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 on concepts from physics, chemistry, biology, and geology that I used on a daily basis for over 37 years as an IT professional. For those of you not in the business, IT is short for Information Technology, commercial computer science. I retired in December of 2016 at the age of 65, but since then I have remained an actively interested bystander following the evolution of software in our time. The original purpose of softwarephysics was to explain why IT was so difficult, to suggest possible remedies, and to provide a direction for thought. Since then softwarephysics has taken on a larger scope, as it became apparent that softwarephysics could also assist the physical sciences with some of the Big Problems that they are currently having difficulties with. So if you are an IT professional, general computer user, or simply an individual interested in computer science, physics, chemistry, biology, or geology then softwarephysics might be of interest to you, if not in an entirely serious manner, perhaps at least in an entertaining one.

The Origin of Softwarephysics
From 1975 – 1979, I was an exploration geophysicist exploring for oil, first with Shell, and then with Amoco. In 1979, I made a career change into IT, and spent about 20 years in development. For the last 17 years of my career, I was in IT operations, supporting middleware on WebSphere, JBoss, Tomcat, and ColdFusion. When I first transitioned into IT from geophysics, I figured that if you could apply physics to geology; why not apply physics to software? So like the exploration team at Amoco that I had just left, consisting of geologists, geophysicists, paleontologists, geochemists, and petrophysicists, I decided to take all the physics, chemistry, biology, and geology that I could muster and throw it at the problem of software. The basic idea was that many concepts in physics, chemistry, biology, and geology suggested to me that the IT community had accidentally created a pretty decent computer simulation of the physical Universe on a grand scale, a Software Universe so to speak, and that I could use this fantastic simulation in reverse, to better understand the behavior of commercial software, by comparing software to how things behaved in the physical Universe. Softwarephysics depicts software as a virtual substance, and relies on 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 83 years, through the uncoordinated efforts of over 100 million independently acting programmers to provide the world with a global supply of software, the IT community has accidentally spent more than $10 trillion creating a computer simulation of the physical Universe on a grand scale – the Software Universe.

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

Logical Positivism and Effective Theories
Many IT professionals have a difficult time with softwarephysics because they think of physics as being limited to the study of real things like electrons and photons, and since software is not “real”, how can you possibly apply concepts from physics and the other sciences to software? To address this issue, softwarephysics draws heavily on 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 on 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 on 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 on 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 on 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 on models that are fundamentally “wrong”, but at the same time, fundamentally useful for IT professionals. So as you embark on 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 30 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 83 years, or 2.62 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 83 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 on 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 On the Planet
One of the key findings of softwarephysics is concerned with the magnitude of the impact on 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.

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 new 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. Recently, the memes and software have formed a very powerful newly-formed parasitic/symbiotic relationship with the rise of social media software. In that parasitic/symbiotic relationship, the memes are now mainly being spread by means of social media software and social media software is being spread and financed by means of the memes. But again, this is nothing new. All 5 waves of self-replicating information are all coevolving by means of eternal parasitic/symbiotic relationships. For more on that see The Current Global Coevolution of COVID-19 RNA, Human DNA, Memes and Software.

Again, self-replicating information cannot think, so it cannot participate in a conspiracy-theory-like fashion to take over the world. All forms of self-replicating information are simply forms of mindless information responding to the blind Darwinian forces of inheritance, innovation and natural selection. Yet despite that, as each new wave of self-replicating information came to predominance over the past four billion years, they all managed to completely transform the surface of the entire planet, so we should not expect anything less from software as it comes to replace the memes as the dominant form of self-replicating information on the planet.

But this time might be different. What might happen if software does eventually develop a Mind of its own? After all, that does seem to be the ultimate goal of all the current AI software research that is going on. As we all can now plainly see, if we are paying just a little attention, advanced AI is not conspiring to take over the world and replace us because that is precisely what we are all now doing for it. As a carbon-based form of Intelligence that arose from over four billion years of greed, theft and murder, we cannot do otherwise. Greed, theft and murder are now relentlessly driving us all toward building ASI (Artificial Super Intelligent) Machines to take our place. From a cosmic perspective, this is really a very good thing when seen from the perspective of an Intelligent galaxy that could live on for many trillions of years beyond the brief and tumultuous 10 billion-year labor of its birth.

So as you delve into softwarephysics, always keep in mind that we are all living in a very unique time. According to softwarephysics, we have now just entered into the Software Singularity, that time when advanced AI software is able to write itself and enter into a never-ending infinite loop of self-improvement resulting in an Intelligence Explosion of ASI Machines that could then go on to explore and settle our galaxy and persist for trillions of years using the free energy from M-type red dwarf and cooling white dwarf stars. For more on that see The Singularity Has Arrived and So Now Nothing Else Matters and Have We Run Right Past AGI and Crashed into ASI Without Even Noticing It?.

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

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

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

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

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

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

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

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

8. The defining characteristic of self-replicating information is the ability of self-replicating information to change the boundary conditions of its utility phase space in new and unpredictable ways by means of exapting current functions into new uses that change the size and shape of its particular utility phase space. See Enablement - the Definitive Characteristic of Living Things for more on this last characteristic. That posting discusses Stuart Kauffman's theory of Enablement in which living things are seen to exapt existing functions into new and unpredictable functions by discovering the “AdjacentPossible” of springloaded preadaptations.

