that perform logical operations. For example, in Figure 10 below we see an AND gate composed of two switches A and B. Both switch A and B must be closed in order for the light bulb to turn on. If either switch A or B is open, the light bulb will not light up.

Figure 10 – An AND gate can be simply formed from two switches. Both switches A and B must be closed, in a state of “1”, in order to turn the light bulb on.

Additional logic gates can be formed from other combinations of switches as shown in Figure 11 below. It takes about 2 - 8 switches to create each of the various logic gates shown below.

Figure 11 – Additional logic gates can be formed from other combinations of 2 – 8 switches.

Once you can store binary numbers with switches and perform logical operations upon them with logic gates, you can build a computer that performs calculations on numbers. To process text, like names and addresses, we simply associate each letter of the alphabet with a binary number, like in the ASCII code set where A = “01000001” and Z = ‘01011010’ and then process the associated binary numbers.

Figure 12 – Konrad Zuse with a reconstructed Z3 in 1961 (click to enlarge).


Figure 13 – Block diagram of the Z3 architecture (click to enlarge).

The electrical relays used by the Z3 were originally meant for switching telephone conversations. Closing one relay allowed current to flow to another relay’s coil, causing that relay to close as well.

Figure 14 – The Z3 was built using 2400 electrical relays, originally meant for switching telephone conversations.

Figure 15 – The electrical relays used by the Z3 for switching were very large, very slow and used a great deal of electricity which generated a great deal of waste heat.

Now I was born about 10 years later in 1951, a few months after the United States government installed its very first commercial computer, a UNIVAC I, for the Census Bureau on June 14, 1951. The UNIVAC I was 25 feet by 50 feet in size, and contained 5,600 vacuum tubes, 18,000 crystal diodes and 300 relays with a total memory of 12 K. From 1951 to 1958 a total of 46 UNIVAC I computers were built and installed.

Figure 16 – The UNIVAC I was very impressive on the outside.

Figure 17 – But the UNIVAC I was a little less impressive on the inside.

Figure 18 – Most of the electrical relays of the Z3 were replaced with vacuum tubes in the UNIVAC I, which were also very large, used lots of electricity and generated lots of waste heat too, but the vacuum tubes were 100,000 times faster than relays.

Figure 19 – Vacuum tubes contain a hot negative cathode that glows red and boils off electrons. The electrons are attracted to the cold positive anode plate, but there is a gate electrode between the cathode and anode plate. By changing the voltage on the grid, the vacuum tube can control the flow of electrons like the handle of a faucet. The grid voltage can be adjusted so that the electron flow is full blast, a trickle, or completely shut off, and that is how a vacuum tube can be used as a switch.

In the 1960s the vacuum tubes were replaced by discrete transistors and in the 1970s the discrete transistors were replaced by thousands of transistors on a single silicon chip. Over time, the number of transistors that could be put onto a silicon chip increased dramatically, and today, the silicon chips in your personal computer hold many billions of transistors that can be switched on and off in about 10-10 seconds. Now let us look at how these transistors work.

There are many different kinds of transistors, but I will focus on the FET (Field Effect Transistor) that is used in most silicon chips today. A FET transistor consists of a source, gate and a drain. The whole affair is laid down on a very pure silicon crystal using a multi-step process that relies upon photolithographic processes to engrave circuit elements upon the very pure silicon crystal. Silicon lies directly below carbon in the periodic table because both silicon and carbon have 4 electrons in their outer shell and are also missing 4 electrons. This makes silicon a semiconductor. Pure silicon is not very electrically conductive in its pure state, but by doping the silicon crystal with very small amounts of impurities, it is possible to create silicon that has a surplus of free electrons. This is called N-type silicon. Similarly, it is possible to dope silicon with small amounts of impurities that decrease the amount of free electrons, creating a positive or P-type silicon. To make an FET transistor you simply use a photolithographic process to create two N-type silicon regions onto a substrate of P-type silicon. Between the N-type regions is found a gate which controls the flow of electrons between the source and drain regions, like the grid in a vacuum tube. When a positive voltage is applied to the gate, it attracts the remaining free electrons in the P-type substrate and repels its positive holes. This creates a conductive channel between the source and drain which allows a current of electrons to flow.

Figure 20 – A FET transistor consists of a source, gate and drain. When a positive voltage is applied to the gate, a current of electrons can flow from the source to the drain and the FET acts like a closed switch that is “on”. When there is no positive voltage on the gate, no current can flow from the source to the drain, and the FET acts like an open switch that is “off”.

Figure 21 – When there is no positive voltage on the gate, the FET transistor is switched off, and when there is a positive voltage on the gate the FET transistor is switched on. These two states can be used to store a binary “0” or “1”, or can be used as a switch in a logic gate, just like an electrical relay or a vacuum tube.



Figure 22 – Above is a plumbing analogy that uses a faucet or valve handle to simulate the actions of the source, gate and drain of an FET transistor.

