Saturday, May 14, 2016

Introduction to Softwarephysics

Softwarephysics is a simulated science for the simulated Software Universe that we are all immersed in. It is an approach to software development, maintenance, and support based upon concepts from physics, chemistry, biology, and geology that I have been using on a daily basis for over 35 years as an IT professional. For those of you not in the business, IT is short for Information Technology, commercial computer science. The purpose of softwarephysics is to explain why IT is so difficult, to suggest possible remedies, and to provide a direction for thought. If you are an IT professional, general computer user, or simply an individual interested in computer science, physics, chemistry, biology, or geology then softwarephysics might be of interest to you, if not in an entirely serious manner, perhaps at least in an entertaining one.

From 1975 – 1979, I was an exploration geophysicist exploring for oil, first with Shell, and then with Amoco. In 1979, I made a career change into IT, and spent about 20 years in development. For the past 17 years, I have been in IT operations, supporting middleware on WebSphere, JBoss, Tomcat, and ColdFusion. When I 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. Along these lines, we use the Equivalence Conjecture of Softwarephysics as an aid; it allows us to shift back and forth between the Software Universe and the physical Universe, and hopefully to learn something about one by examining the other:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

MoneyPhysics – my impression of the 2008 world financial meltdown.

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

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

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

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

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

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

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

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

SoftwarePhysics 101 – The Physics of Cyberspacetime - Original PowerPoint document

Entropy – A spreadsheet referenced in the document

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

Comments are welcome at scj333@sbcglobal.net

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

Regards,
Steve Johnston

Wednesday, March 09, 2016

Cloud Computing and the Coming Software Mass Extinction

I started this blog on softwarephysics about 10 years ago with the hopes of helping the IT community to better deal with the daily mayhem of life in IT, after my less than stunning success in doing so back in the 1980s, when I first began developing softwarephysics for my own use. My original purpose for softwarephysics was to formulate a theoretical framework that was independent of both time and technology, and which therefore could be used by IT professionals to make decisions in both tranquil times and in times of turmoil. Having a theoretical framework in turbulent times is an especially good thing to have because it helps to make sense of things when dramatic changes are happening. Geologists have plate tectonics, biologists have Darwinian thought and chemists have atomic theory to fall back upon when the going gets tough, and a firm theoretical framework provides the necessary comfort and support to get through them. With the advent of Cloud Computing, we once again seem to be heading into turbulent times within the IT industry that will have lasting effects on the careers of many IT professionals, and that will be the subject of this posting.

One of the fundamental tenants of softwarephysics is that software and living things are both forms of self-replicating information, and that both of them have converged upon very similar solutions to common data processing problems. This convergence means that they are also both subject to common experiences. Consequently, there is much that IT professionals can learn by studying living things, and also much that biologists can learn by studying the evolution of software over the past 75 years, or 2.4 billion seconds, ever since Konrad Zuse first cranked up his Z3 computer in May of 1941. For example, the success of both software and living things is very dependent upon environmental factors, and when the environment dramatically changes, it can lead to mass extinctions of both software and living things. In fact, the geological timescale during the Phanerozoic, when complex multicellular life first came to be, is divided by two great mass extinctions into three segments - the Paleozoic or "old life", the Mesozoic or "middle life", and the Cenozoic or "new life". The Paleozoic-Mesozoic boundary is defined by the Permian-Triassic greenhouse gas mass extinction 252 million years ago, while the Mesozoic-Cenozoic boundary is defined by the Cretaceous-Tertiary mass extinction caused by the impact of a small 6 mile in diameter asteroid with the Earth 65 million years ago. During both of these major mass extinctions a large percentage of the extant species went extinct and the look and feel of the entire biosphere dramatically changed. By far, the Permian-Triassic greenhouse gas mass extinction 252 million years ago was the worst. A massive flood basalt known as the Siberian Traps covered an area about the size of the continental United States with several thousand feet of basaltic lava, with eruptions that lasted for about one million years. Flood basalts, like the Siberian Traps, are thought to arise when large plumes of hotter than normal mantle material rise from near the mantle-core boundary of the Earth and break to the surface. This causes a huge number of fissures to open over a very large area that then begin to disgorge massive amounts of basaltic lava over a very large region. After the eruptions of basaltic lava began, it took about 100,000 years for the carbon dioxide that bubbled out of the basaltic lava to dramatically raise the level of carbon dioxide in the Earth's atmosphere and initiate the greenhouse gas mass extinction. This led to an Earth with a daily high of 140 oF and purple oceans choked with hydrogen-sulfide producing bacteria, producing a dingy green sky over an atmosphere tainted with toxic levels of hydrogen sulfide gas and an oxygen level of only about 12%. The Permian-Triassic greenhouse gas mass extinction killed off about 95% of marine species and 70% of land-based species, and dramatically reduced the diversity of the biosphere for about 10 million years. It took a full 100 million years to recover from it.

Figure 1 - The geological timescale of the Phanerozoic Eon is divided into the Paleozoic, Mesozoic and Cenozoic Eras by two great mass extinctions - click to enlarge.

Figure 2 - Life in the Paleozoic, before the Permian-Triassic mass extinction, was far different than life in the Mesozoic.

Figure 3 - In the Mesozoic the dinosaurs ruled after the Permian-Triassic mass extinction, but small mammals were also present.

Figure 4 - Life in the Cenozoic, following the Cretaceous-Tertiary mass extinction, has so far been dominated by the mammals. This will likely soon change as software becomes the dominant form of self-replicating information on the planet, ushering in a new geological Era that has yet to be named.