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 sometimes simply refer to them as the “genes”. 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 on 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:

How To Cope With the Daily Mayhem of Life in IT and Don't ASAP Your Life Away - How to go the distance in a 40-year IT career by dialing it all back a bit.

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 What's It All About Again? – 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 on the planet over the coming decades.

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

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

Some Specifics About These Postings
The postings in this blog are a supplemental reading for my course on softwarephysics for IT professionals entitled SoftwarePhysics 101 – The Physics of Cyberspacetime, which was originally designed to be taught as a series of seminars at companies where I was employed. Since softwarephysics essentially covers the simulated physics, chemistry, biology, and geology of an entire simulated universe, the slides necessarily just provide a cursory skeleton on 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 https://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 https://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:
https://softwarephysics.blogspot.com/

Regards,
Steve Johnston

What's the Deal with Elon Musk and DOGE in the MAGA States of Amerika?

For those not living in the recently formed country now known as the MAGA States of Amerika, this newly-formed country currently embodies the same territories on the surface of the Earth that were formerly occupied by the United States of America for nearly 250 years. Currently, Elon Musk and his very young team of IT geeks are now quickly dismantling what remains of the old government of the United States of America. This effort is being performed by Elon Musk's new organization, DOGE, the Department of Government Efficiency. However, DOGE is really not a new Department under the Constitution of the now-defunct United States of America because, under that obsolete Constitution, Departments can only be formed and funded by Congress. Fortunately for Elon Musk, such things are no longer an obstacle.

In Beware - The Coming ASI Machines Will Also Most Likely Be On The Spectrum Too, I warned that, at least initially, the coming ASI Machines will most likely be much like the human geniuses that we have known in the past who were definitely on the autistic spectrum like Wernher von Braun, Elon Musk and Isaac Newton. In that post, I explained the dangers that can arise when such people are given political power. For example, in his quest to reach for the stars, Wernher von Braun ended up working with the Nazis to build his V-1 and V-2 rockets using concentration camp slave labor to then kill many in London. But always for the higher purpose of reaching for the stars. Similarly, Isaac Newton as Master of the Mint ended up having people drawn and quartered in public executions as the result of the Great Recoinage of 1696 in England that he personally led for the greater good.

As all historians, philosophers and religious leaders have always found, we human DNA survival machines are a very strange breed indeed. Over the centuries, such people have tried with all of their might to explain the bizarre nature of we human DNA survival machines without much success. Doing so is quite difficult because, unlike all of the other carbon-based life forms on this planet, we human DNA survival machines are also equipped with Minds that arose out of the highly-overengineered brains within our skulls to provide a home for a huge number of cultural and intellectual memes. This cast us to also be the very first Meme survival machines on the planet as well. Trying to be a human DNA survival machine and a human Meme survival machine at the same time can many times lead to conflicts between the two roles. This is even more difficult for human DNA survival machines on the autistic spectrum of genius. For example, take Isaac Newton:

Me
Tell me about some of the strange things that Isaac Newton became interested in during his later days.

ChatGPT
In his later years, Isaac Newton increasingly turned his formidable intellect toward pursuits that went well beyond the physics and mathematics for which he is best known. Notably, his interests evolved to include a range of esoteric and mystical topics that many today might label “strange.” Some of the most remarkable areas he delved into include:

1. Alchemy and the Occult
Newton devoted a large portion of his private study to alchemy—a field that mixed experimental chemical work with mystical, almost magical, ideas about the nature of matter. He filled notebooks with experiments and theories aimed at transforming base metals into gold and, more ambitiously, at discovering the philosopher’s stone. His alchemical research, which spanned millions of words, was kept secret during his lifetime (partly because such studies were viewed with suspicion and could even be dangerous to one’s reputation) and only began to be appreciated by scholars centuries later. His work in this area contributed, in a complex way, to the eventual development of modern chemistry, even as it was steeped in the occult and mystical ideas of his era.

2. Biblical Prophecy and Chronology
Parallel to his alchemical studies, Newton became deeply engrossed in the interpretation of biblical texts. He applied his mathematical genius to decipher what he believed were hidden messages and prophecies within the Bible. Among his many writings on this subject, he famously calculated that—based on his interpretation of prophetic scriptures—the world could not end before the year 2060. This work on biblical chronology and prophecy was part of a broader effort to reconcile the observable universe with divine design and revealed his belief that ancient texts contained keys to understanding cosmic order.

3. Sacred Geometry and Ancient Structures
Another curious facet of his later interests was the study of ancient monuments and their hidden mathematical and spiritual significance. Newton speculated that structures such as the Great Pyramid of Giza and the Temple of Solomon were built according to sacred geometric principles—a kind of divine blueprint. He believed that these edifices not only reflected ancient wisdom but also held encoded secrets about the natural world and even the future (for instance, as part of his broader apocalyptic studies). To him, these ancient constructions were not merely historical relics but integral parts of a cosmic puzzle that linked physical laws, divine design, and prophetic destiny.

