Thursday, March 16, 2017

Thalamus Prediction

Concurrent Pattern Hierarchy

This is just a short post to make a quick prediction about the internal organization of the thalamus, a relatively small but complex area of the brain that is thought to serve primarily as a relay center between various sensors and the sensory cortex. Given my current understanding of the brain and intelligence, I predict that the parts of the thalamus that process sensory signals (e.g., the lateral and medial geniculate nuclei) will be found to be hierarchically organized. The function of the hierarchy is to discover small concurrent patterns in the sensory space. These are commonly called "spatial patterns" in neuroscience. I personally don't like the use of the word "spatial" to refer to patterns because I think it is misleading. All patterns are temporal in my view, even if they refer to visual patterns. Here are some of the characteristics of the thalamic pattern hierarchy as predicted by my current model:
  • The hierarchy consists of a huge number of pattern detectors organized as binary trees.
  • The bottom level of the hierarchy receives signals from sensors.
  • The hierarchy has precisely 10 levels. This means that the most complex patterns have 1024 inputs.
  • Every level in the hierarchy makes reciprocal connections with the first level of the cerebral cortex.
  • Every pattern detector receive recognition feedback signals from the first level of the cerebral cortex.
The cerebral cortex (sequence memory) can instantly stitch these elementary patterns to form much bigger entities of arbitrary complexity. A number of researchers in artificial general intelligence (AGI), such as Jeff Hawkins and Subutai Ahmad of Numenta, assume (incorrectly in my view) that both concurrent and sequential patterns are learned and detected in the cortical columns of the cerebral cortex. In my model of the cortex, the cortical columns are used exclusively for sequence learning and detection while concurrent patterns are learned and recognized by the thalamus.

Stay tuned.

Edit 3/16/2017, 2:42 PM:

I should have elaborated further on the binary tree analogy. I prefer to call it an inverse or upside-down binary tree. That is to say, each node (pattern detector) in the tree receives only two inputs from lower level nodes. Each node may send output signals to any number of higher level nodes. It is a binary tree in the sense that the number of inputs doubles every time one climbs up one level in the hierarchy.

Saturday, January 7, 2017

Raising Money for AI Research

Smartphone Apps

I refuse to solicit or accept money from anyone to finance my research because I don't want to be indebted to or controlled by others. So I recently came up with a plan to put some of the knowledge I have acquired over the years to good use and do it in a way that does not reveal my hand too much. I am working on two intelligent mobile applications as described below. Let me know if you think they might be useful to you.

1. Crystal Clear Smartphone Conversations

The first app will filter out all background sounds other than the user's voice during a call. It will also repair or clean up the user's voice by filling in missing signals if necessary. Can be activated or deactivated at the touch of a button. Advantage: Crystal clear conversations.

2. Voice-based Security

The second app will use both voice and speech recognition to eliminate passwords. It does this by asking the user to read a random word or phrase. This app can be used for unlocking the phone, accessing accounts, etc. If your voice changes over time or if you want to give someone else access to your accounts, the app can be reset in an instant. Advantage: High security and no need to remember passwords.


Although I think the first app has a better chance of being successful, I believe the second one is also doable. Some in the voice authentication and security business may disagree but the human voice is very much like a fingerprint. Every voice is unique in subtle ways that current technologies may not be able to capture. I use Microsoft Visual Studio and C# exclusively for programming. I will be using the Xamarin cross-platform tools to deploy the apps for the Windows Phone, the iPhone and Android phones. I don't anticipate needing GPU coprocessing.

I will release beta-test versions as soon as they are ready. Given my schedule, I anticipate the first app to be ready in two or three months.

The Ultimate Goal

If any of the apps is successful, I may venture into the hearing aid business. My plan is to generate enough funds to finance an artificial intelligence and computer research and development company. I believe that the requirements of true intelligence call for a new type of computer hardware and a better way to create software. My ultimate goal (or dream) is to build a truly intelligent bipedal robot that can do all your chores around the house such as cleaning, preparing food, babysitting the kids, doing the laundry, gardening, etc. A tall order, I know.

Wednesday, November 30, 2016

True Artificial Intelligence Will Arrive Suddenly and Will Stun the World


In this article, I argue that true artificial intelligence, aka artificial general intelligence or AGI, may arrive on the world scene within the next ten/fifteen years or even sooner. It will not be a gradual process. It will arrive suddenly and take the world completely by surprise.

