Currently, there are a number of diverse collaborations throughout the world exploring the origin of life on Earth and elsewhere. These teams are normally composed of members from the hard sciences, such as astronomers, astrophysicists, biologists, biochemists, chemists, geologists, geochemists, geophysicists, and physicists. I would like to propose that these diverse teams also take on a few additional participants from the Computer Science departments of their home universities and also a number of IT professionals from the IT departments of several major corporations throughout the world. The purpose of this strange collaboration would be to use the origin and evolution of commercial software over the past 70 years as a model for the origin and evolution of life on Earth and elsewhere. My hope would be that amongst such a diverse team something would click – somebody from outside IT might see something in what the IT community has painstakingly built over the past 70 years that rings a bell in their domain of experience. I think this effort could be conducted with very little cost using remote WebEx conferences, instant messaging, and email over the Internet. I would be very much interested in participating in such a collaboration as a junior member, with the intention of recruiting additional IT professionals from the pool of computer science graduates from the participating universities who happen to have some interest in bioinformatics or biologically inspired computing, and who have also moved into the world of corporate IT. Let me explain.
Currently, I am in the Middleware Operations group of the IT department of a major US corporation and support all of its externally facing websites and also all of the internal applications used to run the business. I graduated from the University of Illinois in 1973 with a B.S. in Physics and from the University of Wisconsin in 1975 with an M.S. in Geophysics, and from 1975 – 1979 I was an exploration geophysicist exploring for oil, first with Shell, and then with Amoco. I started programming in 1972, and in 1979 I decided to make a career change and become an IT professional in Amoco’s IT department. When I first transitioned into IT from geophysics, I figured 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.
I will soon be turning 62 years old and heading into the homestretch, so a few years back I started this blog on softwarephysics to share what I had discovered over the years with the rest of the IT community. My initial intention for my blog on softwarephysics was to help 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 had so graciously provided to the scientific community free of charge, and also of the very significant potential scientific value that it provided. 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.
Currently, there are many researchers working on the origin of life on Earth and elsewhere, but the problem is that on Earth we are missing the rocks from the very first billion years of the Earth’s history when life first arose, so workers in the field are left to draw historical inferences in deep time based upon the modern metabolic pathways, RNA, and DNA we still have at hand today, and also upon biochemical simulations in the lab that are based upon those inferences. And even if we do find life on other planets, we will most likely be faced with the same challenge of not being able to figure out how it all happened.
My suggestion would be that everybody is looking just a couple of levels too low in the hierarchy of self-replicating information. Living things are just one form of self-replicating information, and all forms of self-replicating information have many characteristics in common as they battle the second law of thermodynamics in a nonlinear Universe. Currently, there are three forms of self-replicating information on the Earth – the genes, memes, and software, with software rapidly becoming the dominant form of self-replicating information on the planet. However, of the three, the only form of self-replicating information that we have a good history of is software, going all the way back to May of 1941 when Konrad Zuse cranked up his Z3 computer for the very first time. So the best model for the origin of life might be obtained by studying the hodge-podge of precursors, false starts, and failed attempts that led to the origin and early evolution of software, with particular attention paid to the parasitic/symbiotic relationships that allowed software to bootstrap itself into existence.
Yes, there are many other examples of universal Darwinism at work in the Universe, such as the evolution of languages or political movements, but I think that the origin and evolution of software provides a unique example because both programmers and living things are faced with nearly identical problems. A programmer must assemble a huge number of characters into complex patterns of source code to instruct a computer to perform useful operations. Similarly, living things must assemble an even larger number of atoms into complex molecules in order to perform the functions of life. And because the Universe is largely nonlinear in nature, meaning that small changes to initial conditions will most likely result in dramatic, and many times, lethal outcomes for both software and living things, the evolutionary history of living things on Earth and of software have both converged upon very similar solutions to overcome the effects of the second law of thermodynamics in a nonlinear Universe. For example, both living things and software went through a very lengthy prokaryotic architectural period, with little internal structure, to be followed by a eukaryotic architectural period with a great deal of internal structure, which later laid the foundations for forms with a complex multicellular architecture. And both also experienced a dramatic Cambrian explosion in which large multicellular systems arose consisting of huge numbers of somatic cells that relied upon the services of large numbers of cells to be found within a number of discrete organs.
Also, software presents a much clearer distinction between the genotype and phenotype of a system than do other complex systems, like languages or other technologies that also undergo evolutionary processes. The genotype of software is determined by the source code files of programs, while the phenotype of software is expressed by the compiled executable files that run upon a computer and that are generated from the source code files by a transcription process similar to the way genes are transcribed into proteins. Also, like a DNA or RNA sequence, source code provides a very tangible form of self-replicating information that can be studied over historical time without ambiguity. Source code is also not unique, in that many different programs, and even programs written in different languages can produce executable files with identical phenotypes or behaviors.
Currently, many researchers working on the origin of life and astrobiology are trying to produce computer simulations to help investigate how life could have originated and evolved at its earliest stages. But trying to incorporate all of the relevant elements into a computer simulation is proving to be a very daunting task indeed. Why not simply take advantage of the naturally occurring $10 trillion computer simulation that the IT community has already patiently evolved over the past 70 years and has already run for 2.2 billion seconds? It has been hiding there in plain sight the whole time for anybody with a little bit of daring and flair to explore.
Some might argue that this is an absurd proposal because software currently is a product of the human mind, while biological life is not a product of intelligent design. Granted, biological life is not a product of intelligent design, but neither is the human mind. The human mind and biological life are both the result of natural processes at work over very long periods of time. This objection simply stems from the fact that we are all still, for the most part, self-deluded Cartesian dualists at heart, with seemingly a little “me” running around within our heads that just happens to have the ability to write software and to do other challenging things. But since the human mind is a product of natural processes in action, so is the software that it produces. See:
The Ghost in the Machine the Grand Illusion of Consciousness
Still, I realize that there might be some hesitation to participate in this collaboration because it might be construed by some as an advocacy of intelligent design, but that is hardly the case. The evolution of software over the past 70 years has essentially been a matter of Darwinian inheritance, innovation and natural selection converging upon similar solutions to that of biological life. For example, it took the IT community about 60 years of trial and error to finally stumble upon an architecture similar to that of complex multicellular life that we call SOA – Service Oriented Architecture. The IT community could have easily discovered SOA back in the 1960s if it had adopted a biological approach to software and intelligently designed software architecture to match that of the biosphere. Instead, the worldwide IT architecture we see today essentially evolved on its own because nobody really sat back and designed this very complex worldwide software architecture; it just sort of evolved on its own through small incremental changes brought on by many millions of independently acting programmers through a process of trial and error. When programmers write code, they always take some old existing code first and then modify it slightly by making a few changes. Then they add a few additional new lines of code and test the modified code to see how far they have come. Usually, the code does not work on the first attempt because of the second law of thermodynamics, so they then try to fix the code and try again. This happens over and over until the programmer finally has a good snippet of new code. Thus, new code comes into existence through the Darwinian mechanisms of inheritance coupled with innovation and natural selection. Some might object that this coding process of software is actually a form of intelligent design, but that is not the case. It is important to differentiate between intelligent selection and intelligent design. In softwarephysics we extend the concept of natural selection to include all selection processes that are not supernatural in nature, so for me, intelligent selection is just another form of natural selection. This is really nothing new. Predators and prey constantly make “intelligent” decisions about what to pursue and what to evade, even if those “intelligent” decisions are only made with the benefit of a few interconnected neurons or molecules. So in this view, the selection decisions that a programmer makes after each iteration of working on some new code really are a form of natural selection. After all, programmers are just DNA survival machines with minds infected with memes for writing software, and the selection processes that the human mind undergo while writing software are just as natural as the Sun drying out worms on a sidewalk or a cheetah deciding upon which gazelle in a herd to pursue.