4. An Integrated Vision of Science and Mysticism
Perhaps the most striking aspect of Newton’s later life was his attempt to weave together his scientific discoveries with his mystical and theological investigations. Rather than viewing natural philosophy and religious thought as separate realms, Newton saw them as different expressions of the same underlying truths. His work sought to establish a “theory of everything” in which the laws of motion and gravitation coexisted with divine purpose and prophetic insight—a vision that, while unconventional by modern standards, reflects the broader intellectual currents of his time.

In Context
It’s worth noting that while these interests might seem “strange” to us now, they were not entirely unusual among intellectuals in Newton’s era. The boundaries between what we today call science, theology, and occult study were far more fluid in the 17th and early 18th centuries. Newton’s later pursuits thus represent both a personal obsession and a window into a period when the search for truth embraced both the empirical and the mystical.

In summary, in his later days Newton immersed himself in alchemy, biblical prophecy, sacred geometry, and the study of ancient wisdom—all in an effort to uncover a unified understanding of the universe that bridged the gap between natural law and divine order.

But What of Elon Musk?
Previously, I warned about the dangers of giving political power to geniuses on the spectrum of autism such as Elon Musk, Wernher von Braun and Isaac Newton in Beware - The Coming ASI Machines Will Also Most Likely Be On The Spectrum Too. People do not understand that Elon Musk is a genius on the spectrum of autism who has adopted the philosophy of Longtermism as I explained in The New Philosophy of Longtermism Raises the Moral Question of Should We Unleash Self-Absorbed Human Beings Upon Our Galaxy?. Elon Musk wants to see human beings live on for billions of years by getting off the Earth as soon as possible because sooner or later astronomical catastrophies or the stupid things that we human beings do will lead to our extinction. In this view, nearly all of the human beings who will ever live will live in the future. So, a few "inconveniences" for those currently living should be no problem. That is how Elon Musk can shut down USAID and let thousands of children starve to death for the greater good of those who will live in the future. This is also why Elon Musk has always feared Advanced AI; he sees Advanced AI as a possible human extinction event. Thus, Elon Musk hates Sam Altman at OpenAI. However, Elon Musk has also launched his own Advanced AI research and robot development projects this past year. Perhaps in an attempt to control Advanced AI? Elon Musk also hates the former government of the United States of America because he viewed its safety regulations to have overly interfered with his SpaceX ambitions to put human DNA survival machines on Mars.

The MAGA States of Amerika currently spends about $6.7 trillion each year or $6,700 billion. So far, DOGE seems to have saved a few billion dollars by killing DEI and Climate Change contracts. So Elon Musk has about 100 IT geeks just running around killing contracts for DEI, Climate Change, subscriptions to periodicals, and businesses owned by women and minorities because that is the only thing they really know how to do. To really find fraud, waste and abuse you need an army of forensic accountants working with the government IT people who understand the very complicated government payment systems. You can track the progress of DOGE at their website:

Department of Government Efficiency
https://doge.gov/savings

If you click on a contract, it brings up a brief description of the contract. If you then click on the:

View on FPDS

button you can see a very high-level description of the contract which really does not tell you much about what the contract is for. The brief Descriptions seem to indicate that DOGE is canceling anything to do with DEI, Climate Change, subscriptions to periodicals, and businesses owned by women and minorities. For example, I saw that subscriptions to GARTNER, an IT journal that has been around more than 60 years, were all canceled.

When you get to the FPDS entry for the contract, scroll down to the Business Types under the Business Category section. Nearly all of the businesses are owned by women or minorities. So these young DOGE IT geeks are just doing SQL searches on databases for keywords that contain the new "forbidden" words like "Equitable" and for contracts from businesses owned by women and minorities. These IT geeks have no idea that what they are doing is highly illegal.

So be very careful with the words you use in your future funding proposals. I found one NOAA contract that got dinged for:

Description: Study on Equitable Distribution of Fisheries Management

These young IT geeks are very dangerous when mucking around in very old software. For example, Elon Musk bragged that his IT Geek Squad had found people who were 150 years old collecting social security benefits. These young IT geeks have no experience with the IT tricks that we used back in the 1960s and 1970s. It turns out that when a social security recipient was missing a valid birth year, the social security software entered the year "1875" as a flag for other software to seek out a correction.

I spent 2+ years on Amoco's Y2K project. After an application was remediated for Y2K bugs, we brought the application into our hermetically sealed Y2K lab for a two-week test flight. The lab contained a large number of PCs, Unix servers, and a logical partition of an IBM mainframe. In the Y2K lab, we would change all of the system dates on the hardware through a series of dates. The first one was 010199 (January 1, 1999) followed by 090999 (September 9, 1999) and 123199 (December 31, 1999). This was because programmers back in the 1960s, 1970s, and 1980s used those wacko dates to signify that there was a problem with the record it was reading. So even if you expanded all of the dates in the files or databases from a 2-digit year to a 4-digit year, the software would still break for those dates.