True AI Will Not Come from the Mainstream AI Community

When I say that the arrival of true AI will take the world by surprise, what I mean is that it will come from an unexpected place. Don't wait for the mainstream AI community to figure out intelligence. That will not happen. Knowing what I know about the brain and intelligence, there is no doubt in my mind that mainstream AI scientists are completely clueless as to how to even approach the problem. They are clueless because over 99 % of AI research money currently goes into funding deep learning, which is, as I have explained elsewhere, a hindrance to progress toward true AI. The most important ingredient in intelligence is time. And yet, amazingly, time is a mere afterthought in AI research, especially deep learning.

There are a handful of AI researchers who do understand the crucial importance of time to intelligence but, as I explained in my previous article, they are handicapped by their continued adherence to a representational approach to intelligence. In other words, in spite of all the hype, they are still doing symbolic AI or GOFAI. Please read, The World Is its Own Model or Why Hubert Dreyfus Is Still Right About AI for more on this topic.

Another obvious reason that the mainstream AI community is clueless is that they believe that the brain is performing some kind of massive parallel computation on sensory inputs. They assume that the brain continually generates an internal model of the world using statistical calculations on its input signals. The problem with this view is that neurons are way too slow for this kind of signal processing. The surprising truth is that the brain does not compute anything when it perceives the world. The brain assumes that the world is deterministic and does its own computations. It learns how the world behaves and expects that this behavior is perfect and will not deviate. The mechanism is akin to an automatic coin sorting machine whereby the machine assumes that the different sizes of the coins automatically determine which slots they belong to.

True AI Will Arrive Suddenly

A truly intelligent system, such as the human brain, consists of multiple, highly integrated modules. What I mean is that every module that comprises an intelligent system has a specific function, organization and operation that complement the other modules. No single module can function in isolation. It is not possible to solve one aspect of intelligence without also solving all the other aspects. In other words, one cannot understand sensory perception without also understanding motor behavior, and vice versa. There will be no evolution during which advances are made a little at a time while machines become gradually more intelligent over the years until a time is reached when they achieve human-like intelligence. True AI will appear suddenly.

The Secret of True AI Will Come from a Completely Unexpected Source

The most surprising thing about the arrival of true AI on the world scene will not be that it is finally here (although that will certainly make the front pages) but where it came from. I am not going to say too much about this other than the following. True AI is so counterintuitive that it would take us (humanity) hundreds, if not thousands of years to figure it out on our own. Fortunately for us, there is an ancient source of scientific knowledge about the brain and intelligence that the world has chosen to ignore. I have worked for more than a decade to decipher and understand this knowledge and I have made great progress. But whether or not I publish my work is not up to me. The only caveat here is that I am a known internet nut. Stay tuned.

The World Is its Own Model or Why Hubert Dreyfus Is Still Right About AI
Why Deep Learning Is a Hindrance to Progress Toward True AI
In Spite of the Successes, Mainstream AI is Still Stuck in a Rut

Sunday, July 10, 2016

The World Is its Own Model or Why Hubert Dreyfus Is Still Right About AI


In this article, I argue that mainstream artificial intelligence is about to enter a new AI winter because, in spite of claims to the contrary, they are still using a representational approach to intelligence, aka symbolic AI or GOFAI. This is a criticism that Hubert Dreyfus has been making for half a century to no avail. I further argue that the best way to get rid of the representationalist baggage is to abandon the observer-centric approach to understanding intelligence and adopt a brain-centric approach. On this basis, I conclude that timing is the key to unlocking the secrets of intelligence.

The World Is its Own Model

Hubert Dreyfus is a Professor of philosophy at the University of California, Berkeley. Dreyfus has been the foremost critic of artificial intelligence research (What Computers Still Can't Do) since its early days. The AI community hates him for it. Here we are, many decades later, and Dreyfus is still right. Drawing from the work of famed German philosopher, Martin Heidegger and the French existentialist philosopher, Merleau-Ponty, Dreyfus's argument has not changed after all those years. Using Heidegger as a starting point, he argues that the brain does not create internal representations of objects in the world. The brain simply learns how to see the world directly, something that Heidegger referred to as presence-at-hand and readiness-to-hand. Dreyfus gave a great example of this in his paper Why Heideggerian AI Failed and how fixing it would require making it more Heideggerian (pdf). He explained how roboticist Rodney Brooks solved the frame problem by moving away from the traditional but slow model-based approach to a non-representational one:
The year of my talk, Rodney Brooks, who had moved from Stanford to MIT, published a paper criticizing the GOFAI robots that used representations of the world and problem solving techniques to plan their movements. He reported that, based on the idea that “the best model of the world is the world itself,” he had “developed a different approach in which a mobile robot uses the world itself as its own representation – continually referring to its sensors rather than to an internal world model.” Looking back at the frame problem, he writes:
And why could my simulated robot handle it? Because it was using the world as its own model. It never referred to an internal description of the world that would quickly get out of date if anything in the real world moved.
Deep Learning's GOFAI Problem