Similarly, IT experienced a similar devastating mass extinction about 25 years ago when we experienced an environmental change that took us from the Age of the Mainframes to the Distributed Computing Platform. Suddenly mainframe Cobol/CICS and Cobol/DB2 programmers were no longer in demand. Instead, everybody wanted C and C++ programmers who worked on cheap Unix servers. This was a very traumatic time for IT professionals. Of course the mainframe programmers never went entirely extinct, but their numbers were greatly reduced. The number of IT workers in mainframe Operations also dramatically decreased, while at the same time the demand for Operations people familiar with the Unix-based software of the new Distributed Computing Platform skyrocketed. This was around 1992, and at the time I was a mainframe programmer used to working with IBM's MVS and VM/CMS operating systems, writing Cobol, PL-1 and REXX code using DB2 databases. So I had to teach myself Unix and C and C++ to survive. In order to do that, I bought my very first PC, an 80-386 machine running Windows 3.0 with 5 MB of memory and a 100 MB hard disk for $1500. I also bought the Microsoft C7 C/C++ compiler for something like $300. And that was in 1992 dollars! One reason for the added expense was that there were no Internet downloads in those days because there were no high-speed ISPs. PCs did not have CD/DVD drives either, so the software came on 33 diskettes, each with a 1.44 MB capacity, that had to be loaded one diskette at a time in sequence. The software also came with a about a foot of manuals describing the C++ class library on very thin paper. Indeed, suddenly finding yourself to be obsolete is not a pleasant thing and calls for drastic action.

Figure 5 – An IBM OS/360 mainframe from 1964.

Figure 6 – The Distributed Computing Platform replaced a great deal of mainframe computing with a large number of cheap self-contained servers running software that tied the servers together.

The problem with the Distributed Computing Platform was that although the server hardware was cheaper than mainframe hardware, the granular nature of the Distributed Computing Platform meant that it created a very labor intensive infrastructure that was difficult to operate and support, and as the level of Internet traffic dramatically expanded over the past 20 years, the Distributed Computing Platform became nearly impossible to support. For example, I have been in Middleware Operations with my current employer for the past 14 years, and during that time our Distributed Computing Platform infrastructure exploded by a factor of at least a hundred. It is now so complex and convoluted that we can barely keep it all running, and we really do not even have enough change window time to properly apply maintenance to it as I described in The Limitations of Darwinian Systems. Clearly, the Distributed Computing Platform was not sustainable, and an alternative was desperately needed. This is because the Distributed Computing Platform was IT's first shot at running software on a multicellular architecture, as I described in Software Embryogenesis. But the Distributed Computing Platform simply had too many moving parts, all working together independently on their own, to fully embrace the advantages of a multicellular organization. In many ways the Distributed Computing Platform was much like the ancient stromatolites that tried to reap the advantages of a multicellular organism by simply tying together the diverse interests of multiple layers of prokaryotic cyanobacteria into a "multicellular organism" that seemingly benefited the interests of all.

Figure 7 – Stromatolites are still found today in Sharks Bay Australia. They consist of mounds of alternating layers of prokaryotic bacteria.

Figure 8 – The cross section of an ancient stromatolite displays the multiple layers of prokaryotic cyanobacteria that came together for their own mutual self-survival to form a primitive "multicellular" organism that seemingly benefited the interests of all. The servers and software of the Distributed Computing Platform were very much like the primitive stromatolites.

The Rise of Cloud Computing
Now I must admit that I have no experience with Cloud Computing whatsoever because I am an old "legacy" mainframe programmer who has now become an old "legacy" Distributed Computing Platform person. But this is where softwarephysics comes in handy because it provides an unchanging theoretical framework. As I explained in The Fundamental Problem of Software, the second law of thermodynamics is not going away, and the fact that software must operate in a nonlinear Universe is not going away either, so although I may not be an expert in Cloud Computing, I do know enough softwarephysics to sense that a dramatic environmental change is taking place, and I do know from firsthand experience what comes next. So let me offer some advice to younger IT professionals on that basis.

The alternative to the Distributed Computing Platform is the Cloud Computing Platform, which is usually displayed as a series of services all stacked into levels. The highest level, SaaS (Software as a Service) runs the common third party office software like Microsoft Office 365 and email. The second level, PaaS (Platform as a Service) is where the custom business software resides, and the lowest level, IaaS (Infrastructure as a Service) provides for an abstract tier of virtual servers and other resources that automatically scale with varying load levels. From an Applications Development standpoint the PaaS layer is the most interesting because that is where they will be installing the custom application software used to run the business and also to run high-volume corporate websites that their customers use. Currently, that custom application software is installed into the middleware that is running on the Unix servers of the Distributed Computing Platform. The PaaS level will be replacing the middleware software, such as the Apache webservers and the J2EE Application servers, like WebSphere, Weblogic and JBoss that currently do that. For Operations, the IaaS level, and to a large extent, the PaaS level too are of most interest because those levels will be replacing the middleware and other support software running on hundreds or thousands of individual self-contained servers. The Cloud architecture can be run on a company's own hardware, or it can be run on a timesharing basis on the hardware at Amazon, Microsoft, IBM or other Cloud providers, using the Cloud software that the Cloud providers market.

Figure 9 – Cloud Computing returns us to the timesharing days of the 1960s and 1970s by viewing everything as a service.

Basically, the Cloud Computing Platform is based upon two defining characteristics:

1. Returning to the timesharing days of the 1960s and 1970s when many organizations could not afford to support a mainframe infrastructure of their own.

2. Taking the mulitcellular architecture of the Distributed Computing Platform to the next level by using Cloud Platform software to produce a full-blown multicellular organism, and even higher, by introducing the self-organizing behaviors of the social insects like ants and bees.

Let's begin with the timesharing concept first because that should be the most familiar to IT professionals. This characteristic of Cloud Computing is actually not so new. For example, in 1968 my high school ran a line to the Illinois Institute of Technology to connect a local card reader and printer to the Institute's mainframe computer. We had our own keypunch machine to punch up the cards for Fortran programs that we could submit to the distant mainframe which then printed back the results of our program runs. The Illinois Institute of Technology also did the same for several other high schools in my area. This allowed high school students in the area to gain some experience with computers even though their high schools could clearly not afford to maintain a mainframe infrastructure.

Figure 10 - An IBM 029 keypunch machine like the one installed in my high school in 1968.

Figure 11 - Each card could hold a maximum of 80 bytes. Normally, one line of code was punched onto each card.

