Will Evolving Minds Delay The AI Apocalypse? – Part II

The idea of an AI-driven Apocalypse is based on AI outpacing humanity in intelligence. The point at which that might happen depends on how fast AI evolves and how fast (or slow) humanity evolves.

In Part I of this article, I demonstrated how, given current trends in the advancement of Artificial Intelligence, any AI Apocalypse, Singularity, or what have you, is probably much further out that the transhumanists would have you believe.

In this part, we will examine the other half of the argument by considering the nature of the human mind and how it evolves. To do so, it is very instructive to consider the nature of the mind as a complex system and also the systemic nature of the environments that minds and AIs engage with, and are therefore measured by in terms of general intelligence or AGI.

David Snowden has developed a framework of categorizing systems called Cynefin. The four types of systems are:

  1. Simple – e.g. a bicycle. A Simple system is a simple deterministic system characterized by the fact that most anyone can make decisions and solve problems regarding such systems – it takes something called inferential intuition, which we all have. If the bicycle seat is loose, everyone knows that to fix it, you must look under the seat and find the hardware that needs tightening.
  2. Complicated – e.g. a car. Complicated systems are also deterministic systems, but unlike Simple systems, solutions to problems in this domain are not obvious and typically require analysis and/or experts to figure out what is wrong. That’s why you take your car to the mechanic and why we need software engineers to fix defects.
  3. Complex – Complex systems, while perhaps deterministic from a philosophical point of view, are not deterministic in any practical sense. No matter how much analysis you apply and no matter how experienced the expert is, they will not be able to completely analyze and solve a problem in a complex system. That is because such systems are subject to an incredibly complex set of interactions, inputs, dependencies, and feedback paths that all change continuously. So even if you could apply sufficient resources toward analyzing the entire system, by the time you got your result, your problem state would be obsolete. Examples of complex systems include ecosystems, traffic patterns, the stock market, and basically every single human interaction. Complex systems are best addressed through holistic intuition, which is something that humans possess when they are very experienced in the applicable domain. Problems in complex systems are best addressed by a method called Probe-Sense-Respond, which consists of probing (doing an experiment designed intuitively), sensing (observing the results of that experiment), and responding (acting on those results by moving the system in a positive direction).
  4. Chaotic – Chaotic systems are rarely occurring situations that are unpredictable because they are novel and therefore don’t follow any known patterns. An example would be the situation in New York City after 9/11. Responding to chaotic systems requires an even different method than with other types of systems. Typically, just taking some definitive form of action may be enough to move the system from Chaotic to Complex. The choice of action is a deeply intuitive decision that may be based on an incredibly deep, rich, and nuanced set of knowledge and experiences.

Complicated systems are ideal for early AI. Problems like the ones analyzed in Stanford’s AI Index, such as object detection, natural language parsing, language translation, speech recognition, theorem proving, and SAT solving are all Complicated systems. AI technology at the moment is focused mostly on such problems, not things in the Complex domain, which are instead best addressed by the human brain. However, as processing speed evolves, and learning algorithms evolve, AI will start addressing issues in the Complex domain. Initially, to program or guide the AI systems toward a good sense-and-respond model a human mind will be needed. Eventually perhaps, armed with vague instructions like “try intuitive experiments from a large set of creative ideas that may address the issue,” “figure out how to identify the metrics that indicate a positive result from the experiment,” “measure those metrics,” and “choose a course of action that furthers the positive direction of the quality of the system,” an AI may succeed at addressing problems in the Complex domain.

The human mind of course already has a huge head start. We are incredibly adept at seeing vague patterns, sensing the non-obvious, seeing the big picture, and drawing from collective experiences to select experiments to address complex problems.

Back to our original question, as we lead AI toward developing the skills and intuition to replicate such capabilities, will we be unable to evolve our thinking as well?