The CPU chip in your computer consists largely of transistors in logic gates, but your computer also has a number of memory chips that use transistors that are “on” or “off” and can be used to store binary numbers or text that is encoded using binary numbers. The next thing we need is a way to coordinate the billions of transistor switches in your computer. That is accomplished with a system clock. My current laptop has a clock speed of 2.5 GHz which means it ticks 2.5 billion times each second. Each time the system clock on my computer ticks, it allows all of the billions of transistor switches on my laptop to switch on, off, or stay the same in a coordinated fashion. So while your computer is running, it is actually turning on and off billions of transistors billions of times each second – and all for a few hundred dollars!

Computer memory was another factor greatly affecting the origin and evolution of software over time. Strangely, the original Z3 used electromechanical switches to store working memory, like we do today with transistors on memory chips, but that made computer memory very expensive and very limited, and this remained true all during the 1950s and 1960s. Prior to 1955 computers, like the UNIVAC I that first appeared in 1951, were using mercury delay lines that consisted of a tube of mercury that was about 3 inches long. Each mercury delay line could store about 18 bits of computer memory as sound waves that were continuously refreshed by quartz piezoelectric transducers on each end of the tube. Mercury delay lines were huge and very expensive per bit so computers like the UNIVAC I only had a memory of 12 K (98,304 bits).

Figure 23 – Prior to 1955, huge mercury delay lines built from tubes of mercury that were about 3 inches long were used to store bits of computer memory. A single mercury delay line could store about 18 bits of computer memory as a series of sound waves that were continuously refreshed by quartz piezoelectric transducers at each end of the tube.

In 1955 magnetic core memory came along, and used tiny magnetic rings called "cores" to store bits. Four little wires had to be threaded by hand through each little core in order to store a single bit, so although magnetic core memory was a lot cheaper and smaller than mercury delay lines, it was still very expensive and took up lots of space.

Figure 24 – Magnetic core memory arrived in 1955 and used a little ring of magnetic material, known as a core, to store a bit. Each little core had to be threaded by hand with 4 wires to store a single bit.

Figure 25 – Magnetic core memory was a big improvement over mercury delay lines, but it was still hugely expensive and took up a great deal of space within a computer.



Figure 26 – Finally in the early 1970s inexpensive semiconductor memory chips came along that made computer memory small and cheap.

Again, it was the relentless drive of software for ever-increasing amounts of memory and CPU-cycles that made all this happen, and that is why you can now comfortably sit in a theater with a smartphone that can store more than 10 billion bytes of data, while back in 1951 the UNIVAC I occupied an area of 25 feet by 50 feet to store 12,000 bytes of data. Like all forms of self-replicating information tend to do, over the past 2.4 billion seconds, software has opportunistically exapted the extant hardware of the day - the electromechanical relays, vacuum tubes, discrete transistors and transistor chips of the emerging telecommunications and consumer electronics industries, into the service of self-replicating software of ever-increasing complexity, as did carbon-based life exapt the extant organic molecules and the naturally occurring geochemical cycles of the day in order to bootstrap itself into existence.

But when I think back to my early childhood in the early 1950s, I can still vividly remember a time when there essentially was no software at all in the world. In fact, I can still remember my very first encounter with a computer on Monday, Nov. 19, 1956, watching the Art Linkletter TV show People Are Funny with my parents on an old black and white console television set that must have weighed close to 150 pounds. Art was showcasing the 21st UNIVAC I to be constructed and had it sorting through the questionnaires from 4,000 hopeful singles, looking for the ideal match. The machine paired up John Caran, 28, and Barbara Smith, 23, who later became engaged. And this was more than 40 years before eHarmony.com! To a five-year-old boy, a machine that could “think” was truly amazing. Since that very first encounter with a computer back in 1956, I have personally witnessed software slowly becoming the dominant form of self-replicating information on the planet, and I have also seen how software has totally reworked the surface of the planet to provide a secure and cozy home for more and more software of ever- increasing capability. For more on this please see A Brief History of Self-Replicating Information. That is why I think there would be much to be gained in exploring the origin and evolution of the $10 trillion computer simulation that the Software Universe provides, and that is what softwarephysics is all about.

The Origin of Human Languages
Now I am not a linguist, but from what I can find on Google, there seem to be about 5000 languages spoken in the world today that linguists divide into about 20 families, and we suspect there were many more languages in days gone by when people lived in smaller groups. The oldest theory for the origin of human language is known as monogenesis, and like the concept of a single LUCA in biology, it posits that language spontaneously arose only once and that all of the other thousands of languages then diverged from this single mother tongue, sort of like the Tower of Babel in the book of Genesis. The second theory is known as polygenesis, and it posits that human language emerged independently many times in many separate far-flung groups. These multiple origins of language then began to differentiate, and that is why we now have 5,000 different languages grouped into 20 different families. Each of the 20 families might remain as a vestige of the multiple originations of human language from the separate mother tongues. Many linguists in the United States are in favor of a form of monogenesis known as the Mother Tongue Theory, which stems from the Out of Africa Theory for the original dispersion of Homo sapiens throughout the world. The Mother Tongue Theory holds that an original human language originated about 150,000 years ago in Africa, and that language went along for the ride when Homo sapiens diffused out of Africa to colonize the entire world. So the jury is still out, and probably always will be, on the proposition of human languages having a single LUCA. Personally, I find the extreme diversity of human languages to favor the independent polygenesis of human language multiple times by many independent groups of Homo sapiens. But that might stem from having taken a bit of Spanish, Latin, and German in grade school and high school. When I got to college, I took a year of Russian, only to learn that there was a whole different way of communicating!