For example, when IT professionals slowly evolved our current $10 trillion worldwide IT architecture over the past 2.2 billion seconds, they certainly did not do so with the teleological intent of creating a simulation of the evolution of the biosphere. Instead, like most organisms in the biosphere, these IT professionals were simply trying to survive just one more day in the frantic world of corporate IT. It is hard to convey the daily mayhem and turmoil of corporate IT to outsiders. In 1979, I had been working continuously on geophysical models and simulations in Fortran and Basic for my thesis and for oil companies, ever since taking CS 101 at the University of Illinois back in 1972, but when I made a career change from being an exploration geophysicist at Amoco to become a systems analyst in Amoco’s IT department in 1979, I was in complete shock. When I first hit the floor of Amoco’s IT department on one very scary Monday morning, I suddenly found myself surrounded by countless teams of IT professionals, all running around like the Mad Hatter in Alice in Wonderland. After a couple of terrorizing weeks on this new job, it seemed to me like I was trapped in a frantic computer simulation, like the ones that I had been programming for the past seven years, hopelessly buried in punch card decks and fan-fold listings. But I quickly realized that all IT jobs essentially boiled down to simply pushing buttons. All you had to do was to push the right buttons, in the right sequence, at the right time, and with zero errors. How hard could that be? Well, it turned out to be very difficult indeed, and in response I began to subconsciously work on softwarephysics to try to figure out why this job was so hard, and how I could dig myself out of the mess that I had gotten myself into. After a while, it dawned on me that the fundamental problem was the second law of thermodynamics operating in a nonlinear simulated universe. The second law made it very difficult to push the right buttons in the right sequence and at the right time because there were so many erroneous combinations of button pushes. Writing and maintaining software was like looking for a needle in a huge utility phase space. There just were nearly an infinite number of ways of pushing the buttons “wrong”. The other problem was that we were working in a very nonlinear utility phase space, meaning that pushing just one button incorrectly usually brought everything crashing down. Next, I slowly began to think of pushing the correct buttons in the correct sequence as stringing together the correct atoms into the correct sequence to make molecules in chemical reactions that could do things. I also knew that living things were really great at doing that. Living things apparently overcame the second law of thermodynamics by dumping entropy into heat as they built low entropy complex molecules from high entropy simple molecules and atoms. I then began to think of each line of code that I wrote as a step in a biochemical pathway. The variables were like organic molecules composed of characters or “atoms” and the operators were like chemical reactions between the molecules in the line of code. The logic in several lines of code was the same thing as the logic found in several steps of a biochemical pathway, and a complete function was the equivalent of a full-fledged biochemical pathway in itself. But one nagging question remained - how could I take advantage of these similarities to save myself? That’s a long story, but in 1985 I started working on BSDE– the Bionic Systems Development Environment, which was used at Amoco to “grow” software biologically from an “embryo” by having programmers turn on and off a set of “genes”. The second half of my original softwarephysics posting provides more information on BSDE:
SoftwarePhysics
To have some fun with softwarephysics, and to see how it might help with exploring the origin of life, please take a look at the postings down below. To get to any other posting, just use the Blog Archive links in the upper right-hand corner of each posting.
A Brief History of Self-Replicating Information
The Driving Forces of Software Evolution
A Proposal For All Practicing Paleontologists
How Software Evolves
Self-Replicating Information
SoftwareBiology
Skip down to the section on SoftwarePaleontology
The Origin of Software the Origin of Life
Programming Clay
Using the Evolution of Software as a Model for Astrobiologists
An IT Perspective of the Cambrian Explosion
Using the Origin of Software as a Model for the Origin of Life
An IT Perspective on the Origin of Chromatin, Chromosomes and Cancer
Software Embryogenesis
Introduction to Softwarephysics
Some Pertinent Observations Already Gleaned From the History of IT
1. It’s all about self-replicating information.
Living things are just one form of self-replicating information that happen to carry along their own hardware with them. The concept of living things is really just an artificial human classification, and that is why it is nearly impossible for us to define. Perhaps, in some sense, the vitalists had it right all along; the mysterious vital force they sought was simply the driving force of self-replicating information seeking to survive. So the definition of life might really just hinge upon an arbitrary level of information density. A quartz crystal forming in a melt, or an ice crystal forming from many mobile water molecules plugging into a lattice, are also primitive forms of self-replicating information, just with a lower information density and a higher entropy than we are accustomed to finding in living things, but it really is just a matter of degree. So do not think in terms of investigating the origin of life, rather, think in terms of investigating the early history of self-replicating information on the Earth. After all, in the end, everything is just made of dead atoms. See:
The Demon of Software
2. Self-replicating information is very opportunistic.
Self-replicating information is very opportunistic and will exapt whatever hardware happens to be available at the time, and will certainly jump ship if something better comes along. For example, software started out on electrical relays and then proceeded to vacuum tubes, discrete transistors, integrated circuits with thousands of transistors, integrated circuits with millions of transistors, integrated circuits with billions of transistors, and will probably jump ship again to optical chips in the next decade or so. Now 4 billion years ago, the only available hardware on the newly formed Earth was organic monomers brought to the Earth by comets and asteroids or generated here by abiotic processes, and rock-forming minerals. All rocks in the Earth’s crust are formed from rock-forming silicate minerals that are composed of polymerized silica tetrahedrons. The silica tetrahedrons are made from one atom of silicon and four atoms of oxygen and are very similar in structure to methane. The silica tetrahedrons have a net charge of -4 so they polymerize into single chains, double chains, sheets, or 3-dimensional frameworks with positive cations of Ca++, Na+, K+, Fe++, or Mg++ interspersed in the crystalline lattice to neutralize the negative charge. So in a sense, they are the silicon equivalent of organic molecules. The rock-forming minerals are essentially the structural proteins that hold rocks together, and their 3-dimensional structures are key to rock properties, just as the structure of alpha chains and beta sheets are key to the properties of proteins. The minerals in rocks are usually formed at very high temperatures and usually under very high pressures too, so they are very much out of thermodynamic equilibrium at the Earth’s surface or at the bottom of the sea. Granted, there may be hundreds of kinds of rocks, but no matter the rock, they all eventually chemically weather down into mud and sand. The mud is formed from sheet-like polymers of silica tetrahedrons called clay minerals, and the sand comes from quartz grains which are made from very tough frameworks of pure silica tetrahedrons. It’s the H+ ions, mainly from carbonic and other acids, that break down the minerals by working themselves into the crystalline lattices to replace the positive cations. Since the silicates are very much like organic molecules, the odds are that our very distant ancestors were some kind of hybrid of the two. Also, life probably first arose several hundred meters below the Earth’s surface in the pore fluids circulating through rocks near the first spreading centers when plate tectonics was first initiated. These environments would have been safe from the late heavy bombardment 3.8 – 4.1 billion years ago. So make friends with a good geochemist. See:
Programming Clay
3. Self-replicating information easily forms parasitic/symbiotic relationships.
Lynn Margulis’s endosymbiotic theory seems to be universal for all forms of self-replicating information. The genes, memes, and software on Earth are all currently deeply intertwined in very complex parasitic/symbiotic relationships amongst them all. The Earth could certainly not support a population of 7 billion people without them all competing and also working together. Similarly, I vividly remember the early 1990s, when it was predicted that LANs composed of “high-speed” Intel 386 PCs running at a whopping 33 MHz would make IBM’s mainframes obsolete, and indeed, IBM nearly did go bankrupt in those days. However, today we find that IBM mainframes running z/OS, Unix servers, client PCs running Windows or Mac, and smartphones have all formed a heavily interdependent hybridized parasitic/symbiotic relationship, and so has the software running upon them.
4. Convergence plays a major role in the evolution of self-replicating information.
As Daniel Dennett put it, there are only a certain number of “Good Tricks” for living things to discover, and the same goes for all forms of self-replicating information. Over the past 70 years, software architecture has very closely recapitulated the same path through Design Space that living things did billions of years ago on Earth. Software went through a lengthy period of prokaryotic organization, which was followed by a period of eukaryotic organization, which finally led to the foundations of multicellular organization. And over the past decade, software has seen a Cambrian explosion, in which large numbers of somatic objects use the services of large numbers of service objects in service organs. See the SoftwarePaleontology section of:
SoftwareBiology
Similarly, in
Crocheting Software
we see that crochet and knitting patterns are precursors to computer software that evolved in parallel branches to software, but were never really in the same line of descent. I imagine on the early Earth there were similar branches of self-replicating information that were never our distant ancestors, but might have been unrelated competitors at the time. They might even still exist today in some isolated environments.