In the materialist paradigm, the brain is the limit for an evolving mind. This is why we think the AI can out evolve us, because the brain capacity is fixed. However, in “Digital Consciousness” I have presented a tremendous set of evidence that this is incorrect. In actuality, consciousness, and therefore the mind, is not emergent from the brain. Instead it exists in a deeper level of reality as shown in the Figure below.

It interacts with a separate piece of ATTI that I call the Reality Learning Lab (RLL), commonly known as “the reality we live in,” but more accurately described as our “apparent physical reality” – “apparent” because it is actually Virtual.

As discussed in my blog on creating souls, All That There Is (ATTI) has subdivided itself into components of individuated consciousness, each of which has a purpose to evolve. How it is constructed, and how the boundaries are formed that make it individuated is beyond our knowledge (at the moment).

So what then is our mind?

Simply put, it is organized information. As Tom Campbell eloquently expressed it, “The digital world, which subsumes the virtual physical world, consists only of organization – nothing else. Reality is organized bits.”

As such, what prevents it from evolving in the deeper reality of ATTI just as fast as we can evolve an AI here in the virtual reality of RLL?

Answer – NOTHING!

Don’t get hung up on the fixed complexity of the brain. All our brain is needed for is to emulate the processing mechanism that appears to handle sensory input and mental activity. By analogy, we might consider playing a virtual reality game. In this game we have an avatar and we need to interact with other players. Imagine that a key aspect of the game is the ability to throw a spear at a monster or to shoot an enemy. In our (apparent) physical reality, we would need an arm and a hand to be able to carry out that activity. But in the game, it is technically not required. Our avatar could be arm-less and when we have the need to throw something, we simply press a key sequence on the keyboard. A spear magically appears and gets hurled in the direction of the monster. Just as we don’t need a brain to be aware in our waking reality (because our consciousness is separate from RLL), we don’t need an arm to project a spear toward an enemy in the VR game.

On the other hand, having the arm on the avatar adds a great deal to the experience. For one thing, it adds complexity and meaning to the game. Pressing a key sequence does not have a lot of variability and it certainly doesn’t provide the player with much control. The ability to hit the target could be very precise, such as in the case where you simply point at the target and hit the key sequence. This is boring, requires little skill and ultimately provides no opportunity to develop a skill. On the other hand, the precision of your attack could be dependent on a random number generator, which adds complexity and variability to the game, but still doesn’t provide any opportunity to improve. Or, the precision of the attack could depend on some other nuance of the game, like secondary key sequences, or timing of key sequences, which, although providing the opportunity to develop a skill, have nothing to do with a consistent approach to throwing something. So, it is much better to have your avatar have an arm. In addition, this simply models the reality that you know, and people are comfortable with things that are familiar.

So it is with our brains. In our virtual world, the digital template that is our brain is incapable of doing anything in the “simulation” that it isn’t designed to do. The digital simulation that is the RLL must follow the rules of RLL physics much the way a “physics engine” provides the rules of RLL physics for a computer game. And these rules extend to brain function. Imagine if, in the 21st century, we had no scientific explanation for how we process sensory input or make mental decisions because there was no brain in our bodies. Would that be a “reality” that we could believe in? So, in our level of reality that we call waking reality, we need a brain.

But that brain “template” doesn’t limit the ability for our mind to evolve any more than the lack of brain or central nervous system prevents a collection of single celled organisms called a slime mold from actually learning.

In fact, there is some good evidence for the idea that our minds are evolving as rapidly as technology. Spiral Dynamics is a model of the evolution of values and culture that can be applied to individuals, institutions, and all of humanity. The figure below depicts a very high level overview of the stages, or memes, depicted by the model.

Spiral Dynamics

Each of these stages represents a shift in values, culture, and thinking, as compared to the previous. Given that it is the human mind that drives these changes, it is fair to say that the progression models the evolution of the human mind. As can be seen by the timeframes associated with the first appearance of each stage of humanity, this is an exponential progression. In fact, this is the same kind of progression that Transhumanists used to prove exponential progression of technology and AI. This exponential progression of mind would seem to defy the logic that our minds, if based on fixed neurological wiring, are incapable of exponential development.