By and large, the mainstream AI community continues to ignore Dreyfus and his favorite philosophers. Indeed, they ignore everyone else including psychologists and neurobiologists who are more than qualified to know a thing or two about intelligence and the brain. AI's biggest success, deep learning, is just GOFAI redux. A deep neural network is actually a rule-based expert system. AI programmers just found a way (gradient descent, fast computers and lots of labeled or pre-categorized data) to create the rules automatically. The rules are in the form, if A then B, where A is a pattern and B a label or symbol representing a category.

The problem with expert systems is that they are brittle. Presented with a situation for which there is no rule, they fail catastrophically. This is what happened back in May to one of Tesla Motors's cars while on autopilot. The neural network failed to recognize a situation and caused a fatal accident. This is not to say that deep neural nets are bad per se. They are excellent in controlled environments, such as the factory floor, where all possible conditions are known in advance and humans are kept at a safe distance. But letting them loose in the real world is asking for trouble.

As I explain below, the AI community will never solve these problems until they abandon their GOFAI roots and their love affair with representations.

The Powerful Illusion of Representations

The hardest thing for AI experts to grasp is that the brain does not model the world. They have all sorts of arguments to justify their claim that the brain creates representations of objects in the world. They point out that MRI scans can pinpoint areas in the brain that light up when a subject is thinking about a word or a specific object. They argue that imagination and dreams are proof that the brain creates representations. These are powerful arguments and, in hindsight, one cannot fault the AI community too much for believing in the illusion of representations. But then again, it is not as if knowledgeable thinkers, such as Hubert Dreyfus, have not pointed out the fallacy of their approach. Unfortunately, mainstream AI is allergic to criticism.

Why the Brain Does Not Model the World

There are many reasons. I'll just list a few as follows.
  • The brain has to continually sense the world in real time in order to interact with it. The perceptions only last a short time and are mostly forgotten afterwards. If the brain had a stored (long-term) model of the world, it would only need to update the model occasionally. There are not enough neurons in the brain to store a model of the world. Besides, the brain's neurons are too slow to engage in any complex computations that an internal model would require.
  • It takes the brain a long time (years) to build a universal sensory framework that can instantly perceive an arbitrary pattern. However, when presented with a new pattern (which is almost all the time since we rarely see the same exact thing more than once), the cortex instantly accommodates existing memory structures to see the new pattern. No new structures are learned. A neural network, by contrast, must be trained with many samples of the new pattern. It follows that the brain does not learn to create models of objects in the world. Rather it learns how to sense the world by figuring out how the world works.
  • The brain should be understood as a complex sensory organ. Saying that the brain models the world is like saying that a sensor models what it senses. The brain builds a huge collection of specialized sensors that sense all sorts of phenomena in the world. The sensors are organized hierarchically. They are just sensors (detectors) that respond directly to specific sensory phenomena in the world. For example, we may have a high level sensor that fires when grandma comes into view but it is not a model of grandma. Our brain cannot model anything outside of it because our eyes do not see grandma. They just sense changes in illumination. To model something, one must have access to both a subject and an object. An artist can model something by looking at both the subject and the painting. The brain must sense things directly. It only has the signals from its senses to work with.
To Understand the Brain, Be the Brain

The most crippling mistake that most AI researchers make is that they try to understand intelligence from the point of view of an outside observer. Rather, they should try to understand it from the point of view of the intelligence itself. They need to adopt a brain-centric approach to AI as opposed to an observer-centric approach. They should ask themselves, what does the brain have to work with? How can the brain create a model of something that it cannot see until it learns how to see it?

Once we put ourselves in the brain's shoes, so to speak, representations no longer exist because they make no sense. They simply disappear.