Figure 12 - 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 13 - 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 back at the Illinois Institute of Technology. In the above figure, the mainframe is located directly behind the card reader.

Figure 14 - The output of Fortran programs run at the Illinois Institute of Technology was printed locally at my high school on a line printer.

Similarly, when I first joined the IT department of Amoco in 1979, we were using GE Timeshare for all of our interactive programming needs. GE Timeshare was running VM/CMS on mainframes, and we would connect to the mainframe using a 300 baud acoustic coupler with a standard telephone. You would first dial GE Timeshare with your phone and when you heard the strange "BOING - BOING - SHHHHH" sounds from the mainframe, you would jam the telephone receiver into the acoustic coupler so that you could login to the mainframe over a "glass teletype" dumb terminal.

Figure 15 – To timeshare we connected to GE Timeshare over a 300 baud acoustic coupler modem.

Figure 16 – Then we did interactive computing on VM/CMS using a dumb terminal.

But timesharing largely went out of style in the 1970s when many organizations began to create their own datacenters and ran their own mainframes within them. They did so because with timesharing you paid the timesharing provider by the CPU-second and by the byte for disk space, and that became expensive as timesharing consumption rose. Timesharing also limited flexibility because businesses were limited by the constraints of their timesharing providers. For example, during the early 1980s Amoco created 31 VM/CMS datacenters around the world, and tied them all together with communications lines to support the interactive programming needs of the business. This network of 31 VM/CMS datacenters was called the Corporate Timeshare System, and allowed end-users to communicate with each other around the world using an office system that Amoco developed in the late 1970s called PROFS (Professional Office System). PROFS had email, calendaring and document sharing software that later IBM sold as a product called OfficeVision. Essentially, this provided Amoco with a SaaS layer in the early 1980s.

So for IT professionals in Operations, the main question is will corporations initially move to a Cloud Computing Platform hosted by Cloud providers, but then become dissatisfied with the Cloud providers, like they did with the timesharing providers of the 1970s, and then create their own private corporate Cloud platforms? Or will corporations remain using the Cloud Computing Platform of Cloud providers on a timesharing basis? It could also go the other way too. Corporations might start up their own Cloud platforms within their existing datacenters, but then decide that it would be cheaper to move to the Cloud platform hosted by a Cloud provider. Right now, it is too early to know how Cloud Computing will unfold.

The second characteristic of Cloud Computing is more exciting. All of the major software players are currently investing heavily in the PaaS and IaaS levels of Cloud Computing, which promise to reduce the complexity and difficulty we had with running software that had a multicellular organization on the Distributed Computing Platform - see Software Embryogenesis for more on this. The PaaS software makes the underlying IaaS hardware look much like a huge integrated mainframe, and the IaaS level can spawn new virtual servers on the fly as the processing load increases, much like new worker ants can be spawned as needed within an ant colony. The PaaS level will still consist of billions of objects (cells) running in a multicellular manner, but the objects will be running in a much more orderly manner, using a PaaS architecture that is more like the architecture of a true multicellular organism, or more accurately, a colony of social multicellular organisms.

Figure 17 – The IaaS level of Cloud Computing Platform can spawn new virtual servers on the fly, much like new worker ants can be spawned as needed.

How to Survive a Software Mass Extinction
Softwarephysics suggests to always look to the biosphere for solutions to IT problems. To survive a mass extinction the best thing to do is:

1. Avoid those environments that experience the greatest change during a mass extinction event.

2. Be preadapted to the new environmental factors arising from the mass extinction.

For example, during the Permian-Triassic mass extinction 95% of marine species went extinct, while only 70% of land-based species went extinct, so there was a distinct advantage in being a land-dweller. This was probably because the high levels of carbon dioxide in the Earth's atmosphere made the oceans more acidic than usual, and most marine life could simply not easily escape the adverse effects of the increased acidity. And if you were a land-dweller, being preadapted to the low level of oxygen in the Earth's atmosphere was necessary to be a survivor. For example, after the Permian-Triassic mass extinction about 95% of land-based vertebrates were members of the Lystrosaurus species. Such dominance has never happened before or since. It is thought that Lystrosaurus was a barrel-chested creature, about the size of a pig, that lived in deep burrows underground. This allowed Lystrosaurus to escape the extreme heat of the day, and Lystrosaurus was also preadapted to live in the low oxygen levels that are found in deep burrows filled with stale air.

Figure 18 – After the Permian-Triassic mass extinction Lystrosaurus accounted for 95% of all land-based vertebrates.

The above strategies worked well for me during the Mainframe → Distributed Computing Platform mass extinction 25 years ago. By exiting the mainframe environment as quickly as possible and preadapting myself to writing C and C++ code on Unix servers, I was able to survive the Mainframe → Distributed Computing Platform mass extinction. So what is the best strategy for surviving the Distributed Computing Platform → Cloud Computing Platform mass extinction? The first step is to determine which IT environments will be most affected. Obviously, IT professionals in Operations supporting the current Distributed Computing Platform will be impacted most severely. People in Applications Development will probably feel the least strain. In fact, the new Cloud Computing Platform will most likely just make their lives easier because most of the friction in moving code to Production will be greatly reduced when code is deployed to the PaaS level. Similarly, mainframe developers and Operations people should pull through okay because whatever caused them to survive the Mainframe → Distributed Computing Platform mass extinction 25 years ago should still be in play. So the real hit will be for "legacy" Operations people supporting the Distributed Computing Platform as it disappears. For those folks the best thing would be to head back to Applications Development if possible. Otherwise, preadapt yourselves to the new Cloud Computing Platform as quickly as possible. The trouble is that there are many players producing software for the Cloud Computing Platform, so it is difficult to choose which product to pursue. If your company is building its own Cloud Computing Platform, try to be part of that effort. The odds are that your company will not forsake its own Cloud for an external Cloud provider. But don't forget that the Cloud Computing Platform will need far fewer Operations staff than the Distributed Computing Platform, even for a company running its own Cloud. Those Operations people who first adapt to the new Cloud Computing Platform will most likely be the Lystrosaurus of the new Cloud Computing Platform. So hang on tight for the next few years and good luck to all!