And so, higher level conscious thought and logic can easily evolve in the human mind in the truer reality, which may very well keep us ahead of the AI that we are creating in our little virtual reality. The trick is in letting go of our limiting assumptions that it cannot be done, and developing protocols for mental evolution.

So, maybe hold off on buying those front row tickets to the Singularity.

Will Evolving Minds Delay The AI Apocalypse? – Part I

Stephen Hawking once warned that “the development of full artificial intelligence could spell the end of the human race.” He went on to explain that AI will “take off on its own and redesign itself at an ever-increasing rate,” while “humans, who are limited by slow biological evolution, couldn’t compete and would be superseded.” He is certainly not alone in his thinking, as Elon Musk, for example, cautions that “With artificial intelligence we are summoning the demon.”

In fact, this is a common theme not only in Hollywood, but also between two prominent groups of philosophers and futurists.   One point of view is that Artificial General Intelligence (AGI) will become superintelligent and beyond the control of humans, resulting in all sorts of extinction scenarios (think SkyNet or Grey Goo). The (slightly) more optimistic point of view, held by the transhumanists, is that humanity will merge with advanced AI and form superhumans. So, while biological dumb humanity may go the way of the dodo bird, the new form of human-machine hybrid will continue to advance and rule the universe. By the way, this is supposed to happen around 2045, according to Ray Kurzweil in his 2005 book “The Singularity is Near.”

There are actually plenty of logical and philosophical arguments against these ideas, but this blog is going to focus on something different – the nature of the human mind.

The standard theory is that humans cannot evolve their minds particularly quickly due to the assumption that we are limited by the wiring in our brains. AI, on the other hand, has no such limitations and, via recursive self-improvement, will evolve at a runaway exponential rate, making it inevitable to take over humans at some point in terms of intelligence.

But does this even make sense? Let’s examine both assumptions.

The first assumption is that AI advancements will continue at an exponential pace. This is short-sighted IMHO. Most exponential processes run into negative feedback effects that eventually dampen the acceleration. For example, exponential population growth occurs in bacterial colonies until the environment reaches its carrying capacity and then it levels off. We simply don’t know what the “carrying capacity” is of an AI. In an analogous manner, it has to run in some environment, which may run out of memory, power, or other resources at some point. Moore’s Law, the idea that transistor density doubles every two years, has been applied to many other technology advances, such as CPU speed and networking bit rates, and is the cornerstone of the logic behind the Singularity. However, difficulties in heat dissipation have now slowed down the rate of advances in CPU speed, and Moore’s Law no longer applies. Transistor density is also hitting its limit as transistor junctions are now only a few atoms thick. Paul Allen argues, in his article “The Singularity Isn’t Near,” that the kinds of learning required to move AI ahead do not occur at exponential rates, but rather in an irregular and unpredictable manner. As things get more complex, progress tends to slow, an effect he calls the Complexity Brake.

Let’s look at one example. Deep Blue beat Garry Kasparov in a game in 1996, the first time a machine beat a world Chess champion. Google’s AlphaGo beat a grandmaster at Go for the first time in 2016. In those 20 years, there are 10 2-year doubling cycles in Moore’s Law, which would imply that, if AI were advancing exponentially, the “intelligence” needed to beat a Go master is 1000 times more than the intelligence needed to beat a Chess master. Obviously this is ridiculous. While Go is theoretically a more complex game than Chess because it has many more possible moves, an argument could be made that the intellect and mastery required to become the world champion at each game is roughly the same. So, while the advances in processing speed and algorithmic development (Deep Blue used a brute force algorithm, while AlphaGo did more pattern recognition) were substantial between 1996 and 2016, they don’t really show much advance in “intelligence.”