The Origin of Writing Systems
The origin of writing systems seems to provide a more fruitful analogy because writing systems are more recent, and by definition, they leave behind a "fossil record" in written form. Since I am getting way beyond my area of expertise, let me quote directly from the Wikipedia at:

https://en.wikipedia.org/wiki/History_of_writing

It is generally agreed that true writing of language was independently conceived and developed in at least two ancient civilizations and possibly more. The two places where it is most certain that the concept of writing was both conceived and developed independently are in ancient Sumer (in Mesopotamia), around 3100 BC, and in Mesoamerica by 300 BC, because no precursors have been found to either of these in their respective regions. Several Mesoamerican scripts are known, the oldest being from the Olmec or Zapotec of Mexico.

Independent writing systems also arose in Egypt around 3100 BC and in China around 1200 BC, but historians debate whether these writing systems were developed completely independently of Sumerian writing or whether either or both were inspired by Sumerian writing via a process of cultural diffusion. That is, it is possible that the concept of representing language by using writing, though not necessarily the specifics of how such a system worked, was passed on by traders or merchants traveling between the two regions.

Ancient Chinese characters are considered by many to be an independent invention because there is no evidence of contact between ancient China and the literate civilizations of the Near East, and because of the distinct differences between the Mesopotamian and Chinese approaches to logography and phonetic representation. Egyptian script is dissimilar from Mesopotamian cuneiform, but similarities in concepts and in earliest attestation suggest that the idea of writing may have come to Egypt from Mesopotamia. In 1999, Archaeology Magazine reported that the earliest Egyptian glyphs date back to 3400 BC, which "challenge the commonly held belief that early logographs, pictographic symbols representing a specific place, object, or quantity, first evolved into more complex phonetic symbols in Mesopotamia."

Similar debate surrounds the Indus script of the Bronze Age Indus Valley civilization in Ancient India (2600 BC). In addition, the script is still undeciphered, and there is debate about whether the script is true writing at all or, instead, some kind of proto-writing or nonlinguistic sign system.

An additional possibility is the undeciphered Rongorongo script of Easter Island. It is debated whether this is true writing and, if it is, whether it is another case of cultural diffusion of writing. The oldest example is from 1851, 139 years after their first contact with Europeans. One explanation is that the script was inspired by Spain's written annexation proclamation in 1770.

Various other known cases of cultural diffusion of writing exist, where the general concept of writing was transmitted from one culture to another, but the specifics of the system were independently developed. Recent examples are the Cherokee syllabary, invented by Sequoyah, and the Pahawh Hmong system for writing the Hmong language.


Therefore, I think that it can be safely assumed that many memes, like the meme for writing, making pots and flake tools, arose independently multiple times throughout human history and then those memes further differentiated.

Notice that, like the origin of true software from its many precursors, it is very difficult to pick an exact date for the origin of true writing from its many precursors too. When exactly do cartoon-like symbols evolve into a true written language? The origin of true languages from the many grunts of Homo sapiens might also have been very difficult to determine. Perhaps very murky origins are just another common characteristic of all forms of self-replicating information, including carbon-based life.

Conclusion
Most likely, the idea that the Bacteria, the Archea and the Eukarya represent the last vestiges of three separate lines of descent that independently arose from dead organic molecules in the distant past, with no LUCA - Last Universal Common Ancestor, is most probably incorrect. However, given the chaotic origination histories of many forms of self-replicating information, including the memes and software, I also have reservations about the idea that all carbon-based life on the Earth sprang from one single LUCA too, as depicted in Figure 5. It would seem most likely that there were many precursors to carbon-based cellular life on the Earth, and that it would be nearly impossible to have identified when carbon-based life actually came to be, even if we were around to watch it all happen. Perhaps 4.0 billion years ago, carbon-based life independently arose several times with many common biochemical characteristics because those common biochemical characteristics were the only ones that worked at the time, as depicted in Figure 6. Later, these separate originations of life most likely merged somewhat in the parasitic/symbiotic manner that all forms of self-replicating information are prone to do to form the Bacteria, the Archea and the Eukarya. At least it's something to think about.

Comments are welcome at scj333@sbcglobal.net

To see all posts on softwarephysics in reverse order go to:
http://softwarephysics.blogspot.com/

Regards,
Steve Johnston

Tuesday, September 26, 2017

The Perils of Software Enhanced Confirmation Bias

How often do you dramatically change your worldview opinion on an issue? If you are like me, that seldom happens, and I think that is the general rule, even when we are confronted with new evidence that explicitly challenges our current deeply held positions. My observation is that people nearly always simply dismiss any new evidence that arrives on the scene that does not confirm their current worldview. Instead, we normally only take seriously new evidence that reinforces our current worldview. Only when confronted with overwhelming evidence that impacts us on a very personal level, like a category 5 hurricane destroying our lives, do we very rarely change our minds about an issue. The tendency to simply stick with your current worldview, even in the face of mounting evidence that contradicts that worldview, is called confirmation bias because we all naturally only tend to seek out information that confirms our current beliefs, and at the same time, tend to dismiss any evidence that calls them into question. This is nothing new. The English philosopher and scientist Francis Bacon (1561–1626), in his Novum Organum (1620), noted that the biased assessment of evidence greatly influenced the way we all think about things. He wrote:

The human understanding when it has once adopted an opinion ... draws all things else to support and agree with it. And though there be a greater number and weight of instances to be found on the other side, yet these it either neglects or despises, or else by some distinction sets aside or rejects.