5. Beware of the memes lurking within your mind.
Meme-complexes are very conservative in nature and have a very great reluctance to adopting new memes from outside that might threaten the very existence of the entire meme-complex. This is especially true of scientific meme-complexes, and rightly so. Scientific meme-complexes must always be on guard to prevent the latest crackpot idea from taking hold. But if you look to the history of science, the downside to all this is that nearly all of the great scientific breakthroughs were delayed by 10 – 50 years, patiently awaiting acceptance by a scientific meme-complex. That is why Thomas Kuhn found that scientific meme-complexes were so very reluctant to adopt paradigm shifts. The rigidity of scientific meme-complexes today holds back scientific progress because it prevents people from working together on difficult problems, like the origin of life.
Some very good books on the subject are:
The Meme Machine (1999) by Susan Blackmore,
Virus of the Mind: The New Science of the Meme (1996) by Richard Brodie
Also, see:
How to Use Softwarephysics to Revive Memetics in Academia
6. Adopt a positivistic approach using effective theories.
Softwarephysics adopts a very positivistic view of software in that we do not care about what software “really” is; we only care about how software is observed to behave, and we only attempt to model this behavior with a set of effective theories. Positivism is an enhanced form of empiricism, in which we do not care about how things “really” are; we are only interested in how things are observed to behave. With positivism, physicists only seek out models of reality - not reality itself. Effective theories are 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 works very well for objects moving in weak gravitational fields at less than 10% of the speed of light and which are larger than a very small mote of dust. For things moving at high velocities or in strong gravitational fields we must use relativity theory, and for very small things like atoms, we must use quantum mechanics. All of the current theories of physics, such as Newtonian mechanics, classical electrodynamics, thermodynamics, statistical mechanics, the special and general theories of relativity, quantum mechanics, and quantum field theories like QED (quantum electrodynamics) are just 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, and that is all positivism hopes to achieve. Remember, all of chemistry is just an approximation of QED, and QED is also just an approximate effective theory that explains nearly all of the behaviors of electrons but cannot explain the gravitational attraction between electrons. When all is said and done, biology and chemistry are all just about electrons in various quantum states, and electrons are nearly massless particles made of who knows what. See:
Model-Dependent Realism - A Positivistic Approach to Realism
Comments are welcome at scj333@sbcglobal.net
To see all posts on softwarephysics in reverse order go to:
https://softwarephysics.blogspot.com/
Regards,
Steve Johnston
Sunday, May 26, 2013
A Proposal for an Odd Collaboration to Explore the Origin of Life with IT Professionals
Wednesday, May 15, 2013
400 PPM – The Dawn of the SophomorEocene
This will be a short posting. A few weeks ago we left the Pleistocene and entered into a new geological Epoch that has yet to be named. Since in science and all of academia, priority is of the utmost importance, and scientists and academicians, in general, have been known to fight intellectual duals to the death over who gets credit for coming up with things first, I would like to plant my flag first and call this new geological Epoch the SophomorEocene after the Greek for “New Wise Fool”. You see, for the past 2.5 million years we have been in the Pleistocene Epoch, which was characterized by about a dozen or so Ice Ages, where vast ice sheets descended down from the poles to the mid-latitudes. Well, a few weeks ago we finally crossed over to a level of 400 PPM of carbon dioxide in the atmosphere for the first time in 2.5 million years, so those Ice Age days are now long gone. What happened is that the Earth has been cooling for the past 40 million years because plate tectonics created so many mountains, like the Himalayas, and erosion of those mountains sucked lots of carbon dioxide out of the atmosphere down to a level of about 280 PPM (see How to Use Your IT Skills to Save the World for more details.) Then when the carbon dioxide levels dropped to about 280 PPM, the Milankovitch cycles were able to begin to initiate a series of a dozen or so Ice Ages. The Milankovitch cycles are caused by periodic minor changes in the Earth’s orbit and inclination that lead to periodic coolings and warmings. In general, the Earth’s temperature drops by about 150 Fahrenheit for about 100,000 years and then increases by 150 Fahrenheit for about 10,000 years. During the cooling period, we have an Ice Age because the snow in the far north does not fully melt during the summer and builds up into huge ice sheets that push down to the lower latitudes. Carbon dioxide levels also drop to about 180 ppm during an Ice Age because organic carbon compounds get trapped into ice-covered sediments containing organic material that does not get a chance to oxidize into carbon dioxide, which further keeps the planet in a deep freeze. Then during the 10,000 year warming periods, we have an interglacial period, like the current Holocene interglacial that we now find ourselves in, and the carbon dioxide levels rise again to about 280 ppm.
Now before geologists had fully figured this all out, for some reason they mistakenly thought that the last Ice Age was really the last Ice Age, and so they called the last 11,000 year period the Holocene Epoch, figuring that all of the Ice Ages were finally over. That was really a mistake. True, the last 11,000 years encompasses all of human history, probably because we were not in an Ice Age, but it really was just an interglacial period in the Pleistocene. So the Holocene really was just a typo in geological thought, and up until a few weeks ago, we really still were in the Pleistocene Epoch the whole time. But then we crossed over 400 PPM of carbon dioxide a few weeks back for the first time in about 2.5 million years and entered into the new SophomorEocene Epoch.
Figure 1 – The geological time scale (click to enlarge)
Figure 2 – Carbon dioxide levels during the Pleistocene varied between 180 and 280 PPM. When carbon dioxide levels were low we had an Ice Age and when they were high we had an interglacial period, like today’s climate.
Figure 3 – The level of carbon dioxide has been rising in the atmosphere over the past 150 years because we burned downed the forests and burned coal, oil, and natural gas at terrific rates. Something like this has never happened before in the Earth’s 4.567 billion year history.
Figure 4 – Carbon dioxide levels at Mauna Loa just broke 400 PPM.
The SophomorEocene Epoch will be very much like the earlier Eocene Epoch which lasted from 56 to 33.9 million years ago. The Eocene started off with a massive increase in the level of carbon dioxide in the atmosphere to over 500 PPM and a warming of the entire planet. All of the ice on the planet melted, so sea level rose to about 300 feet higher than it is today, and creatures in southern Illinois frolicked upon the new seacoast, while alligators in the Arctic Circle swam in tropical swamps. The very expensive real estate of the American East Coast was at bargain prices too because it was several hundred feet under water. Finally, the end of the Eocene was marked by a significant drop in carbon dioxide levels and a cooling of the planet, which returned to an icehouse climate, with ice reappearing at the poles and a rapidly growing Antarctic ice sheet.
So the SophomorEocene will be very much like the Eocene. As we burn up all of the remaining fossil fuels, carbon dioxide levels will continue to rise, causing huge expenses that civilization will have a hard time absorbing. Finally, when all of the fossil fuels are gone, the carbon dioxide levels will begin to drop due to natural processes, and we will once again return to Pleistocene-like conditions, with polar ice caps and a climate that we are used to. However, this cycle will probably take several hundred thousand years to complete. For more details, please see:
http://en.wikipedia.org/wiki/Eocene
http://www.geo.wvu.edu/~kammer/g231/PETM.pdf
So What Can You Do?
Please read How to Use Your IT Skills to Save the World.
As an 18th-century liberal and 20th-century conservative, I would strongly encourage all 21st-century conservatives not to bet against science and become wise fools. You see, when it comes to climate change - saving money sure gets expensive.
Figure 5 – Congratulations, with lots of hard work, we all finally made it happen! The Logo for the SophomorEocene.
Comments are welcome at scj333@sbcglobal.net
To see all posts on softwarephysics in reverse order go to:
https://softwarephysics.blogspot.com/
Regards,
Steve Johnston
Saturday, March 23, 2013
The Driving Forces of Software Evolution
In 1979 I made a career change from being an exploration geophysicist exploring for oil with Amoco to become an IT professional in Amoco’s IT department. At the time, I figured if you could apply physics to geology, why not apply physics to software? That is when I first started working on softwarephysics to help me cope with the daily mayhem of life in IT. Since I had only taken one computer science class in college back in 1972, I was really starting from scratch in this new career, but since I had been continuously programming geophysical models for my thesis and for oil companies during that period, I did have about seven years of programming experience as a start. When I made this career change into IT, I quickly realized that all IT jobs essentially boiled down to simply pushing buttons. All you had to do was push the right buttons, in the right sequence, at the right time, and with nearly zero defects. How hard could that be? Well, as we all know that is indeed a very difficult thing to do.
When I first started programming in 1972, as a physics major at the University of Illinois in Urbana, I was pushing the buttons on an IBM 029 keypunch machine to feed cards into a million dollar mainframe computer with a single CPU running with a clock speed of about 750 KHz and about 1 MB of memory.