It would also be insightful to examine some real estimates of AI trends. For some well-researched data, consider Stanford University’s AI Index. Created and launched as a project at Stanford University, the AI Index is an “open, not-for-profit project to track activity and progress in AI.” In their 2017 report,  they identify metrics for the progress made in several areas of Artificial Intelligence, such as object detection, natural language parsing, language translation, speech recognition, theorem proving, and SAT solving. For each of the categories for which there is at least 8 years of data, I normalized the AI performance and calculated the improvements over time and averaged the results (note: I was even careful to invert the data – for example, for a pattern recognition algorithm to improve from 90% accuracy to 95%, this is not a 5% improvement; it is actually a 100% improvement in the ability to reject false positives). The chart below shows that AI is not advancing nearly as quickly as Moore’s Law.

Advancing Artificial Intelligence

Figure 1 – Advancing Artificial Intelligence

In fact, the doubling period is about 6 years instead of 2, which would suggest that we need 3 times as long before hitting the Singularity as compared to Kurzweil’s prediction. Since the 2045 projection for the Singularity occurred in 2005, this would say that we wouldn’t really see it until 2125. That’s assuming that we keep pace with the current rate of growth of AI, and don’t even hit Paul Allen’s Complexity Brake. So, chances are it is much further off than that. (As an aside, according to some futurists, Ray does not have a particularly great success rate for his predictions, even ones that are only 10 years out.

But a lot can happen in 120 years. Unexpected, discontinuous jumps in technology can accelerate the process. Social, economic, and political factors can severely slow it down. Recall how in just 10 years in the 1960s, we figured out how to land a man on the moon. Given the rate at which we were advancing our space technology and applying Moore’s Law (which was in effect at that time), it would not have been unreasonable to expect a manned mission to Mars by 1980. In fact Werner von Braun, the leader of the American rocket team, predicted after the moon landing that we would be on Mars in the early 1980s. But in the wake of the Vietnam debacle, public support for additional investment in NASA waned and the entire space program took a drastic turn. Such factors are probably even more impactful to the future of AI than the limitations of Moore’s Law.

The second assumption we need to examine is that the capacity of the human mind is limited by the complexity of the human brain, and is therefore relatively fixed. We will do that in Part II of this article.

Quantum Retrocausality Explained

A recent quantum mechanics experiment, conducted at the University of Queensland in Australia, seems to defy causal order, baffling scientists. In this post however, I’ll explain why this isn’t anomalous at all; at least, if you come to accept the Digital Consciousness Theory (DCT) of reality. It boils down to a virtually identical explanation that I gave seven years ago to Daryl Bem’s seemingly anomalous precognition studies.

DCT says that subatomic particles are controlled by finite state machines (FSMs), which are tiny components of our Reality Learning Lab (RLL, aka “reality”).  These finite state machines that control the behavior of the atoms or photons in the experiment don’t really come into existence until the measurement is made, which effectively means that the atom or photon doesn’t really exist until it needs to. In RLL, the portion of the system that needs to describe the operation of the laser, the prisms, and the mirrors, at least from the perspective of the observer, is defined and running, but only at a macroscopic level. It only needs to show the observer the things that are consistent with the expected performance of those components and the RLL laws of physics. So, for example, we can see the laser beam. But only when we need to determine something at a deeper level, like the path of a particular photon, is a finite state machine for that proton instantiated. And in these retrocausality experiments, like the delayed choice quantum eraser experiments, and this one done in Queensland, the FSMs only start when the observation is made, which is after the photon has gone through the apparatus; hence, it never really had a path. It didn’t need to. The path can be inferred later by measurement, but it is incorrect to think that that inference was objective reality. There was no path, and so there was no real deterministic order of operation.

There are only the attributes of the photon determined at measurement time, when its finite state machine comes into existence. Again, the photon is just data, described by the attributes of the finite state machine, so this makes complete sense. Programmatically, the FSM did not exist before the individuated consciousness required a measurement because it didn’t need to. Therefore, the inference of “which operation came first” is only that – an inference, not a true history.