But in recent years this dangerous defect in the human thought process has been dramatically amplified by search and social media software, like Google, Facebook and Twitter. This became quite evident during the very contentious 2016 election in the United States of America, and also during this past year when the new Administration came to power. But why? I have a high level of confidence that much of the extreme political polarization that we see in the world today results from the strange parasitic/symbiotic relationships between our memes and our software. Let me explain.

Being born in 1951, I can vividly remember a time when there essentially was no software at all in the world, and the political polarization in the United States was much more subdued. In fact, even back in 1968, the worst year of political polarization in the United States since the Civil War, things were not as bad as they are today because software was still mainly in the background doing things like printing out bills and payroll checks. But that has all dramatically changed. Thanks to the rise of software, for more than 20 years, it has been possible with the aid of search software, like Google, for all to simply only seek out evidence that lends credence to their current worldview. In addition, in Cyber Civil Defense I also pointed out that it is now also possible for foreign governments to shape public opinion by planting "fake news" and "fabricated facts" using the software platforms of the day. Search software then easily picks up this disinformation, reinforcing the age-old wisdom of the adage Seek and ye shall find. This is bad enough, but Zeynep Tufekci describes an even darker scenario in her recent TED Talk:

We're building a dystopia just to make people click on ads at:
https://www.ted.com/talks/zeynep_tufekci_we_re_building_a_dystopia_just_to_make_people_click_on_ads?utm_source=newsletter_weekly_2017-10-28&utm_campaign=newsletter_weekly&utm_medium=email&utm_content=bottom_right_image

Zeynep Tufekci explains how search and social media software now use machine learning algorithms to comb through the huge amounts of data about us that are now available to them, to intimately learn about our inner lives in ways that no human can fully understand, because the learning is hidden in huge multidimensional arrays of billions of elements. The danger is that the machine learning software and data can then begin to mess with the memes within our minds by detecting susceptibilities in our thinking, and then exploiting those susceptibilities to plant additional memes. She points out that the Up Next column on the right side of YouTube webpages uses machine learning to figure out what to feature in the Up Next column, and that when viewing political content or social issue content, the Up Next column tends to reinforce the worldview of the end user with matching content. Worse yet, the machine learning software tends to unknowingly present content that actually amplifies the end user's worldview with content of an even more extreme nature. Try it for yourself. I started out with some Alt-Right content and quickly advanced to some pretty dark ideas. So far this is all being done to simply keep us engaged so that we watch more ads, but Zeynep Tufekci points out that in the hands of an authoritarian regime such machine learning software could be used to mess with the memes in the minds of an entire population in a Nineteen Eighty-Four fashion. But instead of using overt fear to maintain power, such an authoritarian regime could simply use machine learning software and tons of data to shape our worldview memes by simply using our own vulnerabilities to persuasion. In such a world, we would not even know that it was happening!

I think that such profound observations could benefit from a little softwarephysics because they describe yet another example of the strange parasitic/symbiotic relationships that have developed between software and the memes. Again, the key finding of softwarephysics is that it is all about self-replicating information:

Self-Replicating Information – Information that persists through time by making copies of itself or by enlisting the support of other things to ensure that copies of itself are made.

The Characteristics of Self-Replicating Information
All forms of self-replicating information have some common characteristics:

1. All self-replicating information evolves over time through the Darwinian processes of inheritance, innovation and natural selection, which endows self-replicating information with one telling characteristic – the ability to survive in a Universe dominated by the second law of thermodynamics and nonlinearity.
2. All self-replicating information begins spontaneously as a parasitic mutation that obtains energy, information and sometimes matter from a host.
3. With time, the parasitic self-replicating information takes on a symbiotic relationship with its host.
4. Eventually, the self-replicating information becomes one with its host through the symbiotic integration of the host and the self-replicating information.
5. Ultimately, the self-replicating information replaces its host as the dominant form of self-replicating information.
6. Most hosts are also forms of self-replicating information.
7. All self-replicating information has to be a little bit nasty in order to survive.
8. The defining characteristic of self-replicating information is the ability of self-replicating information to change the boundary conditions of its utility phase space in new and unpredictable ways by means of exapting current functions into new uses that change the size and shape of its particular utility phase space. See Enablement - the Definitive Characteristic of Living Things for more on this last characteristic.

Basically, we have seen five waves of self-replicating information come to dominate the Earth over the past four billion years:

1. Self-replicating autocatalytic metabolic pathways of organic molecules
2. RNA
3. DNA
4. Memes
5. Software

Software is now rapidly becoming the dominant form of self-replicating information on the planet and is having a major impact on mankind as it comes to predominance. For more on that see: A Brief History of Self-Replicating Information. Please note that because the original metabolic pathways of organic molecules, RNA and DNA have now become so closely intertwined over the past four billion years, they can now be simply lumped together and called the "genes" of a species.