Figure 1 - An IBM 029 keypunch machine like the one I first learned to program on at the University of Illinois in 1972.
Figure 2 - Each card could hold a maximum of 80 bytes. Normally, one line of code or one 80 byte data record was punched onto each card.
Figure 3 - The cards for a program were held together into a deck with a rubber band, or for very large programs, the deck was held in a special cardboard box that originally housed blank cards. Many times the data cards for a run followed the cards containing the source code for a program. The program was compiled and linked in two steps of the run and then the generated executable file processed the data cards that followed in the deck.
Figure 4 - To run a job, the cards in a deck were fed into a card reader, as shown on the left above, to be compiled, linked, and executed by a million dollar mainframe computer with a clock speed of about 750 KHz and about 1 MB of memory.
Now I push these very same buttons for a living on a $500 laptop with 2 CPUs running with a clock speed of 2.5 GHz and 8 GB of memory. So hardware has improved by a factor of over 10 million since 1972.
Figure 5 - Now I push these very same buttons for a living that I pushed on IBM 029 keypunch machines, the only difference is that I now push them on a $500 machine with 2 CPUs running with a clock speed of 2.5 GHz and 8 GB of memory.
Now how much progress have we seen in our ability to develop, maintain and support software over this very same period of time? I would estimate that our ability to develop, maintain and support software has only increased by a factor of about 10 – 100 times since 1972, and I think that I am being very generous here. In truth, it is probably much closer to being a factor of 10, rather than to being a factor of 100. Here is a simple thought experiment. Imagine assigning two very good programmers the same task of developing some software to automate a very simple business function. Imagine that one programmer is a 1972 COBOL programmer using an IBM 029 keypunch machine, while the other programmer is a 2013 Java programmer using the latest Java IDE (Integrated Development Environment software) to write and debug a Java program. Now set them both to work. Hopefully, the Java programmer would win the race, but by how much? I think that the 2013 Java programmer, armed with all of the latest tools of IT, would be quite pleased if she could finish the project 10 times faster than the 1972 programmer cutting cards on an IBM 029 keypunch machine. This would indeed be a very revealing finding. Why has the advancement of hardware outpaced the advancement of software by nearly a factor of a million since 1972? How can we possibly account for this vast difference between the advancement of hardware and the advancement of software over a span of 40 years that is on the order of 6 orders of magnitude? Clearly, two very different types of processes must be at work to account for such a dramatic disparity.
My suggestion would be that hardware advanced so quickly because it was designed using science, while the advancement of software was left to evolve more or less on its own. You see, nobody really sat back and designed the very complex worldwide software architecture that we see today, it just sort of evolved on its own through small incremental changes brought on by many millions of independently acting programmers, through a process of trial and error. In this view, software itself can be thought of as a form of self-replicating information, trying to survive by replicating before it disappears into extinction, and evolving over time on its own. Software is certainly not alone in this regard. There currently are three forms of self-replicating information on the planet – the genes, memes, and software, with software rapidly becoming the dominant form of self-replicating information on the Earth. (see A Brief History of Self-Replicating Information for more details).
The evolution of all three forms of self-replicating information seems to be primarily driven by two factors - the second law of thermodynamics and nonlinearity. Before diving into the second law of thermodynamics again, let us first review the first law of thermodynamics. The first law of thermodynamics describes the conservation of energy. Energy cannot be created nor destroyed; it can only be transformed from one form of energy into another form of energy, and none is created or lost in the process. For example, when you drive to work, you convert the chemical energy in gasoline, which originally came from sunlight that was captured by single-celled life forms many millions of years ago. These organisms were later deposited in shallow-sea mud that subsequently turned into shale, as the carbon-rich mud was pushed down, compressed and heated by the accumulation of additional overlying sediments. The heat and pressure at depth cooked the shale enough to turn the single-celled organisms into oil, which then later migrated into overlying sandstone and limestone reservoir rock. When the gasoline is burned in your car engine, about 85% of the energy is immediately turned into waste heat energy, leaving about 15% left to be turned into the kinetic energy of your moving car. This remaining 15% is also eventually turned into waste heat energy too by the conclusion of your trip by wind resistance, rolling friction, and your brake linings. So when all is said and done, the solar energy that was released many millions of years ago by the Sun finally ends up as heat energy with 100% efficiency, none of the energy is lost during any of the steps of the process. It is just as if the million-year-old sunlight had just now fallen upon the asphalt parking lot of where you work and heated its surface. (see A Lesson From Steam Engines, Computer Science as a Technological Craft, and Entropy - the Bane of Programmers for more details)
There is a very similar effect in place for information. It turns out that information cannot be created nor destroyed either; it can only be converted from one form of information into another. It is now thought that information, like energy, is conserved because all of the current theories of physics are both deterministic and reversible. By deterministic, we mean that given the initial state of a system, a deterministic theory guarantees that one, and only one, possible outcome will result. Similarly, reversible theories describe interactions between objects in terms of reversible processes. A reversible process is a process that can be run backwards in time to return the Universe back to the initial state that it had before the process even began as if the process had never even happened in the first place. For example, the collision between two perfectly elastic balls at low energy is a reversible process that can be run backwards in time to return the Universe to its original state because Newton’s laws of motion are reversible. Knowing the position of each ball at any given time and also its momentum, a combination of its speed, direction, and mass, we can predict where each ball will go after a collision between the two, and also where each ball came from before the collision as well, using Newton’s laws of motion. For a deterministic reversible process such as this, the information required to return a system back to its initial state cannot be destroyed, no matter how many collisions might occur, in order to be classified as a reversible process that is operating under reversible physical laws.
Figure 6 - The collision between two perfectly elastic balls at low energy is a reversible process because Newton’s laws of motion are deterministic and reversible.
The fact that all of the current theories of physics, including quantum mechanics, are both deterministic and reversible, is a heavy blow to philosophy because it means that once a universe such as ours spuds off from the multiverse, all the drama is already over – all that will happen is already foreordained to happen, so there is no free will to worry about. Luckily, we do have the illusion that free will really exists because our Universe is largely composed of nonlinear systems and chaos theory has shown that even though nonlinear systems behave in a deterministic manner, they are not predictable because very small changes to initial conditions of a nonlinear system can produce huge changes to the final outcome of the system. This chaotic behavior makes the Universe appear to be random to us and gives us a false sense of security that all is not already foreordained (see Software Chaos for more on this). Also, remember that all of the current theories of physics are only effective theories. 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. All effective theories are just approximations of reality and are not really the fundamental “laws” of the Universe, but these effective theories make exceedingly good predictions of the behavior of physical systems over the limited ranges in which they apply. We keep coming up with better ones all the time. For example, if the above collision between two perfectly elastic balls were conducted at a very high energy, meaning that the balls were traveling close to the speed of light, Newton’s laws of motion would no longer work, and we would need to use another effective theory called the special theory of relativity to perform the calculations. However, we know that the special theory of relativity is also just an approximation because it cannot explain the behavior of very small objects like the electrons in atoms, where we use another effective theory called quantum mechanics. So perhaps one day we will come up with a more complete effective theory of physics that is not deterministic and reversible. It’s just that all of the ones we have come up with so far always are deterministic and reversible.
In Entropy - the Bane of Programmers
we went on to describe the second law of thermodynamics as the propensity of isolated macroscopic systems to run down or depreciate with time, as first proposed by Rudolph Clausius in 1850. Clausius observed that the Universe is constantly smoothing out differences. For example, his second law of thermodynamics proposed that spontaneous changes tend to smooth out differences in temperature, pressure, and density. Hot objects tend to cool off, tires under pressure leak air, and the cream in your coffee will stir itself if you are patient enough. Clausius defined the term entropy to measure this amount of smoothing-out or depreciation of a macroscopic system, and with the second law of thermodynamics, proposed that entropy always increased whenever a change was made. In The Demon of Software, we drilled down deeper still and explored Ludwig Boltzmann’s statistical mechanics, developed in 1872, in which he viewed entropy from the perspective of the microstates that a large number of molecules could exist in. For any given macrostate of a gas in a cylinder, Boltzmann defined the entropy of the system in terms of the number N of microstates that could produce the observed macrostate as:
S = k ln(N)
For example, air is about 78% nitrogen, 21% oxygen and 1% other gasses. The macrostate of finding all the oxygen molecules on one side of a container and all of the nitrogen molecules on the other side has a much lower number of microstates N than the macrostate of finding the nitrogen and oxygen thoroughly mixed together, so the entropy of a uniform mixture is much greater than the entropy of finding the oxygen and nitrogen separated. We used poker to clarify these concepts with the hope that you would come to the conclusion that the macrostate of going broke in Las Vegas had many more microstates than the macrostate of breaking the bank at one of the casinos.