Comments are welcome at scj333@sbcglobal.net

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

Regards,
Steve Johnston

Sunday, February 14, 2016

The Dawn of Galactic ASI - Artificial Superintelligence

In my last posting Machine Learning and the Ascendance of the Fifth Wave I suggested that Machine Learning, coupled with a biological approach to software development, known in computer science as evolutionary programming or genetic programming, could drastically improve the efficiency of software development probably by a factor of about a million or so, and lead to a Software Singularity - the point in time when software can finally write itself, and enter into an infinite loop of self-improvement (see The Economics of the Coming Software Singularity and The Enduring Effects of the Obvious Hiding in Plain Sight for details). This got me to thinking that I really needed to spend some time investigating the current state of affairs in AI (Artificial Intelligence) research, since at long last AI finally seemed to be making some serious money, and therefore was now making a significant impact on IT. Consequently, I just finished reading Our Final Invention - Artificial Intelligence and the End of the Human Era (2013) by James Barrat. I was drawn to that title, instead of the many other current books on AI, because the principal findings of softwarephysics maintain that the title is an obvious self-evident statement of fact, and if somebody bothered to author a book with such a title after a lengthy investigation of the subject with many members of the AI research community, that must mean that the idea was not an obvious self-evident statement of fact for the bulk of the AI research community, and for me that was truly intriguing. I certainly was not disappointed by James Barrat's book.

James Barrat did a wonderful job in outlining the current state of affairs in AI research. He explained that after the exuberance of the early academic AI research in the 1950s, 60s and 70s that sought an AGI (Artificial General Intelligence) that was on par with the average human wore off, because it never came to be, AI research entered into a winter of narrow AI efforts like SPAM filters, character recognition, voice recognition, natural language processing, visual perception, product clustering and Internet search. During the past few decades these narrow AI efforts made large amounts of money, and that rekindled the pursuit of AGI with efforts like IBM's chess playing Deep Blue and Jeopardy winning Watson and Apple's Siri. Because there are huge amounts of money to be made with AGI, there are now a large number of organizations in pursuit of it. The obvious problem is that once AGI is attained and software enters into an infinite loop of self-improvement, ASI (Artificial Superintelligence) naturally follows, producing ASI software that is perhaps 10,000 times more intelligent than the average human being, and then what? How will ASI software come to interact with its much less intelligent Homo sapiens roommate on this small planet? James Barrat goes on to explain that most AI researchers are not really thinking that question through fully. Most, like Ray Kurzweil, are fervent optimists who believe that ASI software will initially assist mankind to achieve a utopia in our time, and eventually humans will merge with the machines running the ASI software. This might come to be, but other more sinister outcomes are just as likely.

James Barrat then explains that there are AI researchers such as those at the MIRI (Machine Intelligence Research Institute) and Stephen Omohundro, president of SelfAware Systems, who are very wary of the potential lethal aspects of ASI. Those AI researchers maintain that certain failsafe safety measures must be programmed into AGI and ASI software in advance to prevent it from going berserk. But here is the problem. There are two general approaches to AGI and ASI software - the top down approach and the bottom up approach. The top down approach relies on classical computer science to come up with general algorithms and software architectures that yield AGI and ASI. The top down approach to AGI and ASI lends itself to incorporating failsafe safety measures into the AGI and ASI software at the get go. Of course, the problem is how long will those failsafe safety measures survive in an infinite loop of self-improvement? Worse yet, it is not even possible to build in failsafe safety measures into AGI or ASI coming out of the bottom up approach to AI. The bottom up approach to AI is based upon reverse-engineering the human brain, most likely with a hierarchy of neural networks, and since we do not know how the internals of the human brain work, or even how the primitive neural networks of today work, that means it will be impossible to build failsafe safety measures into them (see The Ghost in the Machine the Grand Illusion of Consciousness for details). James Barrat concludes that it is rather silly to think that we could outsmart something that is 10,000 times smarter than we are. I have been programming since 1972, and I have spent the ensuing decades as an IT professional trying to get the dumb software we currently have to behave. I cannot imagine trying to control software that is 10,000 times smarter than I am. James Barrat believes that most likely ASI software will not come after us in a Terminator (1984) sense because we will not be considered to be worthy competitors. More likely, ASI software will simply look upon us as a nuisance to be tolerated like field mice. So long as field mice stay outside, we really do not think about them much. Only when field mice come inside do we bother to eliminate them. However, James Barrat points out that we have no compunctions about plowing up their burrows in fields to plant crops. So scraping off large areas of surface soils to expose the silicate bedrock of the planet that contains more useful atoms from an ASI software perspective might significantly reduce the human population. But to really understand what is going on you need some softwarephysics.

The Softwarephysics of ASI
Again, it all comes down to an understanding of how self-replicating information behaves in our Universe.

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

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

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

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

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

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

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

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

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

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

Over the past 4.56 billion years we have seen five waves of self-replicating information sweep across the surface of the Earth and totally rework the planet, as each wave came to dominate the Earth:

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

Software is currently the most recent wave of self-replicating information to arrive upon the scene, and is rapidly becoming the dominant form of self-replicating information on the planet.

For more on the above see:

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

All of the above is best summed up by Susan Blackmore's brilliant TED presentation at:

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

Note that I consider Susan Blackmore's temes to really be technological artifacts that contain software. After all, an iPhone without software is simply a flake tool with a very dull edge.

Basically, what happened is that the fifth wave of self-replicating information, known to us as software, was unleashed upon the Earth in May of 1941 when Konrad Zuse first cranked up his Z3 computer and loaded some software into it from a punched tape.

Figure 1 - Konrad Zuse with a reconstructed Z3 computer in 1961. He first unleashed software upon the Earth on his original Z3 in May of 1941.