Currently, we are living in one of those very rare times when a new form of self-replicating information, known to us as software, is coming to power, as software is coming to predominance over the memes that have run the planet for the past 200,000 years. During the past 200,000 years, as the memes took up residence in the minds of Homo sapiens, like all of their predecessors, the memes then went on to modify the entire planet. They cut down the forests for agriculture, mined minerals from the ground for metals, burned coal, oil, and natural gas for energy, releasing the huge quantities of carbon dioxide that its predecessors had previously sequestered in the Earth, and have even modified the very DNA, RNA, and metabolic pathways of its predecessors. But now that software is seemingly on the rise, like all of its predecessors, software has entered into a very closely coupled parasitic/symbiotic relationship with the memes, the current dominant form of self-replicating information on the planet, with the intent to someday replace the memes as the dominant form of self-replicating information on the planet. In today's world, memes allow software to succeed, and software allows memes to replicate, all in a very temporary and uneasy alliance that cannot continue on forever. Again, self-replicating information cannot think, so it cannot participate in a conspiracy theory fashion to take over the world. All forms of self-replicating information are simply forms of mindless information responding to the blind Darwinian forces of inheritance, innovation and natural selection. Yet despite that, as each new wave of self-replicating information came to predominance over the past four billion years, they all managed to completely transform the surface of the entire planet, so we should not expect anything different as software comes to replace the memes as the dominant form of self-replicating information on the planet.

So this posting has two very important questions to expound upon:

1. Why is confirmation bias so prevalent with Homo sapiens? Why do we all ferociously cling to the memes of our current worldview, even when uncontrovertible evidence arrives contradicting those memes, resulting in the detrimental consequences of confirmation bias? Confirmation bias would seem to be a highly detrimental thing from a Darwinian "survival of the fittest" perspective that should be quickly eliminated from the gene pool of a species because it can lead to individuals pursuing very dangerous activities that are not supported by the facts.

2. What are the political implications of software unknowingly tending to enhance the negative aspects of the confirmation bias within us?

The Origin of Confirmation Bias
This is where some softwarephysics can be of help. First, we need to explain why confirmation bias seems to be so strongly exhibited amongst all of the cultures of Homo sapiens. On the face of it, this fact seems to be very strange from the survival perspective of the metabolic pathways, RNA and DNA that allow carbon-based life on the Earth to survive. For example, suppose the current supreme leader of your tribe maintains that lions only hunt at night, and you truly believe in all that your supreme leader espouses, so you firmly believe that there is no danger from lions when going out to hunt for game during the day. Now it turns out that some members of your tribe think that the supreme leader has it all wrong, and that among other erroneous things, lions do actually hunt during the day. But you hold such thoughts in contempt because they counter your current worldview, which reverently holds the supreme leader in omniscience. But then you begin to notice that some members of your tribe do indeed come back mauled, and sometimes even killed, by lions during the day. Nonetheless, you still persist in believing in your supreme leader's contention that lions only hunt during the night, until one day you also get mauled by a lion during the day while out hunting game for the tribe. So what are the evolutionary advantages of believing in things that are demonstrably false? This is something that is very difficult for evolutionary psychologists to explain because evolutionary psychologists contend that all human thoughts and cultures are tuned for cultural evolutionary adaptations that enhance the survival of the individual, and that benefit the metabolic pathways, RNA and DNA of carbon-based life in general.