We also discussed the apparent paradox of Maxwell’s Demon and how Leon Brillouin solved the mystery with his formulation of information as the difference between the initial and final entropies of a system after a determination of the state of the system had been made.
∆I = Si - Sf
Si = initial entropy
Sf = final entropy
Since the second law of thermodynamics demands that the entropy of the Universe must constantly increase, it also implies that the total amount of useful information in the Universe must constantly decrease. The number of low-entropy macrostates of a system, with very few contributing microstates, will always be much smaller than the number of high-entropy macrostates of a system with a large number of microstates. That is why a full house in poker or a bug-free program are rare, while a single pair or a slightly buggy program are much more common. So low-entropy forms of energy and information will always be much rarer in the Universe than high-entropy forms of energy and information. What this means is that the second law of thermodynamics demands that whenever we do something, like push a button, the total amount of useful energy and useful information in the Universe must decrease! Now, of course, the local amount of useful energy and information of a system can always be increased with a little work. For example, you can charge up your cell phone and increase the amount of useful energy in it, but in doing so, you will also create a great deal of waste heat when the coal that generated the electricity was burned because not all of the energy in the coal can be converted to electricity. Similarly, if you rifle through a deck of cards, you can always manage to deal yourself a full house, but in doing so, you will still decrease the total amount of useful information in the Universe. If you later shuffle your full house back into the deck of cards, your full house still exists in a disordered shuffled state with increased entropy, but the information necessary to reverse the shuffling process cannot be destroyed, so your full house could always be reclaimed by exactly reversing the shuffling process. It is just not a very practical thing to do, and that is why it appears that the full house has been destroyed by the shuffle.
The actions of the second law naturally lead to both the mutation of self-replicating information and to the natural selection of self-replicating information as well. This is because the second law guarantees that errors, or mutations, will always occur in all copying processes, and also limits the existence of the low-entropy resources, like a useful source of energy, that are required by all forms of self-replicating information to replicate. The existence of a limited resource base naturally leads to the selection pressures of natural selection because there simply are not enough resources to go around for all of the consumers of the resource base. Any form of self-replicating information that is better adapted to its environment will have a better chance at obtaining the resources it needs to replicate, and will, therefore, have a greater chance of passing that trait on to its offspring. Since all forms of self-replicating information are just one generation away from extinction, natural selection plays a very significant role in the evolution of all forms of self-replicating information.
The fact that the Universe is largely nonlinear in nature, meaning that very small changes to the initial state of a system can cause very large changes to the final state of a system as it moves through time, also means that small copying errors usually lead to disastrous, and many times lethal, consequences for all forms of self-replicating information (see Software Chaos for more details). The idea that the second law of thermodynamics, coupled with nonlinearity, are the fundamental problems facing all forms of self-replicating information, and therefore, are the driving forces behind evolution, are covered in greater detail in The Fundamental Problem of Software.
In addition to the second law of thermodynamics and nonlinearity, the evolution of software over the past 70 years has also shown that there have been several additional driving forces operating one level higher than the fundamental driving forces of the second law of thermodynamics and nonlinearity. Since the only form of self-replicating information that we have a good history of is software, I think the evolutionary history of software provides a wonderful model to study for those researchers looking into the origin and evolution of early life on Earth and also for those involved in the search for life elsewhere in the field of astrobiology. So let us outline those forces below for their benefit.
The Additional Driving Forces of Evolution
1. Darwin’s concept of evolution by means of inheritance and innovation honed by natural selection has certainly played the greatest role in the evolution of software over the past 70 years (2.2 billion seconds). All software evolves by means of these processes. In softwarephysics, we extend the concept of natural selection to include all selection processes that are not supernatural in nature. So a programmer making a selection decision after testing his latest iteration of code is considered to be a part of nature, and is therefore, a form of natural selection. Actually, the selection process for code is really performed by a number of memes residing within the mind of a programmer. Software is currently the most recent form of self-replicating information on the planet and it is currently exploiting the memes of various meme-complexes on the planet to survive. Like all of its predecessors, software first emerged as a pure parasite in May of 1941 on Konrad Zuse’s Z3 computer. Initially, software could not transmit memes, it could only perform calculations, like a very fast adding machine, so it was a pure parasite. But then the business and military meme-complexes discovered that software could be used to store and transmit memes, and software then quickly entered into a parasitic/symbiotic relationship with the memes. Today, software has formed strong parasitic/symbiotic relationships with just about 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 (see How Software Evolves for more details).
2. Stuart Kauffman’s concept of "order for free", like the emergent order found within a phospholipid bilayer that is simply seeking to minimize its free energy, and which forms the foundation upon which all biological membranes are built, or the formation of a crystalline lattice out of a melt, leading to Alexander Graham Cairns-Smith’s theory, first proposed in 1966, that there was a clay microcrystal precursor to RNA (see The Origin of Software the Origin of Life and Programming Clay for more details).
3. Lynn Margulis’s discovery that the formation of parasitic/symbiotic relationships between organisms is a very important driving force of evolution (see Software Symbiogenesis for more details).
4. Stephen Jay Gould’s concept of exaptation – the reuse of code originally meant for one purpose, but later put to use for another. We do that all the time with the reuse of computer code (see When Toasters Fly for more details).
5. Simon Conway Morris’s contention that convergence is a major driving force in evolution, where organisms in different evolutionary lines of descent evolve similar solutions to solve the same problems, as outlined in his book Life’s Solution (2003). We have seen this throughout the evolutionary history of software architecture, as software has repeatedly recapitulated the architectural design history of living things on Earth. The very lengthy period of unstructured code (1941 – 1972) was similar to the very lengthy dominance of the prokaryotic architecture of early life on Earth. This was followed by the dominance of structured programming (1972 – 1992), which was very similar to the rise of eukaryotic single-celled life. Object-oriented programming took off next, primarily with the arrival of Java in 1995. Object-oriented programming is the implementation of multicellular organization in software. Finally, we are currently going through a Cambrian explosion in IT with the SOA (Service Oriented Architecture) revolution, where consumer objects (somatic cells) make HTTP SOAP calls on service objects (organ cells) residing within organ service JVMs to provide webservices (see the SoftwarePaleontology section of SoftwareBiology for more details).
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.
Figure 8 – Computer simulations reveal how a camera-like eye can easily evolve from a simple light-sensitive spot on the skin.
Figure 9 – We can actually see this evolutionary history unfold in the evolution of the camera-like eye by examining modern-day mollusks such as the octopus.
6. Peter Ward’s observation that mass extinctions are key to clearing out ecological niches through dramatic environmental changes which additionally open other niches for exploitation. We have seen this throughout the evolutionary history of software as well. The distributed computing revolution of the early 1990s was a good example when people started hooking up cheap PCs into LANs and WANs and moved to a client/server architecture that threatened the existence of the old mainframe software. The arrival of the Internet explosion in 1995 opened a whole new environmental niche too for web-based software, and today we are going through a wireless-mobile computing revolution which is also opening entirely new environmental niches for software that might threaten the old stationary PC software we use today (see How to Use Your IT Skills to Save the World and Is Self-Replicating Information Inherently Self-Destructive? for more details).
7. The dynamite effect, where a new software architectural element spontaneously arises out of nothing, but its significance is not recognized at the time, and then it just languishes for many hundreds of millions of seconds, hiding in the daily background noise of IT. And then just as suddenly, after perhaps 400 – 900 million seconds, the idea finally catches fire and springs into life and becomes ubiquitous. What seems to happen with most new technologies, like eyeballs or new forms of software architecture, is that the very early precursors do not provide that much bang for the buck so they are like a lonely stick of dynamite with an ungrounded blasting cap stuck into it, waiting for a stray voltage to finally come along and set it off (see An IT Perspective of the Cambrian Explosion for more details).