Timing is the Key to Unsupervised Learning

The reason that people like Yann LeCun, Quoc Le and others in the machine learning community are having such a hard time with unsupervised learning (the kind of learning that people do) is that they do not try to "see" what the brain sees. The cortex only has discrete sensory spikes to work with. It does not know or care where they come from. It just has to make sense of the spikes by figuring out how they are ordered. Here is the clincher. The only order that can be found in multiple sensory streams of discrete signals is temporal order: they are either concurrent or sequential. Timing is thus the key to unsupervised learning and everything else in intelligence.

One only has to take a look at the center-surround design of the human retina to realize that the brain is primarily a complex timing mechanism. It may come as a surprise to some that we cannot see anything unless there is motion in the visual field. This is the reason that the human eye is continually moving in tiny movements called microsaccades. Movements in the visual field generate precisely timed spikes that depend on the direction and speed of the movements. The way the brain sees is completely different from the way computer vision systems work. They are not even close.

New AI Winter in the Making

Discrete signal timing should be the main focus of AI research, in my opinion. It is very precise in the brain, on the order of milliseconds. This is something that neurobiologists and psychologists have known about for decades. But the AI community thinks they know better. They don't. They are lost in a lost world of their own making. Is it any wonder that their field goes from one AI winter to the next? Artificial intelligence research is entering a new winter as I write but most AI researchers are not aware of it.

See Also

Mark Zuckerberg Understands the Problem with DeepMind's Brand of AI
Why Deep Learning Is a Hindrance to Progress Toward True AI
In Spite of the Successes, Mainstream AI is Still Stuck in a Rut

Monday, July 4, 2016

Why We Have a Supernatural Soul


In this article, I argue that we consciously experience something that is provably nonexistent in the physical or material universe. Therefore, it can only be the result of a non-material entity.

From Neuronal Pulses to the Illusion of Distance

To deny the existence of an immaterial or supernatural soul is to stop believing one's own eyes. The amazing colorful 3D vista we think we see in front of our eyes is entirely supernatural. Why? Because there is no 3D vista in our visual cortex or anywhere else. Our visual cortex and our entire brain are just a bunch of firing neurons. Space and distance are not functions or properties of neuronal pulses. Every pulse is pretty much identical to another. The only thing that matters in the brain, as far as intelligence is concerned, is the temporal relationships between the pulses. They are either concurrent or sequential.

We certainly do not sense biochemicals and electric pulses flowing through our axons, synapses and dendrites. We see a fabulous, dynamic model of the world in glorious 3D. Something must have translated those neuronal firings into a colorful 3D vista. Call it spirit, soul or whatever. But it certainly exists and it is not material, a billion materialists claiming otherwise notwithstanding.

Why (Space) Distance Is an Illusion of the Soul/Spirit

It can be logically shown that space (distance) does not exist at all. It is an illusion, i.e., a creation of the mind. I posted an article on this topic back in 2010. Let me just repeat the main argument below. The reason that space/distance is an illusion is that the existence of space leads to an infinite regress. Over the years, I have found that almost everything that is fundamentally wrong with classical physics has to do with infinite regress. Note that physical space is defined as a collection of positions existing apart from particles. The idea is that, in order for any physical entity or property to exist, it must exist at a specific position in space. But if a position is a physical entity that exists, it too, must exist at a specific position. In other words, if space exists, where is it? One can posit a meta-space for space, and a meta-meta-space for the meta-space, but this quickly turns into an infinite regress. The only possible conclusion is that there is no such thing as space. It is an illusion of perception.

The Society of the Soul

Again, we must ask the essential question. If space/distance does not exist, why do we see and consciously experience a 3D vista? Where does it come from? The answer should be obvious. Since it comes from neither the brain nor the external physical universe, it must come from some other realm, a parallel but complementary realm. It must be a non-physical phenomenon. This is undeniable.

I hypothesize that every soul/spirit consists of a huge number of individual parts (call them qualia, if you wish), each one of which is distinct from the other but each belonging to a single entity, the soul. The function of a quale is to give a unique meaning to the neuronal pulse it is associated with in order to distinguish it from another. In other words, there is a unique quale for every conscious pulse event in the cortex. The illusion of space is a manifestation of a subassembly of "positional" qualia. The soul is thus a society of qualia.