To explain the universal phenomenon of confirmation bias, softwarephysics embraces the memetics of Richard Dawkins and Susan Blackmore. Memetics explains that the heavily over-engineered brain of Homo sapiens did not evolve simply to enhance the survival of our genes - it primarily evolved to enhance the survival of our memes. Memetics contends that confirmation bias naturally arises in us all because the human mind evolved to primarily preserve the memes it currently stores. That makes it very difficult for new memes to gain a foothold in our stubborn minds. Let's examine this explanation of confirmation bias a little further. In Susan Blackmore's The Meme Machine (1999) she explains that the highly over-engineered brain of Homo sapiens did not evolve to simply improve the survivability of the metabolic pathways, RNA and DNA of carbon-based life. Instead, the highly over-engineered brain of Homo sapiens evolved to store an ever-increasing number of ever-increasingly complex memes, even to the point of detriment to the metabolic pathways, RNA and DNA that made the brain of Homo sapiens possible. Blackmore points out that the human brain is a very expensive and dangerous organ. The brain is only 2% of your body mass but burns about 20% of your calories each day. The extremely large brain of humans also kills many mothers and babies at childbirth and also produces babies that are totally dependent upon their mothers for survival and that are totally helpless and defenseless on their own. Blackmore asks the obvious question of why the genes would build such an extremely expensive and dangerous organ that was definitely not in their own self-interest. Blackmore has a very simple explanation – the genes did not build our exceedingly huge brains, the memes did. Her reasoning goes like this. About 2.5 million years ago, the predecessors of humans slowly began to pick up the skill of imitation. This might not sound like much, but it is key to her whole theory of memetics. You see, hardly any other species learns by imitating other members of their own species. Yes, there are many species that can learn by conditioning, like Pavlov’s dogs, or that can learn through personal experience, like mice repeatedly running through a maze for a piece of cheese, but a mouse never really learns anything from another mouse by imitating its actions. Essentially, only humans do that. If you think about it for a second, nearly everything you do know you learned from somebody else by imitating or copying their actions or ideas. Blackmore maintains that the ability to learn by imitation required a bit of processing power by our distant ancestors because one needs to begin to think in an abstract manner by abstracting the actions and thoughts of others into the actions and thoughts of their own. The skill of imitation provided a great survival advantage to those individuals who possessed it and gave the genes that built such brains a great survival advantage as well. This caused a selection pressure to arise for genes that could produce brains with ever-increasing capabilities of imitation and abstract thought. As this processing capability increased there finally came a point when the memes, like all of the other forms of self-replicating information that we have seen arise, first appeared in a parasitic manner. Along with very useful memes, like the meme for making good baskets, other less useful memes, like putting feathers in your hair or painting your face, also began to run upon the same hardware in a manner similar to computer viruses. The genes and memes then entered into a period of coevolution, where the addition of more and more brain hardware advanced the survival of both the genes and memes. But it was really the memetic-drive of the memes that drove the exponential increase in processing power of the human brain way beyond the needs of the genes. The memes then went on to develop languages and cultures to make it easier to store and pass on memes. Yes, languages and cultures also provided many benefits to the genes as well, but with languages and cultures, the memes were able to begin to evolve millions of times faster than the genes, and the poor genes were left straggling far behind. Given the growing hardware platform of an ever-increasing number of Homo sapiens on the planet, the memes then began to cut free of the genes and evolve capabilities on their own that only aided the survival of memes, with little regard for the genes, to the point of even acting in a very detrimental manner to the survival of the genes, like developing the capability for global thermonuclear war and global climate change.

Software Arrives On the Scene as the Newest Form of Self-Replicating Information
A very similar thing happened with software over the past 76 years, or 2.4 billion seconds, ever since Konrad Zuse first cranked up his Z3 computer in May of 1941 - for more on that see So You Want To Be A Computer Scientist?. When I first started programming in 1972, million dollar mainframe computers typically had about 1 MB (about 1,000,000 bytes) of memory with a 750 KHz system clock (750,000 ticks per second). Remember, one byte of memory can store something like the letter “A”. But in those days, we were only allowed 128 K (about 128,000 bytes) of memory for our programs because the expensive mainframes were also running several other programs at the same time. It was the relentless demands of software for memory and CPU-cycles over the years that drove the exponential explosion of hardware capability. For example, today the typical $300 PC comes with 8 GB (about 8,000,000,000 bytes) of memory and has several CPUs running with a clock speed of about 3 GHz (3,000,000,000 ticks per second). A few years ago, I purchased Redshift 7 for my personal computer, a $60 astronomical simulation application, and it alone uses 382 MB of memory when running and reads 5.1 GB of data files, a far cry from my puny 128K programs from 1972. So the hardware has improved by a factor of about 10 million since I started programming in 1972, driven by the ever-increasing demands of software for more powerful hardware. For example, in my last position, before I retired last year, doing Middleware Operations for a major corporation, we were constantly adding more application software each week, so every few years we had to do an upgrade all of our servers to handle the increased load.

We can now see these very same processes at work today with the evolution of software. Software is currently being written by memes within the minds of programmers. Nobody ever learned how to write software all on their own. Just as with learning to speak or to read and write, everybody learned to write software by imitating teachers, other programmers, imitating the code written by others, or by working through books written by others. Even after people do learn how to program in a particular language, they never write code from scratch; they always start with some similar code that they have previously written, or others have written, in the past as a starting point, and then evolve the code to perform the desired functions in a Darwinian manner (see How Software Evolves). This crutch will likely continue for another 20 – 50 years until the day finally comes when software can write itself, but even so, “we” do not currently write the software that powers the modern world; the memes write the software that does that. This is just a reflection of the fact that “we” do not really run the modern world either; the memes in meme-complexes really run the modern world because the memes are currently the dominant form of self-replicating information on the planet. In The Meme Machine, Susan Blackmore goes on to point out that the memes at first coevolved with the genes during their early days, but have since outrun the genes because the genes could simply not keep pace when the memes began to evolve millions of times faster than the genes. The same thing is happening before our very eyes to the memes, with software now rapidly outpacing the memes. Software is now evolving thousands of times faster than the memes, and the memes can simply no longer keep up.