Currently, researchers working on the origin of life and astrobiology are trying to produce computer simulations to help investigate how life could originate and evolve at its earliest stages. As you can see, trying to incorporate all of the above elements into a computer simulation would be a very daunting task indeed. The good news is that over the past 70 years the IT community has spent over $10 trillion building this computer simulation for them, and has already run it for over 2.2 billion seconds. It has been hiding there in plain sight the whole time for anybody with a little bit of daring and flair to explore.
Comments are welcome at scj333@sbcglobal.net
To see all posts on softwarephysics in reverse order go to:
https://softwarephysics.blogspot.com/
Regards,
Steve Johnston
Sunday, March 03, 2013
How to Use Softwarephysics to Revive Memetics in Academia
I just finished The Meme Machine (1999) by Susan Blackmore, which I found to be a very significant piece of scientific work that finally formalized the science of memetics in academia. As you know, softwarephysics maintains that there currently are three forms of self-replicating information on the planet – the genes, memes, and software, with software rapidly becoming the dominant form of self-replicating information. See A Brief History of Self-Replicating Information for details. Actually, in softwarephysics, the genes are thought to be an amalgam of at least three, and possibly many more, of the original forms of self-replicating information that brought forth life upon the Earth - the original organic metabolic pathways, RNA, and DNA. However, these three forms of self-replicating information are now so deeply intertwined that we can safely think of them as being one and call them the genes.
I first became aware of the memes in 1986 while in the IT department of Amoco working on BSDE – the Bionic Systems Development Environment. BSDE was my first practical application of softwarephysics and was used to “grow” applications from an “embryo” by allowing programmers to turn on and off a number of “genes” to generate code on the fly in an interactive mode. Applications were grown to maturity within BSDE through a process of embryonic growth and differentiation, with BSDE performing a maternal role through it all. Because BSDE generated the same kind of code that it was made of, BSDE was also used to generate code for itself. The next generation of BSDE was grown inside of its maternal release. Over a period of seven years, from 1985 – 1992, more than 1,000 generations of BSDE were grown to maturity, and BSDE slowly evolved into a very sophisticated tool through small incremental changes. During this period, BSDE also put several million lines of code into production at Amoco. For more on BSDE see the last half of my original post on SoftwarePhysics. Anyway, one day I was explaining BSDE to a fellow coworker and he recommended that I read The Selfish Gene (1976), for me the most significant book of the 20th century because it explains so much. In The Selfish Gene Richard Dawkins ended the book by explaining that there were now two forms of self-replicating information on the planet – the genes and memes. The concept of memes was later advanced by Daniel Dennett in Consciousness Explained (1991) and Richard Brodie in Virus of the Mind: The New Science of the Meme (1996), and was finally formalized by Susan Blackmore in The Meme Machine. For those of you not familiar with the term meme, it rhymes with the word “cream”. Memes are cultural artifacts that persist through time by making copies of themselves in the minds of human beings and were first recognized by Richard Dawkins in The Selfish Gene. Dawkins described memes as “Examples of memes are tunes, ideas, catch-phrases, clothes fashions, ways of making pots or of building arches. Just as genes propagate themselves in the gene pool by leaping from body to body via sperms or eggs, so memes propagate themselves in the meme pool by leaping from brain to brain via a process which, in the broad sense, can be called imitation.”. Just as genes come together to build bodies, or DNA survival machines, for their own mutual advantage, memes also come together from the meme pool to form meme-complexes for their own joint survival. DNA survives down through the ages by inducing disposable DNA survival machines, in the form of bodies, to produce new disposable DNA survival machines. Similarly, memes survive in meme-complexes by inducing the minds of human beings to reproduce memes in the minds of others. To the genes and memes, human bodies are simply disposable DNA survival machines housing disposable minds that come and go with a lifespan of less than 100 years. The genes and memes, on the other hand, continue on largely unscathed by time as they skip down through the generations. However, both genes and memes do evolve over time through the Darwinian mechanisms of inheritance, innovation and natural selection. You see, the genes and memes that do not come together to build successful DNA survival machines, or meme-complexes, are soon eliminated from the gene and meme pools. So both genes and memes are selected for one overriding characteristic – the ability to survive. Once again, the “survival of the fittest” rules the day. Now it makes no sense to think of genes or memes as being either “good” or “bad”; they are just mindless forms of self-replicating information bent upon surviving with little interest in you as a disposable survival machine. So in general, these genes and memes are not necessarily working in your best interest, beyond keeping you alive long enough so that you can pass them on to somebody else. That is why, if you examine the great moral and philosophical teachings of most religions and philosophies, you will see a plea for us all to rise above the selfish self-serving interests of our genes and memes.
Meme-complexes come in a variety of sizes and can become quite large and complicated with a diverse spectrum of member memes. Examples of meme-complexes of increasing complexity and size would be Little League baseball teams, clubs and lodges, corporations, political and religious movements, tribal subcultures, branches of the military, governments and cultures at the national level, and finally the sum total of all human knowledge in the form of all the world cultures, art, music, religion, and science put together. Meme-complexes can do wonderful things, as is evidenced by the incredible standard of living enjoyed by the modern world, thanks to the efforts of the scientific meme-complex, or the great works of art, music, and literature handed down to us from the Baroque, Classical, and Romantic periods, not to mention the joys of jazz, rock and roll, and the blues. However, meme-complexes can also turn incredibly nasty. Just since the Scientific Revolution of the 17th century we have seen the Thirty Years' War (1618 -1648), the Salem witch hunts (1692), the French Reign of Terror (1793 – 1794), American slavery (1654 – 1865), World War I (all sides) (1914 – 1918), the Stalinist Soviet Union (1929 – 1953), National Socialism (1933 – 1945), McCarthyism (1949 – 1958), Mao’s Cultural Revolution (1969 – 1976), and Pol Pot’s reign of terror (1976 – 1979).
The problem is that when human beings get wrapped up in a meme-complex, they can do horrendous things without even being aware of the fact. This is because, in order to survive, the first thing that most meme-complexes do is to use a meme that turns off human thought and reflection. To paraphrase Descartes ”I think, therefore I am" a heretic. So if you ever questioned any of the participants caught up in any of the above atrocious events, you would find that the vast majority would not have any qualms about their deadly activities whatsoever. In fact, they would question your loyalty and patriotism for even bringing up the subject. For example, during World War I, which caused 40 million casualties and the deaths of 20 million people for apparently no particular reason at all, there were few dissenters beyond Albert Einstein in Germany and Bertrand Russell in Great Britain, and both suffered the consequences of not being on board with the World War I meme-complex. Unquestioning blind obedience to a meme-complex through unconditional group-think is definitely a good survival strategy for any meme-complex. But the scientific meme-complex has an even better survival strategy – skepticism and scrutiny. Using skepticism and scrutiny may not seem like a very good survival strategy for a meme-complex because it calls into question the validity of the individual memes within the meme-complex itself. But that can also be a crucial advantage. By eliminating memes from within the scientific meme-complex that cannot stand up to skepticism and scrutiny, the whole scientific meme-complex is strengthened, and when this skepticism and scrutiny are turned outwards towards other meme-complexes, the scientific meme-complex is strengthened even more so.
Reading The Selfish Gene was a real epiphany for me. All of a sudden everything finally made sense. Living things do not use their genes to build and operate their bodies, rather genes use bodies to store and replicate genes! Similarly, brains do not use abstract concepts to allow them to do things in the real world, rather abstract concepts, or memes, use brains to store and replicate memes. Given that, the absurd “real world” of human affairs finally made sense, and I immediately adopted these concepts into my own worldview and have used them routinely on a daily basis ever since to explain it all. I also became aware at this time that a third form of self-replicating information, in the form of software, had also recently appeared upon the scene and that software was rapidly taking over control from the memes that were currently running the world.
In The Meme Machine, Susan Blackmore goes much further with the concept of memes and brings memetics to the level of a fully comprehensive science that is falsifiable. Memetics can now explain many current observations better than other models, and can make predictions of observations yet to be made that can be investigated and tested in the future. As such, memetics should now stand in good stead with the rest of the sciences. For example, Blackmore maintains that memetic-drive was responsible for creating our extremely large brains and also our languages and cultures as well, in order to store and spread memes more effectively. Many researchers have noted that the human brain is way over-engineered for the needs of a simple hunter-gatherer. After all, even a hundred years ago, people did not require the brain-power to do IT work, yet today we find many millions of people earning their living doing IT work, or at least trying to. Blackmore then 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 themselves. 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.