Conscious versus Unconscious Neurons

But what about the cerebellum which is completely unconscious while being very active during waking hours? The cerebellum is a parallel brain, a pure automaton. It is a supervised, sensorimotor behaving machine that handles routine tasks for us (e.g., walking, balancing or maintaining posture) while the conscious cortex is busy thinking about other things. Why is it unconscious? Obviously, as an automaton, it does not have to be. Its function is not to pay attention to anything in particular but to make it possible for the brain to focus on more important matters. Without it, we would not be able to walk and speak or even think at the same time.

In my opinion, future neurological studies will reveal the existence of a fundamental physicochemical difference between the working of cortical neurons and of cerebellar neurons. There is something qualitatively special about the physiology of some (not all) cortical neurons that makes it possible for the quales to interface with them. I am also willing to bet that future experimental research will show that this special property is missing in animals. Only human cortices have them.

See Also

Why Space (Distance) Is an Illusion

Thursday, June 30, 2016

The Case Against Superintelligence

Limited Attention Span

The superintelligent machine is a flawed concept, in my opinion. It is based on the assumption that the IQ of a single intelligent system can increase indefinitely. There are two problems with it. First, an intelligent system can only focus on one thing at a time. This imposes a severe limitation on the learning capability of the system. While it is possible to increase the learning speed of a machine by feeding it high speed data such as videos, learning to interact with the real world will still have to be done in real time.

The Tree of Knowledge

Second, knowledge is necessarily organized hierarchically in memory. This means that there are many branches in the tree of knowledge. This is the only way to store a huge amount of information in a limited space and to organize it in a way that makes it readily accessible within a context. This is important because it allows the system to infer analogies, an essential characteristic of intelligence. In other words, items that share a branch belong to the same category and the activation of a low level item brings up all the other related items (one thought brings up other thoughts). It is up to the attention mechanism to scan these family relations and pick one to focus on.

The Superintelligent Society

The problem is that a superintelligence would have vast numbers of awakened relations (branches) to pick from. It would take an inordinate length of time to do so. This is why it is best to specialize in an area of knowledge and rely on the expertise of others. This is what humans do. In a sense, humanity is already a superintelligent system consisting of millions of interacting individuals specialising in various areas of knowledge. The system, as a whole, is much more intelligent than any individual can ever be. The internet has accelerated communication between individuals making our global superintelligence even more efficient.

So I don't see a future dominated by a single or a few superintelligent machines but one in which many highly specialized artificial intelligences form a superintelligent society.

Saturday, June 18, 2016

Why Steven Carlip Is Mistaken about the Speed of Gravity or Why LIGO Is Still a Scam


This is a continuation of my previous post, Why LIGO Is a Scam. Steven Carlip is a quantum gravity physicist and a proponent of Einstein's theory of general relativity (GR). He is the author of a famous 1999 paper that purportedly explains how GR gets around the problem of the finite speed of gravity. In this article, I argue that Carlip's explanation is flawed because the GR model is self-contradictory. I further argue that Carlip is equally wrong about the speed of the electric field and that, as a result of his wrong assumptions, he failed to notice some extraordinary physics. I will base my argument on this explanation by Carlip directed at the layperson: Does Gravity Travel at the Speed of Light?

Carlip Is Wrong About the Symmetry of the Gravitational Field

To his credit, Carlip acknowledges that the speed of gravity has never been measured because, according to him, "such an experiment is beyond current technological capabilities." It is a concession that the hypothesis that he defends is not supported by observation. But I'll explain in a minute why it is not true that the experiment is beyond current technological capabilities. Carlip then succinctly and precisely describes the problem:
In the simple newtonian model, gravity propagates instantaneously: the force exerted by a massive object points directly toward that object's present position. For example, even though the Sun is 500 light seconds from the Earth, newtonian gravity describes a force on Earth directed towards the Sun's position "now," not its position 500 seconds ago. Putting a "light travel delay" (technically called "retardation") into newtonian gravity would make orbits unstable, leading to predictions that clearly contradict Solar System observations.
I should first point out that the Newtonian model says nothing about the "propagation" of gravity. There is no propagation because it assumes that gravity is instantaneous. This being said, even though he does not realize it, Carlip's own understanding of Newtonian physics proves that changes in gravity are felt instantly everywhere. This is because Newton made another equally crucial assumption. He assumed that gravity is perfectly symmetrical or spherical around a homogeneous gravitational source. This is given by the inverse square law and this is what is observed. Ironically, GR makes the same assumption (inverse square law) even though a finite speed of gravity would break the assumed symmetry if the gravitational source is moving. The GR model is thus self-contradictory.