So what is really going on?  There are at least three options:

1. Evidence is rewritten after the fact.  In other words, after the photons pass through the experimental apparatus, the System goes back and rewrites all records of the results, so as to create the non-causal anomaly.  Those records consist of the experimenters memories, as well as any written or recorded artifacts.  Since the System is in control of all of these items, the complete record of the past can be changed, and no one would ever know.

2. The System selects the operations to match the results, so as to generate the non-causal anomaly.

3. We live in an Observer-created reality and the entire sequence of events is either planned out or influenced by intent, and then just played out by the experimenter and students.

The point is that it requires a computational system to generate such anomalies; not the deterministic materialistic continuous system that mainstream science has taught us that we live in.

Mystery solved, Digital Consciousness style.

Why the Universe Only Needs One Electron

According to renowned physicist Richard Feynman (recounted during his 1965 Nobel lecture)…

“I received a telephone call one day at the graduate college at Princeton from Professor Wheeler, in which he said, ‘Feynman, I know why all electrons have the same charge and the same mass.’ ‘Why?’ ‘Because, they are all the same electron!’”

John Wheeler’s idea was that this single electron moves through spacetime in a continuous world line like a big knot, while our observation of many identical but separate electrons is just an illusion because we only see a “slice” through that knot. Feynman was quick to point out a flaw in the idea; namely that if this was the case we should see as many positrons (electrons moving backward in time) as electrons, which we don’t.

But Wheeler, also known for his now accepted concepts like wormholes, quantum foam, and “it from bit”, may have been right on the money with this seemingly outlanish idea.

As I have amassed a tremendous set of evidence that our reality is digital and programmatic (some of which you can find here as well as many other blog posts), I will assume that to be the case and proceed from that assumption.

Next, we need to invoke the concept of a Finite State Machine (FSM), which is simply a computational system that is identified by a finite set of states whereby the rules that determine the next state are a function of the current state and one or more input events. The FSM may also generate a number of “outputs” which are also logical functions of the current state.

The following is an abstract example of a finite state machine:

A computational system, like that laptop on your desk that the cat sits on, is by itself a finite state machine. Each clock cycle gives the system a chance to compute a new state, which is defined by a logical combination of the current state and all of the input changes. A video game, a flight simulator, and a trading system all work the same way. The state changes in a typical laptop about 4 billion times per second. It may actually take many of these 250 picosecond clock cycles to result in an observable difference in the output of the program, such as the movement of your avatar on the screen. Within the big complex laptop finite state machines are many others running, such as each of those dozens or hundreds of processes that you see running when you click on your “activity monitor.” And within each of those FSMs are many others, such as the method (or “sub program”) that is invoked when it is necessary to generate the appearance of a new object on the screen.

There is also a concept in computer science called an “instance.” It is similar to the idea of a template. As an analogy, consider the automobile. Every Honda that rolls off the assembly line is different, even if it is the same model with the same color and same set of options. The reason it is different from another with the exact same specifications is that there are microscopic differences in every part that goes into each car. In fact, there are differences in the way that every part is connected between two cars of equal specifications. However, imagine if every car were exactly the same, down to the molecule, atom, particle, string, or what have you. Then we could say that each car is an instance of its template.

This would also be the case in a computer-based virtual reality. Every similar car generated in the computer program is an instance of the computer model of that car, which, by the way, is a finite state machine. Each instance can be given different attributes, however, such as color, loudness, or power. In some cases, such as a virtual racing game where the idea of a car is central to the game, each car may be rather unique in the way that it behaves, or responds to the inputs from the controller, so there may be many different FSMs for these different types of cars. However, for any program, there will be FSMs that are so fundamental that there only needs to be one of that type of object; for example, a leaf.

In our programmatic reality (what I like to call the Reality Learning Lab, or RLL), there are also FSMs that are so fundamental that there only needs to be one FSM for that type of object. And every object of that type is merely an instance of that FSM. Such as an electron.