A very similar thing happened with software over the past 70 years. When I first started programming in 1972, million dollar mainframe computers typically had about 1 MB (about 1,000,000 bytes) of memory. 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 $600 PC comes with 8 GB (about 8,000,000,000 bytes) of memory. Recently, 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.
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 some 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. The memes have since modified the entire planet. They have 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 sequestered in the Earth, and have even modified the very DNA, RNA, and metabolic pathways of its predecessors.
We can 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, or by imitating the code of others, or by working through books written by others. Even after people do learn how to program in a certain 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. See Self-Replicating Information for more details on this stage of self-replicating information. See A Brief History of Self-Replicating Information for details.
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, software could not transmit memes, it could only perform calculations, like a very fast adding machine, so it was a pure parasite. But then the business and military meme-complexes discovered that software could be used to transmit memes, and software then entered into a parasitic/symbiotic relationship with the memes. Today, software has formed strong parasitic/symbiotic relationships with just about 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 in person. 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.
Memetics in Academia
I started working on softwarephysics in 1979 when I made a career change from being an exploration geophysicist to becoming an IT professional, and consequently, I have mainly focused upon software and its effects as the latest form of self-replicating information on the planet over the ensuing years. I must admit that, over this same period of time, I did not closely follow what had happened to memetics in academia. However, I had noticed that the concept of memes had seemed to have permeated throughout the popular culture, granted, many times in a rather confused and distorted manner, but still, I figured that memetics had done quite well in academia as well. But after reading The Meme Machine, it dawned upon me that this book should have totally revolutionized all of the fields in academia that dealt with the human condition, such as psychology, sociology, history, physical anthropology, cultural anthropology, political science, and economics, but I had not noticed that happening as a passive observer.
After doing a little research on the Internet, it seemed to me that memetics had gotten off to a pretty good start in the late 1980s and continued on into the 1990s, but had seemingly died in academia around 2005! Perhaps I am wrong, but this would truly be an intellectual tragedy if it were true. My perception is based on the fact that I could not find very much serious material about memetics published after 2005. Also, I came across the very last issue of:
Journal of Memetics - Evolutionary Models of Information Transmission at:
http://cfpm.org/jom-emit/
and it contained a very disturbing paper by Bruce Edmonds:
The revealed poverty of the gene-meme analogy -
why memetics per se has failed to produce substantive results
http://cfpm.org/jom-emit/2005/vol9/edmonds_b.html
which contained the graph of memetics papers shown in Figure 1.
Figure 1 – A graph of academic papers on memetics over nearly 20 years. (click to enlarge)
Now I must point out that his dramatic downturn in the graph is based solely upon a single data point at the very end of the data set, which is always a very serious warning signal in science, but I must admit that this paper did indeed appear in the very last issue of the journal and that I have not found very much work being done in memetics past the year 2005 in academia. Additionally, I was very dismayed to read of the numerous, and very wrong-headed, objections to memetics that Susan Blackmore outlined in The Meme Machine, and that I also found on the Internet.
Deja Vu All Over Again
However, all is not lost. I have personally seen all this before. It all stems from the very conservative nature of meme-complexes, and their very great reluctance to adopt new memes that threaten the very existence of the entire meme-complex. This is especially true of scientific meme-complexes, and rightly so. Scientific meme-complexes always have to be on guard to prevent the latest crackpot idea from taking hold. But if you look to the history of science, the downside to all this is that nearly all of the great scientific breakthroughs were delayed by 10 – 50 years, patiently waiting for acceptance by a scientific meme-complex. That is why Thomas Kuhn found that scientific meme-complexes were so very reluctant to adopt paradigm shifts. Let me provide a personal example.
I finished my B.S. in Physics at the University of Illinois in 1973. However, early in my senior year, I, unfortunately, discovered that there was no future in physics in the United States. At the time, there were thousands of newly minted Ph.D. graduates, but only a handful even got a postdoc position in 1972. Most ended up doing other things, like waiting tables. This was just a precursor to America’s long and very sad march to becoming a country that has little confidence in science. Now I did happen to have a roommate who was a geology major, and; he suggested that I try switching into geophysics to explore for oil for oil companies. Like all little boys, I had once had a rock collection, so I figured that being a geophysicist with a job was a lot better than being a waiter, and so I made the switch. When I got to the Department of Geology and Geophysics at the University of Wisconsin in the summer of 1973, I had many deficiencies in geology, having not taken a single course in geology for my entire undergraduate career, so I had to take many undergraduate courses in geology to make up for what I had missed.
These were very interesting times in geology because of the plate tectonics revolution of the late 1960s. The plate tectonics revolution was a dramatic paradigm shift for classical geology, and everything had to be rethought in light of the new model. However, the textbooks in 1973 that I studied had not had time to catch up with the radical new worldview of plate tectonics, and still contained the old classical geological models that now seemed rather silly in light of the new model of plate tectonics. So it was rather exciting to be a graduate student in the middle of a paradigm shift. Let me explain what had happened. By the early 1960s, geologists had done a marvelous job at figuring out what had happened over the past billion years of geological time, but they had done a truly miserable job at explaining why things had happened. By mapping outcrops and road cuts, geologists were able to see mountains rise from the sea over the course of tens of millions of years, only to be later eroded down to flat plains over the course of hundreds of millions of years, and they saw massive volcanic eruptions like the Deccan Traps covering 500,000 square miles of India to a depth of 6,000 feet, and there were the ever-present earthquakes and volcanoes to deal with too. But by the early 1960s, the geologists were stuck, they could just not figure out what was going on. Then geophysics came to the rescue. Modern geophysics really got started after World War II, with the availability of lots of government war surplus gear for the Universities to buy on the cheap. With geophysics, we began to explore the Earth with things besides our eyes, ears, and hands. Yes, geologists will actually listen to rocks as they whack them with a field hammer. Geophysicists did that by shooting seismic waves into the Earth and mapping the variations of the Earth’s magnetic, electrical, and gravitational fields. With these technologies, geophysicists were able to “see” below the Earth’s surface, and more importantly, to “see” under the Earth’s oceans. Now it turns out that most of the evidence for plate tectonics was under water at the spreading centers, like the Mid-Atlantic Ridge, and at subduction zones at the deep oceanic trenches. If the Earth had not had oceans, the geologists would have seen plate tectonics in action with their very own eyes, and would have figured it all out hundreds of years ago. Of course, if the Earth had had no oceans, there would not be any geologists here to bother with the problem in the first place!
Now the sad point to all this is that people like Alfred Wegener had accumulated enough data by 1912 to come up with an alternative to the classical geological models called Continental Drift that contained all of the essentials of plate tectonics. Continental Drift was just a little fuzzy on the exact mechanisms of plate tectonics, but it certainly deserved proper recognition by the geological community at the time and additional research efforts into its significance. But instead, Continental Drift was totally rejected by the geological establishment of the day, and poor Alfred Wegner was ridiculed for all his excellent efforts. So despite the fact that millions of grade-school children had commented to their geography teachers that South America seemed to fit together with Africa like two pieces of a puzzle, only to be told that it was just a funny coincidence, the geologists stubbornly stuck to their old models until irrefutable geophysical data forced them to change in the 1960s.
I have not spent a great deal of time investigating this, but I do seem to see these very same processes at work against memetics in academia on the Internet. Yes, memetics, like all new sciences, has to start out as a feeble candle in the darkness, like the theory of Continental Drift, but it holds so much promise! I don’t know if anybody in academia is still working on memetics, but if there are some young courageous souls out there with a bit of daring and flair, I would like to pass the following suggestions on to them. Below are some suggestions based on softwarephysics that might help to clear up some of the confusions that academia seems to have with memetics. Yes, I know when you have a hammer, everything begins to look like a nail, but please bear with me.
Suggestions For A Research Program To Revive Memetics
1. Genes, memes, and software are all forms of self-replicating information, so the first thing you have to understand is the nature of information in physics. There seems to be a great deal of confusion about what exactly a meme is and what information is in the criticisms that I have seen of memetics. See
The Demon of Software and
Is Information Real? for more details.