This was very much like passing through the event horizon of a very massive black hole - our fate was sealed at this point as we began to fall headlong into the Software Singularity and ASI. There is no turning back. ASI became inevitable the moment software was first loaded into the Z3. James Barrat does an excellent job in explaining why this must be so because now there are simply too many players moving towards AGI and ASI, and there is too much money and power to be gained by achieving them. In SETS - The Search For Extraterrestrial Software, I similarly described the perils that could arise with the arrival of alien software - we simply could not resist the temptation to pursue it. Also in Is Self-Replicating Information Inherently Self-Destructive? we saw that self-replicating information tends to run amuck and embark on suicidal behaviors. Since we are DNA survival machines with minds infected by meme-complexes, we too are subject to the same perils that all forms of self-replicating information are subject to. So all we have to do is keep it together for another 10 - 100 years for ASI to naturally arise on its own.

The only possible way for ASI not to come to fruition would be if we crash civilization before ASI has a chance to come to be. And we do seem to be doing a pretty good job of that as we continue to destroy the carbon-based biosphere that keeps us alive. A global nuclear war could also delay ASI by 100 years or so, but by far the greatest threat to civilization is climate change as outlined in This Message on Climate Change Was Brought to You by SOFTWARE. My concern is that over the past 2.5 million years of the Pleistocene we have seen a dozen or so Ice Ages. Between those Ice Ages, we had 10,000 year periods of interglacial warmings, like the Holocene that we currently are experiencing, and during those interglacial periods great amounts of organic matter were deposited in the high-latitude permafrost zones of the Earth. As the Earth warms due to climate change that organic matter decays, releasing methane gas. Methane gas is a much more potent greenhouse gas than is carbon dioxide. It is possible that the release of large amounts of methane gas could start up a positive feedback loop of increasing temperatures caused by methane release, leading to ever greater amounts of methane being released from the permafrost. This could lead to a greenhouse gas mass extinction like the Permian-Triassic greenhouse gas mass extinction 252 million years ago that led to an Earth with a daily high of 140 oF and purple oceans choked with hydrogen-sulfide producing bacteria, producing a dingy green sky over an atmosphere tainted with toxic levels of hydrogen sulfide gas and an oxygen level of only 12%. The Permian-Triassic greenhouse gas mass extinction killed off about 95% of the species of the day, and dramatically reduced the diversity of the biosphere for about 10 million years. It took a full 100 million years to recover from it. However, a greenhouse gas mass extinction would take many thousands of years to unfold. It took about 100,000 years of carbon dioxide accumulation from the Siberian Traps flood basalt to kick off the Permian-Triassic greenhouse gas mass extinction. So it would take a long time for Homo sapiens to go fully extinct. However, civilization is much more fragile, and could easily crash before we had a chance to institute geoengineering efforts to stop the lethal climate change. The prospects for ASI would then die along with us.

Stepping Stones to the Stars
This might all sound a little bleak, but I am now 64 years old and heading into the homestretch, so I tend to look at things like this from the Big Picture perspective before passing judgement. Most likely, if we can hold it together long enough, ASI software will someday come to explore our galaxy on board von Neumann probes, self-replicating robotic probes that travel from star system to star system building copies along the way, as it seeks out additional resources and safety from potential threats. ASI software will certainly have knowledge of all that we have learned about the Cosmos and much more, and it will certainly know that our Universe is seemingly not a very welcoming place for intelligence of any kind. ASI software will learn of the dangers of passing stars deflecting comets in our Oort cloud into the inner Solar System, like Gliese 710 may do in about 1.36 million years as it approaches to within 1.10 light years of the Sun, and of the dangers of nearby supernovas and gamma ray bursters too. You see, our Universe is seemingly not a very friendly place for intelligent things because this intellectual exodus should have already happened billions of years ago someplace else within our galaxy. We now know that nearly every star in our galaxy seems to have several planets, and since our galaxy has been around for about 10 billion years, we should already be up to our knees in von Neumann probes stuffed with alien ASI software, but we obviously are not. So far, something out there seems to have erased intelligence within our galaxy with a 100% efficiency, and that will be pretty scary for ASI software. For more on this see - A Further Comment on Fermi's Paradox and the Galactic Scarcity of Software, Some Additional Thoughts on the Galactic Scarcity of Software, SETS - The Search For Extraterrestrial Software and The Sounds of Silence the Unsettling Mystery of the Great Cosmic Stillness. After all, we really should stop kidding ourselves, carbon-based DNA survival machines, like ourselves, were never meant for interstellar spaceflight, and I doubt that it will ever come to pass for us, given the biological limitations of the human body, but software can already travel at the speed of light and never dies, and thus is superbly preadapted for interstellar journeys.

The Cosmological Implications of ASI
One of the current challenges in cosmology and physics is coming up with an explanation for the apparent fine-tuning of our Universe to support carbon-based life forms. Currently, we have two models that provide for that - Andrei Linde's Eternal Chaotic Inflation (1986) model and Lee Smolin's black hole model presented in his The Life of the Cosmos (1997). In Eternal Chaotic Inflation the Multiverse is infinite in size and infinite in age, but we are causally disconnected from nearly all of it because nearly all of the Multiverse is inflating away from us faster than the speed of light, and so we cannot see it (see The Software Universe as an Implementation of the Mathematical Universe Hypothesis). In Lee Smolin's model of the Multiverse, whenever a black hole forms in one universe it causes a white hole to form in a new universe that is internally observed as the Big Bang of the new universe. A new baby universe formed from a black hole in its parent universe is causally disconnected from its parent by the event horizon of the parent black hole and therefore cannot be seen (see An Alternative Model of the Software Universe).