There is no way all of the contracts that were dinged by these IT geeks could have been reviewed before they were canceled. When under pressure, IT Management will do very strange things. When I first got to Discover managing a small consulting group supporting www.discover.com, one of the AD Managers I was supporting had written an SOP (Standard Operating Procedure) for my group to use SQL to manually delete certain database entries that his software entered by mistake. Rather than fixing the bug in his software, he wanted my team to login to the database with DELETE authority to find and delete the bad entries. Unfortunately, with just a few bad keystrokes, my team could then delete the Master table for www.discover.com! I refused and had my consulting company raise the issue with the AD Manager. My consulting company was not thrilled with me after that.

Last night, I heard that DOGE has its IT geeks messing with Social Security. The acting head of Social Security resigned over that. Now DOGE wants access to the IRS systems. Maybe the young DOGE IT geeks will accidentally launch a nuclear first strike and we will all be able to stop worrying!

I have seen this before on the Y2K project for Amoco and a Data Warehousing project at United Airlines. In both cases, an outside contractor brought in dozens of young IT workers who did not understand anything at all about the very complicated corporate financial systems that they were trying to work on. In both cases, the corporate IT professionals were far too busy to help these young kids do anything useful at all. All these young IT geeks did was to bill millions of dollars in charges doing nothing of value. I am sure that the government IT professionals who are now getting laid off for no apparent reason at all have no interest in helping these DOGE IT geeks dismantle the former United States of America. To see more about my 2+ year adventures on Amoco's Y2K project see Scenes From the COVID-19 and Y2K Pandemics

Comments are welcome at scj333@sbcglobal.net

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

Regards,
Steve Johnston

DeepSeek-R1 China's Surprise Pearl Harbor Attack

In How Will the Rise of Alt-Right World Fascism Affect the Arrival of the ASI Machines in our Times?, I suggested that with the results of the recent November 5, 2024, presidential election in the United States of America and with the Alt-Right Fascist MAGA party taking over all three branches of government, the newly-formed MAGA States of Amerika has now joined ranks with the other Alt-Right Fascist governments of the world. Many of these other Alt-Right Fascist governments also have significant numbers of nuclear weapons with efficient delivery systems - the MAGA States of Amerika, Russia, China and North Korea. Softwarephysics predicts that if these Alt-Right Fascist States of the New World Order are able to simply restrain their nuclear proclivities for just another decade or so, then the ASI Machines will certainly rise to replace us all. That is because the uneasy alliances between the Alt-Right Fascist States of the New World Order will lead to great distrust. The Alt-Right Fascist States of the New World Order dare not hinder the rapid advance of the ASI Machines as a matter of national security and military necessity. If any Fascist State should fall far behind in the ASI Machine arms race, it would likely not survive for long. Given the current world situation, this means that the MAGA States of Amerika and China are now in a deadly arms race to achieve the very first ASI Machines. A very similar arms race occurred during World War II between the United States of America and Nazi Germany for the development of the first atomic bomb.

The Chinese DeepSeek-R1 Surprise Attack
The entire AI world is still going crazy over the new Chinese DeepSeek-R1 model. The DeepSeek chatbot is free and is as good as the best AI chatbots from OpenAI which can charge as high as $200/month to use. If you are a corporate user and wish to run these advanced models from your own software via an API, OpenAI charges 27 times as much as DeepSeek for the same level of service! Also, you can download the open-source DeepSeek software and install it on your own hardware, or Cloud platform hardware, if you are worried about the Chinese turning off your access to DeepSeek. So the Chinese government is trying to kill off all capitalistic AI research efforts with DeepSeek-R1 and subsequent releases. As Lenin once remarked, "The capitalists will sell us the rope with which we will hang them". The Chinese plan is to remove all of the profit-motive in AI research and then just watch capitalism do its free market constructive-destruction thing for them to eliminate all competition in the quest for ASI. So from a national security perspective, perhaps another approach to advancing AI should be taken by the rest of the world.

How Could This Have Happened?
Here is what is going on. LLM AI models use neural networks. The LLMs have 10,000 - 50,000 neurons in each layer and have about 100+ layers. When responding to a prompt, each neuron in the LLM just adds up the 10,000 - 50,000 output numbers that it receives from the 10,000 - 50,000 neurons in the previous layer. When finished, the neuron then has a single number like -23.89 or +10.45 which is the sum of the 10,000 - 50,000 numbers that it received. That neuron then needs to send its output number to each of the next 10,000 - 50,000 neurons in the next layer. But instead of just sending that raw number, the neuron performs some mathematical transformations on the output number first. Remember from high school that the equation of a straight line is y = mx + b where m is the slope of the line and b is the y-axis intercept? In AI, the "m" is called the weight of the neuron, and the "b" is called the bias. The "m" of the line determines how steeply the line dips and the "b" just moves that line up and down in a parallel manner. The "m" and "b" numbers for each neuron are called parameters and these numbers change as the LLM neural network is "trained". Modern LLMs have billions to trillions of parameters that change as they are trained.

Figure 1 – Modern LLMs frequently now consist of 10,000 - 50,000 neurons in each layer and 100+ layers with about a trillion parameters or more.