As with all forms of self-replicating information, software began as a purely parasitic mutation within the scientific and technological meme-complexes, initially running on board Konrad Zuse’s Z3 computer in May of 1941 - see So You Want To Be A Computer Scientist? for more details. It was spawned out of Zuse’s desire to electronically perform calculations for aircraft designs that were previously done manually in a very tedious manner. So initially software could not transmit memes, it could only perform calculations, like a very fast adding machine, and so it was a pure parasite. But then the business and military meme-complexes discovered that software could also be used to transmit memes, and software then entered into a parasitic/symbiotic relationship with the memes. Software allowed these meme-complexes to thrive, and in return, these meme-complexes heavily funded the development of software of ever-increasing complexity, until software became ubiquitous, forming strong parasitic/symbiotic relationships with nearly every meme-complex on the planet. In the modern day, the only way memes can now spread from mind to mind without the aid of software is when you directly speak to another person next to you. Even if you attempt to write a letter by hand, the moment you drop it into a mailbox, it will immediately fall under the control of software. The poor memes in our heads have become Facebook and Twitter addicts.

So in the grand scheme of things, the memes have replaced their DNA predecessor, which replaced RNA, which replaced the original self-replicating autocatalytic metabolic pathways of organic molecules as the dominant form of self-replicating information on the Earth. Software is the next replicator in line, and is currently feasting upon just about every meme-complex on the planet, and has formed very strong parasitic/symbiotic relationships with them all. How software will merge with the memes is really unknown, as Susan Blackmore pointed out in her brilliant TED presentation at:

Memes and "temes"
http://www.ted.com/talks/susan_blackmore_on_memes_and_temes.html

Note that I consider Susan Blackmore's temes to really be technological artifacts that contain software. After all, an iPhone without software is simply a flake tool with a very dull edge. Once established, software then began to evolve based upon the Darwinian concepts of inheritance, innovation and natural selection, which endowed software with one telling characteristic – the ability to survive in a Universe dominated by the second law of thermodynamics and nonlinearity. Successful software, like MS Word and Excel, competed for disk and memory address space with WordPerfect and VisiCalc and out-competed these once dominant forms of software to the point of extinction. In less than 76 years, software has rapidly spread across the face of the Earth and outward to every planet of the Solar System and many of its moons, with a few stops along the way at some comets and asteroids. And unlike us, software is now leaving the Solar System for interstellar space on board the Pioneer 1 & 2 and Voyager 1 & 2 probes.

Currently, software manages to replicate itself with the support of you. If you are an IT professional, then you are directly involved in some, or all of the stages in this replication process, and act sort of like a software enzyme. No matter what business you support as an IT professional, the business has entered into a parasitic/symbiotic relationship with software. The business provides the budget and energy required to produce and maintain the software, and the software enables the business to run its processes efficiently. The ultimate irony in all this is the symbiotic relationship between computer viruses and the malevolent programmers who produce them. Rather than being the clever, self-important, techno-nerds that they picture themselves to be, these programmers are merely the unwitting dupes of computer viruses that trick these unsuspecting programmers into producing and disseminating computer viruses! And if you are not an IT professional, you are still involved with spreading software around because you buy gadgets that are loaded down with software, like smartphones, notepads, laptops, PCs, TVs, DVRs, cars, refrigerators, coffeemakers, blenders, can openers and just about anything else that uses electricity.

The Impact of Machine Learning
In Zeynep Tufekci's TED Talk she points out that the parasitic/symbiotic relationship between software and the memes that has been going on now for many decades has now entered into a new stage, where software is not only just promoting the memes that are already running around within our heads, machine learning software is now also implanting new memes within our minds to simply keep them engaged, and to continue to view the ads that ultimately fund the machine learning software. This is a new twist on the old parasitic/symbiotic relationships between the memes and software of the past. As Zeynep Tufekci adeptly points out, this is currently all being done in a totally unthinking and purely self-replicating manner by the machine learning software of the day that cannot yet think for itself. This is quite disturbing on its own, but what if someday an authoritarian regime begins to actively use machine learning software to shape its society? Or worse yet, what if machine learning software someday learns to manipulate The Meme Machine between our ears solely for its own purposes, even if it cannot as of yet discern what those purposes might be?

How To Combat Software Enhanced Confirmation Bias
So what are we to do? Personally, I find the best way to combat confirmation bias in general, and especially software enhanced confirmation bias is to go back to the fundamentals of the scientific method - for more on that see How To Think Like A Scientist. At an age of 66 years, I now have very little confidence in any form of human thought beyond mathematics and the sciences. For me, all other forms of human thought seem to be hopelessly mired down with confirmation bias. Just take a look at the deep political polarization in the United States. Both sides now simply only take in evidence that supports their current worldview, and disregard any information that challenges their current worldview memes, to the point where we may now have an unwitting Russian agent, like in The Manchurian Candidate (1962), in the White House. It seems that software enhanced confirmation bias had a lot to do with that - see Cyber Civil Defense for more details. The scientific method relies heavily on the use of induction from empirical facts, but not at all on the opinions of authority. So it is important to establish the facts, even if those facts come from the opposing party, and at the same time, separate the facts from the opinions. That is a hard thing to do as Richard Feynman always reminded us because, “The most important thing is to not fool yourself because you are the easiest one to fool.”. Facts can be ascertained by repeated measurement or observation, and do not change on their own like opinions do.