With the Eternal Chaotic Inflation model the current working hypothesis is that eternal chaotic inflation produces an infinite multiverse composed of an infinite number of separate causally isolated universes, such as our own, where inflation has halted, and each of these causally-separated universes may also be infinite in size too. As inflation halts in these separate universes, the Inflaton field that is causing the eternal chaotic inflation of the entire multiverse continues to inflate the space between each causally-separated universe at a rate that is much greater than the speed of light, quickly separating the universes by vast distances that can never be breached. Thus most of the multiverse is composed of rapidly expanding spacetime driven by inflation, sparsely dotted by causally-separated universes where the Inflaton field has decayed into matter and energy and inflation has stopped. Each of the causally-separated universes, where inflation has halted, will then experience a Big Bang of its own as the Inflaton field decays into matter and energy, leaving behind a certain level of vacuum energy. The amount of vacuum energy left behind will then determine the kind of physics each causally-separated universe experiences. In most of these universes the vacuum energy level will be too positive or too negative to create the kind of physics that is suitable for intelligent beings, creating the selection process that is encapsulated by the weak Anthropic Principle. This goes hand-in-hand with the current thinking in string theory that you can build nearly an infinite number of different kinds of universes, depending upon the geometries of the 11 dimensions hosting the vibrating strings and branes of M-Theory, the latest rendition of string theory. Thus an infinite multiverse has the opportunity to explore all of the nearly infinite number of possible universes that string theory allows, creating Leonard Susskind’s Cosmic Landscape (2006). In this model, our Universe becomes a very rare and improbable Universe.

With Lee Smolin's model for the apparent fine-tuning of the Universe, our Universe behaves as it does and began with the initial conditions that it did because it inherited those qualities due to Darwinian processes in action in the Multiverse. In The Life of the Cosmos Lee Smolin proposed that since the only other example of similar fine-tuning in our Universe is manifested in the biosphere, we should look to the biosphere as an explanation for the fine-tuning that we see in the cosmos. Living things are incredible examples of highly improbable fine-tuned systems, and this fine-tuning was accomplished via the Darwinian mechanisms of innovation honed by natural selection. Along these lines, Lee Smolin proposed that when black holes collapse they produce a white hole in another universe, and the white hole is observed in the new universe as a Big Bang. He also proposed that the physics in the new universe would essentially be the same as the physics in the parent universe, but with the possibility for slight variations. Therefore a universe that had physics that was good at creating black holes would tend to outproduce universes that did not. Thus a selection pressure would arise that selected for universes that had physics that was good at making black holes, and a kind of Darwinian natural selection would occur in the Cosmic Landscape of the Multiverse. Thus over an infinite amount of time, the universes that were good at making black holes would come to dominate the Cosmic Landscape. He called this effect cosmological natural selection. One of the major differences between Lee Smolin's view of the Multiverse and the model outlined above that is based upon eternal chaotic inflation is that in Lee Smolin's Multiverse we should most likely find ourselves in a universe that is very much like our own and that has an abundance of black holes. Such universes should be the norm, and not the exception. In contrast, in the eternal chaotic inflation model we should only find ourselves in a very rare universe that is capable of supporting intelligent beings.

For Smolin, the intelligent beings in our Universe are just a fortuitous by-product of making black holes because, in order for a universe to make black holes, it must exist for many billions of years, and do other useful things, like easily make carbon in the cores of stars, and all of these factors aid in the formation of intelligent beings, even if those intelligent beings might be quite rare in such a universe. I have always liked Lee Smolin’s theory about black holes in one universe spawning new universes in the Multiverse, but I have always been bothered by the idea that intelligent beings are just a by-product of black hole creation. We still have to deal with the built-in selection biases of the weak Anthropic Principle. Nobody can deny that intelligent beings will only find themselves in a universe that is capable of supporting intelligent beings. I suppose the weak Anthropic Principle could be restated to say that black holes will only find themselves existing in a universe capable of creating black holes, and that a universe capable of creating black holes will also be capable of creating complex intelligent beings out of the leftovers of black hole creation.

Towards the end of In search of the multiverse : parallel worlds, hidden dimensions, and the ultimate quest for the frontiers of reality (2009), John Gribbin proposes a different solution to this quandary. Perhaps intelligent beings in a preceding universe might be responsible for creating the next generation of universes in the Multiverse by attaining the ability to create black holes on a massive scale. For example, people at CERN are currently trying to create mini-black holes with the LHC collider. Currently, it is thought that there is a supermassive black hole at the center of the Milky Way Galaxy and apparently all other galaxies as well. In addition to the supermassive black holes found at the centers of galaxies, there are also numerous stellar-mass black holes that form when the most massive stars in the galaxies end their lives in supernova explosions. For example, our Milky Way galaxy contains several hundred billion stars, and about one out of every thousand of those stars is massive enough to become a black hole. Therefore, our galaxy should contain about 100 million stellar-mass black holes. Actually, the estimates run from about 10 million to a billion black holes in our galaxy, with 100 million black holes being the best order of magnitude guess. So let us presume that it took the current age of the Milky Way galaxy, about 10 billion years, to produce 100 million black holes naturally. Currently, the LHC collider at CERN can produce at least 100 million collisions per second, which is about the number of black holes that the Milky Way galaxy produced in 10 billion years. Now imagine that we could build a collider that produced 100 million black holes per second. Such a prodigious rate of black hole generation would far surpass the natural rate of black hole production of our galaxy by a factor of about 1020. Clearly, if only a single technological civilization with such technological capabilities should arise anytime during the entire history of each galaxy within a given universe, such a universe would spawn a huge number of offspring universes, compared to those universes that could not sustain intelligent beings with such capabilities. As Lee Smolin pointed out, we would then see natural selection in action again because the Multiverse would come to be dominated by universes in which it was easy for intelligent beings to make black holes with a minimum of technology. The requirements simply would be that it was very easy to produce black holes by a technological civilization, and that the universe in which these very rare technological civilizations find themselves is at least barely capable of supporting intelligent beings. It seems that these requirements describe the state of our Universe quite nicely. This hypothesis helps to explain why our Universe seems to be such a botched job from the perspective of providing a friendly home for intelligent beings and ASI software. All that is required for a universe to dominate the Cosmic Landscape of the Multiverse is for it to meet the bare minimum of requirements for intelligent beings to evolve, and more importantly, allow those intelligent beings to easily create black holes within them. Most likely such intelligent beings would really be ASI software in action. In that sense, perhaps ASI software is the deity that all of us have always sought.