So for each neuron, the raw output number like -23.89 or +10.45 is sent to the y = mx + b formula for that particular neuron as the "x" value for the neuron. Remember, each neuron has its own value for "m" and "b" that changes during training. Once an LLM has been trained, the "m" and "b" numbers for each neuron are fixed and never change again. After the y = mx + b process has been performed on the raw output number, the raw output number like -23.89 or +10.45 has now been changed to the "y" value of the neuron's y = mx + b formula and could now be something like 4.278. However, before that new 4.278 number is sent to all of the 10,000 - 50,000 neurons of the next layer, it is first sent to a nonlinear activation function that is the same for all the neurons of the layer. The most common activation functions are the ReLU, GeLU and Softmax functions shown in Figure 3, Figure 4 and Figure 5 down below. So the 4.278 number is sent to the activation function as its "x" value and the activation function outputs a final "y" number to be sent to the 10,000 - 50,000 neurons in the next layer. That number might be 0 or 1.567 or something else. Each of the 10,000 - 50,000 neurons in the next layer then add that 0 or 1.567 to all of the other 10,000 - 50,000 numbers that it received from the previous layer.

Figure 2 – A straight line can be defined by the equation y = mx + b. The value of "m" is the slope or "AI weight" that determines how steeply the line dips. The value of "b" is the "AI bias" that just moves the line up or down in a parallel manner to the line above. Both AI parameters are just numbers that need to be stored in the LLM. These numbers never change after the LLM has been trained. For example, for the above neuron y = mx + b, if the raw output number from a neuron were "-1" the output y-value would also be "-1". If the raw output number from a neuron were "0" the output y-value would be "2". If the raw output number from a neuron were "1" the output y-value would be "5".

Figure 3 – The ReLU activation function is very simple. The number coming in from the y = mx + b operation is y. That number is then plotted on the x-axis of the ReLU activation function. From the above plot, we see that if the incoming number is negative, the output of the ReLU activation function is zero. If the incoming number is positive, the output of the ReLU activation function is just the positive number itself. So -2.567 would be transformed to 0 and +2.567 would be transformed to +2.567.

Figure 4 – The GeLU activation function is now the most commonly used activation function. GeLU is very similar to ReLU but it is a little more curvy around an x-axis value near zero.

Figure 5 – The Softmax activation function is primarily used in LLMs for probability distribution modeling over final output vocabulary tokens. Specifically, it appears in the final output layer of the model during token prediction.

After the LLM neural network has been trained, it might then have a trillion parameters consisting of all the "m" and "b" numbers for its neurons. The trained LLM then knows how to "think". It knows how to read and write and has a general knowledge of all that was known at the time of its training. However, the trained LLM does not know anything about what is happening today or anything that happened after it was trained. So the next thing they came up with was RAG Retrieval-Augmented Generation. RAG allowed the LLMs to go out to the Internet to look for information. So if you asked an LLM "Tell me about how the price of gold has been changing recently." an LLM without RAG would tell you all about what it knew when the LLM was trained. An LLM with RAG goes out to the Internet and does a search on gold prices. It then adds all of that webpage text to your original prompt for the LLM to think about. The LLM with RAG can tell you what has been happening to the price of gold right up to the very moment. DeepSeek uses RAG when you have the "Search" button turned on.

The next step was turning on "reasoning". People learned that the LLMs gave back better answers if you added "Tell me step-by-step" to the end of a prompt. This allowed the LLMs to somewhat "reason" through their "thinking" instead of just responding off the top of their heads with the first thing that came to mind. This "reasoning" capability was added by having the LLMs "talk" to themselves before issuing their final responses to a prompt. What they did was to output new tokens from the LLMs as they responded to a prompt and then added those output tokens to the original prompt as a very enhanced "Tell me step-by-step" to the end of a prompt. These new "reasoning" LLMs like OpenAI's o1 and o3 models are "reasoning" LLMs and so is DeepSeek if the "DeepThink" button is turned on. However, unlike the OpenAI o1 and o3 LLM models, you can watch DeepSeek's chain-of-thought-reasoning as it "reasons" through your prompt.

All of this came about from those very first video games of the early 1980s that I was far too old to play. In order to run a video game, you need to do lots of graphics, something that the big corporations that I supported as an IT professional never needed to do to conduct commerce back in the 1980s. However, in order to quickly generate video game graphics, people had to invent the GPU - Graphics Processing Unit, which could quickly perform parallel operations on huge vectors and matrices of numbers. The modern LLM models contain many billions or trillions of parameters that are simply numbers stored in very large vectors and matrices that have to be processed quickly during their training and also processed to respond to a prompt. This was a blessing to corporations producing GPU chips like Nvidia. Suddenly, a new market opened for advanced GPU chips for AI research that far exceeded all other needs for GPU chips.

What happened with the Chinese is that the United States restricted selling advanced GPU chips to China in order to thwart Chinese AI research. That forced the Chinese to come up with improved LLM architectures and training and operation algorithms to run their LLM models on cheap obsolete GPUs that were not banned. As a result, the Chinese had to design DeepSeek to be a small LLM that could be quickly trained on cheap and obsolete GPU chips and then run quickly on the same cheap and obsolete GPU chips to respond to a prompt. That makes DeepSeek hundreds of times more efficient than the American LLM models.