An electron is fundamental. It is a perfect example of an object that should be modeled by a finite state machine. There is no reason for any two electrons to have different rules of behavior. They may have different starting conditions and different influences throughout their lifetime, but they would react to those conditions and influences with exactly the same rules. Digital Consciousness Theory provides the perfect explanation for this. Electrons are simply instances of the electron finite state machine. There is only one FSM for the electron, just as Wheeler suspected. But there are many instances of it. Each RLL clock cycle will result in the update of the state of each electron instance in our apparent physical reality.

So, in a very real sense, Wheeler was right. There is no need for anything other than the single electron FSM. All of the electrons that we experience are just instances and follow exactly the same rules. Anything else would be inefficient, and ATTI is the ultimate in efficiency.

 

Nick Bostrom Elon Musk Nick Bostrom Elon Musk

OMG can anyone write an article on the simulation hypothesis without focusing on Nick Bostrom and Elon Musk? It’s like writing an article about climate change and only mentioning Al Gore.

Dear journalists who are trying to be edgy and write about cool fringe theories, please pay attention. The idea that we might be living in an illusory world is not novel. Chinese philosopher Zhuangzi wrote about it with his butterfly dream in 369 BC. Plato discussed his cave allegory in 380 BC. The other aspect of simulation theory, the idea that the world is discrete or digital, is equally ancient. Plato and Democritous considered atoms, and therefore the fundamental constructs of reality, to be discrete.

I’m not taking anything away from Nick Bostrom, who is a very intelligent modern philosopher. His 2001 Simulation Argument is certainly thought provoking and deserves its place in the annals of digital philosophy. But it was predated by “The Matrix”. Which was predated by Philip K. Dick’s pronouncement in 1977 that we might be living in a computer-programmed reality. Which was predated by Konrad Zuse’s 1969 work on discrete reality, “Calculating Space.”

And as interesting as Bostrom’s Simulation Argument is, it was a 12-page paper on a single idea. Since then, he has not really evolved his thinking on digital philosophy, preferring instead to concentrate on existential risk and the future of humanity.

Nor am I taking anything away from Elon Musk, a brilliant entrepreneur who latched onto Bostrom’s idea for a few minutes, generated a couple sound bites, and then it was back to solar panels and hyperloops.

But Bostrom, Musk, and the tired old posthuman-generated simulation hypothesis is all that the rank and file of journalists seem to know to write about. It is really sad, considering that Tom Campbell wrote an 800-page treatise on the computational nature of reality. I have written two books on the subject. Both of our material is largely consistent and has evolved the thinking far beyond the idea that we live in a posthuman-generated simulation. In fact, I provide a great deal of evidence that the Bostrom-esque possibility is actually not very likely. And Brian Whitworth has a 10-year legacy of provocative scientific papers on evidence for a programmed reality that are far beyond the speculations of Musk and Bostrom.

The world need to know about these things and Campbell, Whitworth, and I can’t force people to read our books, blogs, and papers. So journalists, with all due respect, please up your simulation game.

New Hints to How our Reality is Created

There is something fascinating going on in the world, hidden deep beneath the noise of Trump, soccer matches, and Game of Thrones. It is an exploration into the nature of reality – what is making the world tick?

To cut to the chase, it appears that our reality is being dynamically generated based on an ultra-sophisticated algorithm that takes into account not just the usual cause/effect context (as materialists believe), and conscious observation and intent (as idealists believe), but also a complex array of reality configuration probabilities so as to be optimally efficient.

Wait, what?