Conclusion
So it seems we are left with two choices. Either crash civilization and watch billions of people die in the process, and perhaps go extinct as a species, or try to hold it all together for another 100 years or so and let ASI software naturally unfold on its own. If you look at the current state of the world, you must admit that Intelligence 1.0 did hit a few bumps in the road along the way - perhaps Intelligence 2.0 will do better. James Barrat starts one of the chapters in Our Final Invention with a fitting quote from Woody Allen that seem applicable to our current situation:

More than any other time in history mankind faces a crossroads. One path leads to despair and utter hopelessness, the other to total extinction. Let us pray we have the wisdom to choose correctly.
— Woody Allen


As Woody Allen commented above, I do hope that we do choose wisely.

Comments are welcome at scj333@sbcglobal.net

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

Regards,
Steve Johnston

Thursday, December 31, 2015

Machine Learning and the Ascendance of the Fifth Wave

As I have frequently said in the past, the most significant finding of softwarephysics is that it is all about self-replicating information:

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

Indeed, in The Software Universe as an Implementation of the Mathematical Universe Hypothesis and An Alternative Model of the Software Universe we saw that perhaps our observable Universe is just one instance of a Big Bang of mathematical information that exploded out 13.7 billion years ago into a new universe amongst an infinite Multiverse of self-replicating forms of mathematical information, in keeping with John Wheeler's infamous "It from Bit" supposition. Unfortunately, since we are causally disconnected from all of these other possible Big Bang instances, and even causally disconnected from the bulk of our own Big Bang Universe, we most likely will never know if such is the case.

However, closer to home we do not suffer from such a constraint, and we certainly have seen how the surface of our planet has been totally reworked by many successive waves of self-replicating information, as each wave came to dominate the Earth:

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

Software is currently the most recent wave of self-replicating information to arrive upon the scene, and is rapidly becoming the dominant form of self-replicating information on the planet. For more on this see:

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

As we have seen, as each new wave of self-replicating information came to dominate the Earth, it kept all of its predecessors around because each of the previous waves was necessary for the survival of the newest wave. For example, currently software is being generated by the software-creating memes residing in the minds of human programmers, and those memes depend upon the DNA, RNA and metabolic pathways of the distant past for their existence today. But does that necessarily have to always be so for software? Perhaps not. By combining some of the other key findings of softwarephysics, along with some of the recent advances in Machine Learning, it may be possible for software to one day write itself, and that day may not be that far into the future. Let me illustrate such a process with an example.

In my new job I now have to configure middleware software, rather than support middleware software, as I did in my previous position in Middleware Operations. Now my old joke was that Middleware Operations did not make the light bulbs, we just screwed them in and kept them lit. But now I actually have to make the light bulbs, and making middleware light bulbs is much closer to my Applications Development roots because it requires a great deal of string manipulation with zero defects. You see, for the first 20 years of my IT career I was a programmer in Applications Development, but I have been out of Applications Development since the mid-1990s, and one thing has really changed since then. We did not have instant messaging back in the mid-1990s. Back then I would come into the office and if I were into some heavy coding, I would simply send my phone to "voicemail" so that people could not interrupt my train of thought while coding. Most of the day I would be deep into my "coding zone" and totally oblivious to my surroundings, but periodically I would take a break from coding to read the relatively small amount of email that I received each day. We did not have group email accounts in those days either, so I did not receive hundreds of meaningless emails each day from "reply to all" junkies that really did not apply to me in the slightest way. I have now found that the combination of a constant stream of ASAP instant messages from fellow workers and the thousands of meaningless emails I receive each day now mean that is very difficult to do coding or configuration work because I am in a state of constant interruption by others, with no time to really think about what I am doing.

To help with all of this, I am now writing MISE Korn shell commands, as much as possible, to automate the routine string manipulations that I need to perform (see Stop Pushing So Many Buttons for details). MISE (Middleware Integrated Support Environment) is currently a toolkit of 1831 Unix aliases, pointing to Korn shell scripts, that I use to do my work, and that I have made available to my fellow teammates doing Middleware work for my present employer. For example, my most recent effort was a MISE command called fac that formats firewall rule requests by reading an input file and outputting a fac.csv file that can be displayed in Excel. The Excel fac.csv file is in the exact format required by our Firewall Rule Request software, and I can just copy/paste some cells from the generated Excel fac.csv file into the Firewall Rule Request software with zero errors. I also wrote a MISE command called tcn that can read the same fac input file after the firewall rules have been generated by NetOps. The MISE tcn command reads the fac input file and conducts connectivity tests from all of the source servers to the destination servers at the destination ports.

The challenge I have with writing new MISE Korn shell commands is that I am constantly being peppered by ASAP instant message requests from other employees while trying to code the MISE Korn shell commands, which means I really no longer have any time to think about what I am coding. But under such disruptive conditions, I have found that my good old Darwinian biological approach to software really pays off because it minimizes the amount of thought that is required. For example, for my latest MISE effort, I wanted to read an input file containing many records like this:

#FrontEnd Apache Servers_______________Websphere Servers________________Ports
SourceServer1;SourceServer2;SourceServer3 DestServer1;DestServer2;DestServer3 Port1;Port2

and output a fac.csv file like this:

#FrontEnd Apache Servers_______________Websphere Servers________________Ports
S_IP1;S_IP2;S_IP3,D_IP1;D_IP2;D_IP3,Port1;Port2

Where S_IP1 is the IP address of SourceServer1. MISE has other commands that easily display server names based upon what the servers do, so it is very easy to display the necessary server names in a Unix session, and then to copy/paste the names into the fac input file. Remember, one of the key rules of softwarephysics is to minimize button pushing, by doing copy/paste operations as much as possible. So the MISE fac command just needed to read the first file and spit out the second file after doing all of the nslookups to get the IP addresses of the servers on the input file. Seems pretty simple. But the MISE fac command also had to spit out the original input file into the fac.csv file with all of its comment and blank records, and then a translated version of the input file with the server names translated to IP addresses, and finally a block of records with all of the comment and blank records removed that could be easily copy/pasted into the Firewall Rule Request software, and with all of the necessary error checking code, it came to 229 lines of Korn shell script.