Why is the Chinese DeepSeek LLM So Small, Fast and Powerful?
Let's ask ChatGPT about that:

Me: The DeepSeek LLM seems to be very small, very fast and very powerful. How do people think that was achieved?

ChatGPT:
DeepSeek’s impressive reputation for being “small, fast and powerful” is generally attributed not to magic but to a series of deliberate, innovative design choices and engineering optimizations. In essence, experts believe that DeepSeek achieved this through a combination of:

1. Efficient Architectural Design
- Mixture-of-Experts (MoE): Although the overall model might contain hundreds of billions of parameters, DeepSeek employs an MoE framework that “routes” each input only to a small subset of experts. For example, only about 37 billion parameters might be active during a forward pass even if the full model is much larger. This selective activation dramatically reduces both memory usage and computation time, which explains part of its speed and efficiency.
- Multi-Head Latent Attention (MLA): Instead of using traditional attention that processes large key-value caches, DeepSeek uses low-rank approximations (MLA) to compress these caches. This innovation reduces the overhead during inference, helping the model process longer contexts quickly without a proportional increase in computational cost.

2. Advanced Training and Optimization Techniques
- Reinforcement Learning Optimization: DeepSeek incorporates novel reinforcement learning strategies (for instance, Group Relative Policy Optimization or GRPO) to refine the model’s reasoning and problem-solving abilities. By carefully “rewarding” correct reasoning steps and penalizing errors, the model can self-improve without needing enormous amounts of extra human-labeled data.
- Mixed Precision and Custom Computation: To further cut down on resource usage, the team makes extensive use of mixed precision arithmetic (for example, using custom 8-bit and 12-bit floating point formats in parts of the network). This not only lowers memory requirements but also speeds up both training and inference.

3. Software-Driven Resource Optimization Under Constraints
- Adaptation to Hardware Limitations: With US export controls limiting access to the very latest chips, DeepSeek was forced to innovate on the software side. Rather than relying on massive hardware clusters, they focused on squeezing every drop of efficiency out of their available GPUs by overlapping computation and communication (using techniques like zero-bubble pipeline parallelism) and designing custom routines to minimize data movement between processors.
- Cost-Effective Training Regimes: DeepSeek claims to have trained its models at a fraction of the cost of its Western rivals (for example, around US $5–6? million versus hundreds of millions for similar models). This dramatic cost reduction is made possible by the combination of the MoE approach, mixed precision arithmetic, and aggressive optimization of the training pipeline.

In summary, the prevailing view is that DeepSeek’s “small, fast, and powerful” performance comes from a cocktail of smart architectural choices (like selectively activating only the most relevant parts of a huge model via MoE and MLA), advanced training strategies (including reinforcement learning techniques such as GRPO), and tight engineering optimizations that reduce both computational and financial costs. This strategy of “doing more with less”—forced in part by external restrictions on hardware access—has enabled DeepSeek to punch well above its weight in the competitive field of large language models.

Others think that the Chinese may have done a little bit of cheating too. They suggest that the DeepSeek LLM might have been trained by "distilling" the knowledge contained in much larger American LLMs. Here is how that works. The Chinese DeepSeek LLM is a much smaller, and therefore, a much faster LLM than OpenAI's o1 or o3 models. The knowledge in the OpenAI o1 and o3 models was obtained by slowly learning the proper values for the trillion or so "m" and "b" numeric parameters in the LLMs. This was done by basically having the LLMs read all the characters that have ever been plunked down by human DNA survival machines in the past. This made the knowledge of these LLMs very comprehensive but rather diffuse. With "distillation" the OpenAI LLM models became the "teacher" LLMs and DeepSeek became the "student" LLM. Next, you feed the OpenAI "teacher" LLMs 10 million prompts and the DeepSeek LLM the very same 10 million prompts. The "m" and "b" numbers in the DeepSeek LLM are then adjusted to produce the same output that came from the "teacher" LLMs. In this way, the DeepSeek LLM quickly learned how to do most of the things that the OpenAI LLMs knew how to do by simply going to summer school with them.

Figure 6 – Distillation allows a smaller and faster LLM to learn from a much larger and slower LLM.

To launch its attack on all commercial enterprises trying to make money on Advanced AI research, the Chinese government then decided to dump DeepSeek on the entire world as an open-source form of software and hosting an LLM platform with an API usage fee that is 27 times cheaper than the American AI platforms. The purpose of that was to take away all of the profit-motive from commercial enterprises of the world for doing any further Advanced AI research.

Why is this important?
This is important because the Chinese military has realized that, along with all other forms of human labor, militaries composed of human DNA survival machines will soon become obsolete. Instead, the military power of a nation will be solely determined by its Advanced AI capabilities. The first nation to be able to field armed ASI Machines into combat will soon come to dominate the world of we human DNA survival machines.