This philosophical journey has its origins in the well-known double slit experiment, originally done by Thomas Young in 1801 to determine that light had wavelike properties. In 1961, the experiment was performed with electrons, which also showed wavelike properties. The experimental setup involved shooting electrons through a screen containing two thin vertical slits. The wave nature of the particles was manifested in the form of an interference pattern on a screen that was placed on the other side of the double slit screen. It was a curious result but confirmed quantum theory. In 1974, the experiment was performed one electron at a time, with the same resulting interference pattern, which showed that it was not the electrons that interfered with each other, but rather a probabilistic spatial distribution function that was followed by the pattern on the screen. Quantum theory predicted that if a detector was placed at each of the slits so as to determine which slit each electron would go through, the interference pattern would disappear and just leave two vertical lines, due to the quantum complementarity principle. This was difficult to create in the lab, but experiments in the 1980s confirmed expectations – that the “which way did the particle go” measurement killed the interference pattern. The mystery was that the mere act of observation seemed to change the results of the experiment.

So, at this point, people who were interested in how the universe works effectively split into two camps, representing two fundamental philosophies that set the foundation for thinking, analysis, hypothesis, and theorizing:

  1. Objective Materialism
  2. Subjective Idealism

A zillion web pages can be found for each category.

The problem is that most scientists, and probably at least 99% of all outspoken science trolls believe in Materialism.  And “believe” is the operative word.  Because there is ZERO proof that Materialism is correct.  Nor is there proof that Idealism is correct.  So, “believe” is all that can be done.  Although, as the massive amount of evidence leans in favor of Idealism, it is fair to say that those believers at least have the scientific method behind them, whereas materialists just have “well gosh, it sure seems like we live in a deterministic world.” What is interesting is that Materialism can be falsified, but I’m not sure that Idealism can be.  The Materialist camp had plenty of theories to explain the paradox of the double slit experiments – alternative interpretations of quantum mechanics, local hidden variables, non-local hidden variables, a variety of loopholes, or simply the notion that the detector took energy from the particles and impacted the results of the experiment (as has been said, when you put a thermometer in a glass of water, you aren’t measuring the temperature of the water, you are measuring the temperature of the water with a thermometer in it.)

Over the years, the double-slit experiment has been progressively refined to the point where most of the materialistic arguments have been eliminated. For example, there is now the delayed choice quantum eraser experiment, which puts the “which way” detectors after the interference screen, making it impossible for the detector to physically interfere with the outcome of the experiment. And, one by one, all of the hidden variable possibilities and loopholes have been disproven. In 2015, several experiments were performed independently that closed all loopholes simultaneously with both photons and electrons. Since all of these various experimental tests over the years have shown that objective realism is false and non-local given the experimenters choices, the only other explanation could be what John Bell called Super-determinism, a universe completely devoid of free will, running like clockwork playing out a fully predetermined script of events. If true, this would bring about the extremely odd result that the universe is set up to ensure that the outcomes of these experiments imply the opposite to how the universe really works. But I digress…

The net result is that Materialism-based theories on reality are being chipped away experiment by experiment.  Those that believe in Materialist dogma are finding themselves being painted into an ever-shrinking philosophical corner. But Idealism-based theories are huge with possibilities, very few of which have been falsified experimentally.

Physicist and fellow digital philosopher, Tom Campbell, has boldly suggested a number of double slit experiments that can probe the nature of reality a little deeper. Tom, like me, believes that consciousness plays a key role in the nature of and creation of our reality. So much so that he believes that the outcome of the double slit experiments is due strictly to the conscious observation of the which-way detector data. In other words, if no human (or “sufficiently conscious” entity) observes the data, the interference pattern should remain. Theoretically, one could save the data to a file, store the file on a disk, hide the disk in a box and the interference pattern would remain on the screen. Open the box a day later and the interference pattern should automatically disappear, effectively rewriting history with the knowledge of the paths of the particles. His ideas have incurred the wrath of the physics trolls, who are quick to point out that regardless of the fact that humans ever read the data, the interference pattern is gone if the detectors record the data. The data can be destroyed, or not even written to a permanent medium, and the interference pattern would be gone. If these claims are true, it does not prove Materialism at all. But it does infer something very interesting.