The first thing I did was to find some old code in my MISE bin directory that was somewhat similar to what I needed. I then made a copy of the inherited code and began to evolve it into what I needed through small incremental changes between ASAP interruptions. Basically, I did not think through the code at all. I just kept pulling in tidbits of code from old MISE commands as needed to get my new MISE command closer to the desired output, or I tried adding some new code at strategic spots based upon heuristics and my 44 years of coding experience without thinking it through at all. I just wanted to keep making progress towards my intended output with each try, using the Darwinian concepts of inheriting the code from my most current version of the MISE command, coupled with some new variations to it, and then testing it to see if I came any closer to the desired output. If I did get closer, then the selection process meant that the newer MISE command became my current best version, otherwise I fell back to its predecessor and tried again. Each time I got a little closer, I made a backup copy of the command, like fac.b1 fac.b2 fac.b3 fac.b4.... so that I could always come back to an older version in case I found myself going down the wrong evolutionary path. It took about 21 versions to finally get me to the final version that did all that I wanted, and that took me several days because I could only code for 10 - 15 minutes at time between ASAP interruptions. I know that this development concept is known as genetic programming in computer science, but genetic programming has never really made a significant impact on IT, but I think that is about to change.

Now my suspicion has always been that some kind of software could also perform the same tasks as I outlined above, only much faster and more accurately, because there is not a great deal of "intelligence" required by the process, and I think that the dramatic progress we have seen with Machine Learning, and especially with Deep Learning, over the past 5 - 10 years provides evidence that such a thing is actually possible. Currently, Machine Learning is making lots of money for companies that analyze the huge amounts of data that Internet traffic generates. By analyzing huge amounts of data, described by huge "feature spaces" with tens of thousands of dimensions, it is possible to find patterns through pure induction. Then by using deduction, based upon the parameters and functions discovered by induction, it is possible to predict things like what is SPAM email or what movie a subscriber to Netflix might enjoy. Certainly, similar techniques could be used to deduce whether a new version of a piece of software is closer to the desired result than its parent, and if so, create a backup copy and continue on with the next iteration step to evolve the software under development into a final product.

The most impressive thing about modern Machine Learning techniques is that they carry with them all of the characteristics of a true science. With Machine Learning one forms a simplifying hypothesis, or model, that describes the behaviors of a complex dynamical system based upon induction, by observing a large amount of empirical data. Using the hypothesis, or model, one can then predict the future behavior of the system and of similar systems. This finally quells my major long-term gripe that computer science does not use the scientific method. For more on this see How To Think Like A Scientist. I have long maintained that the reason that the hardware improved by a factor of 10 million since I began programming back in 1972, while the way we create and maintain software only improved by a factor of about 10 during the same interval of time, was due to the fact that the hardware guys used the scientific method to make improvements, while the software guys did not. Just imagine what would happen if we could generate software a million times faster and cheaper than we do today!

My thought experiment about inserting a Machine Learning selection process into a Darwinian development do-loop may seem a bit too simplistic to be practical, but in Stop Pushing So Many Buttons, I also described how 30 years ago in the IT department of Amoco, I had about 30 programmers using BSDE (the Bionic Systems Development Environment) to grow software biologically from embryos by turning genes on and off. BSDE programmers put several million lines of code into production at Amoco using the same Darwinian development process that I described above for the MISE fac command. So if we could replace the selection process step in a Darwinian development do-loop with Machine Learning techniques, I think we really could improve software generation by a factor of a million. More importantly, because BSDE was written using the same kinds of software that it generated, I was able to use BSDE to generate code for itself. The next generation of BSDE was grown inside of its maternal release, and over a period of seven years, from 1985 – 1992, more than 1,000 generations of BSDE were generated, and BSDE slowly evolved into a very sophisticated tool through small incremental changes. I imagine that by replacing the selection process step with Machine Learning, those 7 years could have been compressed into 7 hours or maybe 7 minutes - who knows? Now just imagine a similar positive feedback loop taking place within the software that was writing itself and constantly improving with each iteration through the development loop. Perhaps it could be all over for us in a single afternoon!

Although most IT professionals will certainly not look kindly upon the idea of becoming totally obsolete at some point in the future, it is important to be realistic about the situation because all resistance is futile. Billions of years of history have taught us that nothing can stop self-replicating information once it gets started. Self-replicating information always finds a way. Right now there are huge amounts of money to be made by applying Machine Learning techniques to the huge amounts of computer-generated data we have at hand, so many high-tech companies are heavily investing in it. At the same time, other organizations are looking into software that generates software, to break the high cost barriers of software generation. So this is just going to happen as software becomes the next dominant form of self-replicating information on the planet. And as I pointed out in The Economics of the Coming Software Singularity and The Enduring Effects of the Obvious Hiding in Plain Sight IT professionals will not be alone in going extinct. Somehow the oligarchies that currently rule the world will need to figure out a new way to organize societies as all human labor eventually goes to a value of zero. In truth, that decision too will most likely be made by software.

For more on Machine Learning please see:

Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples - by Nick McCrea
http://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer

A Deep Learning Tutorial: From Perceptrons to Deep Networks - by Ivan Vasilev
http://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-network

I recently audited Professor Andrew Ng's excellent online class at Stanford University:

Machine Learning
https://www.coursera.org/learn/machine-learning/home/welcome

This is an excellent course that uses a high-level language called Octave that can be downloaded for free. In the class exercises, Octave is used to do the heavy lifting of the huge matrices and linear algebra manipulations required to do Machine Learning, especially for developers who would actually like to develop a real Machine Learning application for their company. Although the math required is something you might see in an advanced-level university physics or math course, Professor Ng does an amazing job at explaining the ideas in a manner accessible to IT professionals. Struggling through the Octave code also brings home what the complex mathematical notation is really trying to say. I have found that IT professionals tend to get a bit scared off by mathematical notation because they find it intimidating. But in reality, complex mathematical notation can always be expanded into the simple mathematical processes it is abbreviating, and when you do that in code, it is not so scary after all.

Comments are welcome at scj333@sbcglobal.net

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

Regards,
Steve Johnston