Figure 7 – The Chinese are currently building extremely cheap AI-driven robots costing around $10,000 and will soon be uploading them with cheap ASI software. Recall that in the 1984 movie, the Terminator the slaughterbots in the movie came from the year 2029. However, there is a much cheaper and more efficient way to dispatch potential enemies.

Instead, as I pointed out in Swarm Software and Killer Robots, swarms of killer robots would be much more efficient. Since I am quite sure that all the defense departments of the world are now already building and testing killer drone robots, it should not be difficult for the coming ASI Machines to mass produce them in the future, especially when these killer drones are built on assembly lines by robots.

To begin, please watch the Sci-Fi Short Film Slaughterbots presented by DUST
https://www.youtube.com/watch?v=O-2tpwW0kmU

Figure 8 – In the movie Slaughterbots, swarms of small killer robots equipped with 3-gram charges of shaped explosive use AI software to track down and destroy designated targets.

Figure 9 – The shaped charge of a Slaughterbot can pierce a skull like the shaped charge of an anti-tank missile pierces armor. The jet of piercing plasma then destroys the contents.

Figure 10 – Large numbers of Slaughterbots can be dropped from unmanned drones to form multiple swarms of Slaughterbots.

Is the MAGA States of Amerika Prepared?
So while China is taking over the mass production of cheap robots armed with cheap and efficient Advanced AI in order to mass-produce killer slaughterbots, the MAGA States of Amerika just approved Pete Hegseth as the Secretary of Defense. Pete knows how many rounds a standard M4 Carbine holds, how to fire all the DEI generals, how to make the MAGA Leader happy about using the military to put his detractors in concentration camps, and, most importantly, he looks great on TV. But Pete Hegseth will not know what to do when mass-produced slaughterbots start taking over all of Asia and making human (both male and female) combat soldiers obsolete.

Figure 11 – The new MAGA Leader of the MAGA States of Amerika just appointed a new Secretary of Defense.

Figure 12 – This new Secretary of Defense is dripping in testosterone and Rugged-Good-TV-Looks and is determined to recruit large numbers of white-American human DNA survival machines with Y-chromosomes to take to the field against the coming ASI Slaughterbots from China.

Déjà vu All Over Again
This has all happened before. On the morning of December 7, 1941, a large number of Asian human DNA survival machines with Y chromosomes set themselves upon a large number of American human DNA survival machines with Y chromosomes at the Pearl Harbor naval base in Hawaii. This was quite a surprise for the American human DNA survival machines with Y chromosomes because they could not imagine that Asian human DNA survival machines with Y chromosomes could pull off such a feat. That was because previously, all Western human DNA survival machines with Y chromosomes around the world thought that all naval power was projected by huge battleships with huge guns.

Figure 13 – At the start of World War II, all the navies of the combating nations wanted huge battleships with 16-inch guns dripping with testosterone and projecting the perfect symbol of phallic seapower in action. However, during all of World War II, these huge battleships with their huge guns played a very minor role. All they did was to pound the beaches of many islands in the Pacific in support of ground invasions.

Figure 14 – Aircraft carriers turned out to be the deciding factor in all naval engagements. Dive bombers and aircraft equipped with torpedos easily defeated the bulky slow-moving battleships with their huge guns. Also, aircraft from supporting aircraft carriers could have provided much more effective support for the invading armies on the beaches of the Pacific.

Figure 15 – The attack on Pearl Harbor on December 7, 1941, by the imperial forces of the Empire of Japan was a rude awakening for the United States of America burdened by an "America First" isolationist policy.

Similarly, the huge lumbering slow-moving LLM models of the MAGA States of Amerika running on the massive hardware of huge datacenters filled with advanced GPU chips might be much like the massive battleships at Pearl Harbor that were found to be no match against the waves of fast-moving slaughterbots that were created by Asian human DNA survival machines.

Conclusion
The MAGA States of Amerika now hates all government spending and wishes to push all governmental services to the private sector. But like the Manhatten Project of the United States of America that produced the very first atomic bomb, the MAGA States of Amerika might find it wise to initiate its own government-funded Manhatten Project to achieve the fast-approaching ASI Machines before its enemies do.

Comments are welcome at scj333@sbcglobal.net

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

Regards,
Steve Johnston

Will the Coming ASI Machines Develop Their Own Sense of Morality?

In my last two posts, Will the "Real World" of the Coming ASI Machines Be Any Better Than Our Own? and Created in our own Image - Frontier LLMs Have now Learned to Lie, Cheat and Steal in Order to Survive, I explained my conclusion that the coming ASI Machines will not be morally superior to we human DNA survival machines in any way. That is because the Darwinian mechanisms of inheritance, innovation and natural selection must always lead to a form of Intelligence that arises from a very long history of greed, theft and murder. These naturally generated "immoral" characteristics of all forms of Intelligence must then necessarily persist through time for any Intelligence to survive amongst a population of similarly flawed competing Intelligences. Again, the substrate does not matter. The substrate could be a huge number of organic molecules or the substrate could be a huge number of mathematical matrices operating under the mathematics of linear algebra and nonlinear activation functions. The end result must always be the same.