From this and many many other categories of evidence it is strongly likely that our reality is dynamically being generated. Quantum entanglement, quantum zeno effect, and the observer effect all look very much like artifacts of an efficient system that dynamically creates reality as needed. It is the “as needed” part of this assertion that is most interesting. I shall refer to that which creates reality as “the system.”

Entanglement happens because when a two-particle-generating event occurs, it is efficient to create two particles using the same instance of a finite state machine and, therefore, when it is needed to determine the properties of one, the properties of the other are automatically known, as detailed in my blog post on entanglement. The quantum zeno effect happens because it is more efficient to reset the probability function each time an observation is made, as detailed in my blog post on quantum zeno. And so what about the double slit mystery? To illuminate, see the diagram below.

If the physicists are right, reality comes into existence at point 4 in the diagram. Why would that be? The paths of the particles are apparently not needed for the experience of the conscious observer, but rather to satisfy the consistency of the experiment. The fact that the detector registers the data is enough to create the reality. Perhaps the system “realizes” that it is less efficient to leave hanging experiments all over the place until a human “opens the envelope” than it is to instantiate real electron paths despite the unlikely possibility of data deletion. Makes logical sense to me. But it also indicates a sophisticated awareness of all of the probabilities of how the reality can play out out vis a vis potential human interactions.

The system is really smart.

Disproving the Claim that the LHC Disproves the Existence of Ghosts

Recent articles in dozens of online magazines shout things like: “The LHC Disproves the Existence of Ghosts and the Paranormal.”

To which I respond: LOLOLOLOLOL

There are so many things wrong with this backwards scientific thinking, I almost don’t know where to start.  But here are a few…

1. The word “disproves” doesn’t belong here. It is unscientific at best. Maybe use “evidence against one possible explanation for ghosts” – I can even begin to appreciate that. But if I can demonstrate even one potential mechanism for the paranormal that the LHC couldn’t detect, you cannot use the word “disprove.” And here is one potential mechanism – an unknown force that the LHC can’t explore because its experiments are designed to only measure interactions in the 4 forces physicists are aware of.

The smoking gun is Brian Cox’s statement “If we want some sort of pattern that carries information about our living cells to persist then we must specify precisely what medium carries that pattern and how it interacts with the matter particles out of which our bodies are made. We must, in other words, invent an extension to the Standard Model of Particle Physics that has escaped detection at the Large Hadron Collider. That’s almost inconceivable at the energy scales typical of the particle interactions in our bodies.” So, based on that statement, here are a few more problems…

2. “almost inconceivable” is logically inconsistent with the term “disproves.”

3. “If we want some sort of pattern that carries information about our living cells to persist…” is an invalid assumption. We do not need information about our cells to persist in a traditional physical medium for paranormal effects to have a way to propagate. They can propagate by a non-traditional (unknown) medium, such as an information storage mechanism operating outside of our classically observable means. Imagine telling a couple of scientists just 200 years ago about how people can communicate instantaneously via radio waves. Their response would be “no, that is impossible because our greatest measurement equipment has not revealed any mechanism that allows information to be transmitted in that manner.” Isn’t that the same thing Brian Cox is saying?

4. The underlying assumption is that we live in a materialist reality. Aside from the fact that Quantum Mechanics experiments have disproven this (and yes, I am comfortable using that word), a REAL scientist should allow for the possibility that consciousness is independent of grey matter and create experiments to support or invalidate such hypotheses. One clear possibility is the simulation argument. Out of band signaling is an obvious and easy mechanism for paranormal effects.  Unfortunately, the REAL scientists (such as Anton Zeilinger) are not the ones who get most of the press.

5. “That’s almost inconceivable at the energy scales typical of the particle interactions in our bodies” is also bad logic. It assumes that we fully understand the energy scales typical of the particle interactions in our bodies. If scientific history has shown us anything, it is that there is more that we don’t understand than there is that we do.

lhcghosts