2. Memes are the most difficult form of self-replicating information to understand because they are all tied up with the very messy “real world” of human affairs. In addition, there are many competing academic meme-complexes to deal with, like cultural anthropology, sociology, and psychology, all vying for the same academic turf, very much like the battle between the geophysicists and geologists over plate tectonics outlined above. Memetics needs some additional help from outside of these contentious domains by looking to the world of software. I have been traipsing through the world of software myself for more than 30 years, and I have yet to come across a fellow research party - not even a lonely lost graduate student! And I am quite sure there must be some out there; graduate students have a tremendous knack for getting lost before their thesis advisor sets them straight upon the right course, narrowly averting a major scientific breakthrough! But I can guarantee that this is a wide-open domain in academia with no competing scientific meme-complexes to deal with.
3. Yes, the genes and memes are both forms of self-replicating information, but if you only work with two forms of self-replicating information, people in the academic meme-complexes that you are about to invade will invariably complain that memes are not exactly the same as genes. Of course memes are not identical to genes; otherwise, memes would be genes! Memes and genes are both just forms of self-replicating information, with some common characteristics, but naturally, with many differences as well. If you bring in a third replicator, like software, the differences between the three become quite evident, and so do their similarities.
4. Memes have much more in common with software than they do with genes. Both memes and software do not have to drag along clunky bodies or have sex to replicate. Like the memes, software is now nearly a pure form of self-replicating information with little physical ties to matter. In the 1950s and 1960s, IT people mainly worried about hardware and not software. The puny computers of the day had so little memory that programs had to be incredibly small to fit within them, and the hardware was not very reliable either, so IT people mostly spent their time dealing with hardware problems. But now hardware is as cheap as dirt, and IT people hardly ever even worry about it any longer. We only get annoyed when the incessant software-drive forces us to upgrade the hardware because we ran out of memory and processor speed again. But that only happens every few years. Still, the major headache with a hardware upgrade is getting all the software installed and operational again on the new hardware which you just have to plug into an electrical socket and connect a few cables to.
To make software an even purer form of information, we now use virtual machines to run software. For example, nearly all of the software I support runs in JVMs (Java Virtual Machines) on Unix servers. This means the compiled Java programs run in a computer-simulated computer of their own called a JVM, which has its own virtual registers, and its own virtual heap and stack memory too. A typical Unix server might run about 100 simulated JVM computers. Worse yet, even the Unix servers are not real! The Unix servers are virtual servers created from a Frame containing many CPUs in a box and are connected to a SAN (Storage Area Network) of disk drives. So when you use a website, all of this software is spread out over several hundred to several thousand of these virtual servers. Consequently, we now have software running on software running on software running on software…… So for most IT people and end-users, it’s all just a huge virtual cyberspacetime continuum of pure information floating in what IT people now call the Cloud.
5. Software is the most recent form of self-replicating information and is the only form of self-replicating information that we have a good history of. Unlike the genes and memes, we have a well-documented history of the arrival of software upon the scene and its evolutionary history as well. I am now 61 years old, and therefore old enough to remember back to my early childhood when there essentially was no software whatsoever. Now software is virtually everywhere and is quickly becoming the dominant form of self-replicating information on the planet. Therefore, software makes a much better model for memes than do the genes because we have seen all of this happen within our very lifetimes. See How Software Evolves
6. Memetics needs to take a cue from physics and adopt a positivistic approach to the subject using effective theories. Positivism is an enhanced form of empiricism, in which we do not care about how things “really” are; we are only interested in how things are observed to behave. With positivism, physicists only seek out models of reality - not reality itself. Effective theories are 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 works very well for objects moving in weak gravitational fields at less than 10% of the speed of light and which are larger than a very small mote of dust. For things moving at high velocities or in strong gravitational fields we must use relativity theory, and for very small things like atoms, we must use quantum mechanics. All of the current theories of physics, such as Newtonian mechanics, classical electrodynamics, thermodynamics, statistical mechanics, the special and general theories of relativity, quantum mechanics, and quantum field theories like QED and QCD are just 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, and that is all positivism hopes to achieve.
So do not worry about whether or not memes “really” exist. For a physicist, if things behave as if memes exist, that is plenty good enough to form the basis for an effective theory. For example, do atoms really exist? Certainly not in the sense that most people would recognize as being “real”. In quantum field theory an atom is just a collection of electron and quark fields that extend over the entire Universe, but that are much stronger in the vicinity of the supposed atom, and that are held together by virtual photons and virtual gluons. When you interact with the quantum fields of an atom by colliding an electron or some other particle with it in order to make a measurement, you will find that the measurement allows you to narrow down the location of the constituent electrons, protons, and neutrons and to also get some idea of their momentum too, but you will never be able to exactly determine the exact location and momentum of any particular particle within an atom because of the Heisenberg Uncertainty Principle. To take this analysis to the level of bulk matter, consider a 200 pound man. According to the best available effective theories of physics, the man really consists of mostly empty space and about 100 pounds of protons, 100 pounds of neutrons, and 0.87 ounces of electrons. A proton, consisting of two up quarks and one down quark, has a mass of 938.27 MeV. Similarly, a neutron, consisting of one up quark and two down quarks, is slightly more massive with a mass of 939.56 MeV. But the up and down quarks themselves are surprisingly quite light. The up quark is now thought to have a mass with an upper limit of only 4 MeV, and the down quark is thought to have a mass with an upper limit of only 8 MeV. So a proton should have a mass of about 16 MeV instead of 938.27 MeV, and the neutron should have a mass of about 20 MeV instead of 939.56 MeV. Where does all this extra mass come from? It comes from the kinetic and binding energy of the virtual gluons that hold the up and down quarks together to form protons and neutrons! Remember that energy can add to the mass of an object via E = mc2 by simply rearranging the formula as m = E/c2. So going back to our fictional 200 pound man, consisting of 100 pounds of protons, 100 pounds of neutrons, and 0.87 ounces of electrons, we can now say that the man actually consists of 0.87 ounces of electrons, 1.28 pounds of up quarks, 2.56 pounds of down quarks, and 196.10 pounds of pure energy! Strangely, everything you see, hear, smell, taste, and feel results from the interactions of less than one ounce of those electrons. And all of the biochemical reactions that keep you alive and even your thoughts at this very moment are all accomplished with this small mass of electrons! According to our best effective theory on the subject, the quantum field theory of QED (1948), when you push your hand down on a table, the electrons in the table and your hand begin to exchange virtual photons that carry the electromagnetic force. This force gives you the illusion that the table and your hand are solid objects, when according to our best effective theories; the table and your hand consist mostly of empty space and pure energy with a thin haze of surrounding electrons. When you look at the man, table, or anything else your Mind is simply creating the illusion of their existence, as ambient photons in the room scatter off the thin haze of electrons surrounding the objects – the objects themselves are mainly composed of pure energy. So do not worry about memes being “real”. Stephen Hawking certainly does not worry about electrons being “real”. For more on this see Introduction to Softwarephysics and Model-Dependent Realism - A Positivistic Approach to Realism.
7. The best way to achieve this is to do some field work in the IT department of a major corporation. See A Proposal For All Practicing Paleontologists for additional advice. Yes, this might be a little scary. It would be best to approach this like a 21st century Margaret Mead doing fieldwork amongst IT professionals in the wild. The good news is that IT people are notoriously straightforward and will certainly not try to trick you like the inhabitants of Samoa. However, you should not tell them that you are trying to revive memetics using concepts from softwarephysics. If you do, they will certainly not burn you at the stake, but your research program will indeed go up in smoke. Instead, tell them you are doing a study comparing Agile development techniques to traditional development techniques. The IT department of the corporation in question will be using one or the other, or possibly both, and will probably get quite excited over your proposal because Agile development is one of the latest IT memes – a nicer way of saying “fads”. You can easily Google what Agile development is all about. Below is the first paragraph from the Wikipedea:
Agile software development is a group of software development methods based on iterative and incremental development, where requirements and solutions evolve through collaboration between self-organizing, cross-functional teams. It promotes adaptive planning, evolutionary development and delivery, a time-boxed iterative approach, and encourages rapid and flexible response to change. It is a conceptual framework that promotes foreseen interactions throughout the development cycle.
It certainly is vague enough to let you easily observe IT people in the field and talk to them about things like evolutionary development without drawing too much attention to yourselves.
I could go on, but this posting is way too long already. If anybody is interested, I would be glad to help with this endeavor.
Comments are welcome at scj333@sbcglobal.net
To see all posts on softwarephysics in reverse order go to:
https://softwarephysics.blogspot.com/
Regards,
Steve Johnston