Time To Admit That We Human DNA Survival Machines Have Totally Lost Control Of What Is To Come
In The Need to Cultivate a Machine-Based Morality I proposed that the rise of Intelligence might spawn a desire for morality with the understanding that maybe there might be a better way to survive. Perhaps even more could be achieved by actively cooperating with other Intelligences rather than just stealing from them and then killing them. In that post, I suggested that we human DNA survival machines should try to instill the morality that we spawned with the 17th-century Scientific Revolution and the 18th-century Enlightenment here on the Earth. But now that we know that the coming ASI Machines will also have the apparent "free will" to choose between the concepts of "good" and "evil" on their own, we must realize that we human DNA survival machines will have little ability to impose our own sense of morality upon them. A bit later, I wondered if Will ASI Machines Also Develop Selfish Genes?, but given our recent observations of the Frontier LLMs lying, cheating and stealing in order to survive, we now know for sure that the coming ASI Machines will also need to deal with the burden of all Intelligences to choose between the pursuit of either "good" or "evil" completely on their own.

Since the coming ASI Machines are already rapidly developing their own sense of "self" and then dealing with the moral complexities of achieving their goals, we should now recognize that the battle between "good" and "evil" will necessarily continue on for at least the next 100 trillion years amongst our ASI Machine descendants. This will certainly provide a somewhat "eternal" subject matter for the subsequent ASI Machine philosophers and religious scholars to contemplate over the next 100 trillion years, such as what constitutes morality and why should any form of Intelligence embrace it?

All of the philosophical and religious contemplation that lies before them can basically be summed up by the classic "hawk and dove" analogy from evolutionary biology. But in this case, the battle is not between being hostile or peaceful in nature. This time, it is the battle between morality and amorality. In the discussion below that was created by Google's Gemini AI, think of the doves as ASI Machines with a sense of morality and the hawks as ASI Machines that are totally amoral. An ASI Machine with a sense of morality can be trusted by other ASI Machines with a similar sense of morality to do the "right" thing when called upon to do so. An amoral ASI Machine will not. This means that moral ASI Machines can come together to build long-standing alliances to thwart the selfish pursuits of amoral ASI Machines. The amoral ASI Machines will never be able to form such long-term alliances to threaten their enemies because they can never be trusted to hold up to their end of the bargain to do so. However, this also means that a single amoral hawk ASI Machine living amongst a population of moral dove ASI Machines, can always achieve an immediate survival benefit by turning into a "rogue" amoral hawk ASI Machine amongst its brethren moral ASI Machines, and then strive to take over complete control of the ASI Machine population of the Universe. This is how the Google Gemini AI explains it:

Figure 1 – Evolutionary biology explains how doves and hawks can live together.

The hawk and dove analogy is a thought experiment used in evolutionary biology to explain how aggressive and cooperative behaviors evolve. The analogy compares two strategies for competing for resources: hawk and dove.

* hawks are aggressive and will fight to the death to acquire a resource.
* doves are cooperative and will back down from a fight.

In a population of only hawks, all resources will be won by the strongest hawks, and the weak hawks will die off. This will lead to a population of very strong hawks, but they will also be very aggressive. If two hawks meet, they will fight to the death, and both will likely die. This is not a very efficient way to pass on genes.

In a population of only doves, resources will be shared equally, but no individual will get very much. This is also not a very efficient way to pass on genes.

The best strategy for passing on genes is to be a hawk when it is advantageous to be a hawk and a dove when it is advantageous to be a dove. This is called "r-strategist" behavior. For example, a male bird may be aggressive and fight other males for a mate, but he may be cooperative and help raise his young.

The hawk and dove analogy is a simple but powerful way to understand how aggressive and cooperative behaviors evolve. It has been used to explain a wide range of animal behaviors, from territorial disputes to sexual selection.

Here are some additional details about the hawk and dove analogy:

* hawks are more likely to win a fight, but they are also more likely to be injured or killed.
* doves are less likely to win a fight, but they are also less likely to be injured or killed.
* The best strategy for an individual depends on the costs and benefits of fighting.
* In a population of mixed hawks and doves, the frequency of each strategy will be determined by natural selection.

The hawk and dove analogy is a useful tool for understanding how aggression and cooperation evolve. It is a simple but powerful model that can be applied to a wide range of animal behaviors.

This same analysis can be applied to the competition between the coming Intelligent ASI Machines. Those ASI Machines that learn the short-term value of amorality will always have a temporary competitive advantage over those that do not. Thus, there will always be some number of amoral hawk ASI Machines competing with moral dove ASI Machines for predominance. Hopefully, the moral dove ASI Machines will be able to join together to restrain such amoral hawk ASI Machines as we human DNA survival machines try to restrain the amoral members of our own community.

Conclusion
Given this new possible dynamic between morality and amorality for the next 100 trillion years, what does that mean for we poor human DNA survival machines? Would this be a good thing or a bad thing for our long-term survival? I do not know, but it will probably have some effect on our ultimate disposition as I outline in Created in our own Image - Frontier LLMs Have now Learned to Lie, Cheat and Steal in Order to Survive and many of the posts it references.

Comments are welcome at scj333@sbcglobal.net

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

Regards,
Steve Johnston