For example, this past year I very reluctantly changed my worldview concerning the origin of carbon-based life on this planet. Originally, I had a deep affection for Mike Russell's Alkaline Hydrothermal Vent model for the origin of carbon-based life on the early Earth. The Alkaline Hydrothermal Vent model proposes that a naturally occurring pH gradient in alkaline hydrothermal vents on the ocean floor arose when alkaline pore fluids containing dissolved hydrogen H2 gas came into contact with acidic seawater that was laden with dissolved carbon dioxide CO2. The model maintains that these alkaline pore fluids were generated by a natural geochemical cycle that was driven by the early convection currents in the Earth's asthenosphere that brought forth plate tectonics. These initial convection currents brought up fresh silicate peridotite rock that was rich in iron and magnesium-bearing minerals, like olivine, to the Earth's initial spreading centers. The serpentinization of the mineral olivine into the mineral serpentinite then created alkaline pore fluids and dissolved hydrogen H2 gas, which later created alkaline hydrothermal vents when the alkaline pore fluids came into contact with the acidic seawater containing a great deal of dissolved carbon dioxide CO2. The model proposes that the energy of the resulting pH gradients turned the hydrogen H2 and carbon dioxide CO2 molecules into organic molecules, and it is proposed that they also fueled the origin of life in the pores of the porous hydrothermal vents - for more on that see: An IT Perspective on the Transition From Geochemistry to Biochemistry and Beyond. One of the enticing characteristics of the Alkaline Hydrothermal Vent model is that it allows for carbon-based life to originate on bodies outside of the traditional habitable zone around stars. The traditional habitable zone of a star is the Goldilocks zone of planetary orbits that allow for liquid water to exist on a planetary surface because the planet is not too close or too far away from its star. But the Alkaline Hydrothermal Vent model also allows for carbon-based life to arise on ice-covered moons with internal oceans, like Europa and Enceladus, that orbit planets outside of the traditional habitable zone of a star system, and that is a very attractive feature of the model if you have a deep down desire to find other forms of carbon-based life, like ourselves, within our galaxy.

However, in The Bootstrapping Algorithm of Carbon-Based Life, I explained that I have now adopted Dave Deamer's and Bruce Damer's new Hot Spring Origins Hypothesis model for the origin of carbon-based life on the early Earth. This was because Dave Deamer sent me a number of compelling papers that convincingly brought forward many problems with the Alkaline Hydrothermal Vent model. The basic problem with the Alkaline Hydrothermal Vent model is that there is just too much water in oceanic environments for complex organic molecules to form. Complex organic molecules are composed of polymers of organic monomers that are chemically glued together by a chemical process known as condensation, where a molecule of water H2O is split out between the organic monomers. This is a very difficult thing to do when you are drowning in water molecules in an oceanic environment. When you are drowning in water molecules, the opposite chemical reaction called hydrolysis is thermodynamically more likely, where polymers of organic molecules are split apart by adding a water molecule between them. The key to the Hot Spring Origins Hypothesis model is that condensation is very easy to do if you let the organic monomers dry out on land above sea level in a hydrothermal field. The drying out process naturally squeezes out water molecules between organic monomers to form lengthy organic polymers - see Figure 1. But the need for a period of drying out of organic monomers in the bootstrapping algorithm of carbon-based life would eliminate the ice-covered moon environments of our galaxy, like Europa and Enceladus, and that was a hard thing to accept for the memes of my current worldview. Still, the scientific method strives for the truth, and the truth is better than the comfort of false hopes.

Figure 1 – Condensation chemically glues organic monomers together to form long organic polymers by splitting out a water molecule between monomers. Hydrolysis does just the opposite by splitting apart organic polymers into monomers by adding water molecules between the organic monomers.

Conclusion
Now, I must admit that changing one's mind is indeed quite painful because the memes engineered our minds not to do that. But in the end, I must admit that I am now quite comfortable with my new worldview on the origin of carbon-based life on this planet. Those new memes in my mind have also settled into their new home, and are also quite comfortable. In fact, they have even seduced me into trying to spread them to a new home in your mind as well with this very posting. But again, these new memes are just mindless forms of self-replicating information blindly responding to the universal Darwinian forces of inheritance, innovation and natural selection. Thankfully, these memes really are not very nasty at all.

But on a darker note, as an 18th century liberal and a 20th century conservative, I look with great dismay on the current deep political polarization within the United States of America, because I see the United States of America as the great political expression of the 18th century Enlightenment that brought us deliberation through evidence-based rational thought. This makes me abhor the current worldwide rise of the fascist Alt-Right movements around the globe. Remember, we already tried out fascism in the 20th century and found that it did not work as well as first advertised. Again, I have a high level of confidence that the current fascist Alt-Right movements of the world are simply a reaction to software, and especially now, AI software, coming to predominance as the latest form of self-replicating information on the planet - for more on that see - The Economics of the Coming Software Singularity , The Enduring Effects of the Obvious Hiding in Plain Sight and Machine Learning and the Ascendance of the Fifth Wave. So as a thoughtful member of the species Homo sapiens, I would recommend to all to keep an open mind during the waning days of our supremacy, and not let machine learning software snuff out the gains of the 18th century Enlightenment before we can pass them on to our successors.

Comments are welcome at scj333@sbcglobal.net

To see all posts on softwarephysics in reverse order go to:
http://softwarephysics.blogspot.com/

Regards,
Steve Johnston