Think about it. This is not complicated logic for propellerheads. If gravity propagated at the speed of light, the gravitational field would be flattened in front of the moving body and elongated in the rear. This is not observed. So the fact that the gravitational field is consistent with a symmetrical expectation given by the inverse square law and with an assumption of nonlocality (i.e., instantaneous action at a distance), it is logical to conclude that the "speed of gravity" has in fact already been measured and that there is no propagation to speak of. Carlip stumbles right out of the gate but he does not notice.

Carlip Is Wrong About Relative Motion

The above argument fully falsifies Carlip's position and there is really no need to go further. But Carlip continues unphased. He writes (emphasis added):
In general relativity, on the other hand, gravity propagates at the speed of light; that is, the motion of a massive object creates a distortion in the curvature of spacetime that moves outward at light speed. This might seem to contradict the Solar System observations described above, but remember that general relativity is conceptually very different from newtonian gravity, so a direct comparison is not so simple. Strictly speaking, gravity is not a "force" in general relativity, and a description in terms of speed and direction can be tricky. For weak fields, though, one can describe the theory in a sort of newtonian language. In that case, one finds that the "force" in GR is not quite central—it does not point directly towards the source of the gravitational field—and that it depends on velocity as well as position. The net result is that the effect of propagation delay is almost exactly cancelled, and general relativity very nearly reproduces the newtonian result.
Carlip's key argument here is in the last two sentences in the above paragraph. It has to do with velocity and position. He is arguing that, by some unexplained magic, information about the velocity and position of a massive body, such as the sun or a planet is transmitted to other bodies. This way a receiving body can, by some other unexplained magic, extrapolate the actual position of the emitting body and react accordingly.

The problem, as I explained in my previous post, is that GR only allows relative position and motion. A body cannot transmit information about its position and motion because it has no way of knowing what they are according to the theory. Since absolute motion and position are forbidden, the body would have to know its instantaneous velocity relative to every other body in the universe. This would require instant communication between it and the other bodies. This, too, is forbidden by the theory. Carlip is stuck between a rock and a hard place. His argument is now fully lodged in the crackpottery sphere.

Carlip Is Wrong About the Speed of the Electric Field

It gets better. Carlip goes on to use an analogy that reveals revolutionary new physics (assuming, of course, that one has an open mind and is not in the habit of kissing ass) but, unfortunately for Carlip and the rest of the world, the whole thing flies right past him unaware. He writes (emphasis added):
This cancellation may seem less strange if one notes that a similar effect occurs in electromagnetism. If a charged particle is moving at a constant velocity, it exerts a force that points toward its present position, not its retarded position, even though electromagnetic interactions certainly move at the speed of light. Here, as in general relativity, subtleties in the nature of the interaction "conspire" to disguise the effect of propagation delay. It should be emphasized that in both electromagnetism and general relativity, this effect is not put in ad hoc but comes out of the equations. Also, the cancellation is nearly exact only for constant velocities. If a charged particle or a gravitating mass suddenly accelerates, the change in the electric or gravitational field propagates outward at the speed of light.
Of course, Carlip's argument about the speed of the electric field fails for the same reasons that his argument about the speed of gravity fails. But it is amazing how a false assumption can color one's judgement. Carlip (and the entire mainstream physics community) is so bent on proving that information can only travel at the speed of light (Einstein's local universe), he fails to realize that his chosen example actually proves the opposite of the position he is defending. It reveals something about the electrostatic field that has been right under their noses for over a century. They cannot see it because they got their Einstein blinders on.

What Carlip does not realize is that his argument actually shows that the electric field is instantaneous, just like gravity. His claim that "electromagnetic interactions" move at the speed of light is only partially true. Only magnetic radiation moves at the speed of light. Changes in the electric field of a charged particle are obviously felt instantaneously everywhere. If it were not so, the electric field of a moving electron would be lopsided resulting in a lopsided or non-symmetrical universe. A purely symmetrical field is what is observed and this is why the electric field of a moving charged particle moves precisely with the particle. There is no delay due to propagation. Carlip's interpretation is bogus. (Christian and Jewish readers only: see special note at the end for a surprising take on this.)

Carlip Is Wrong About Binary Pulsars

Carlip then brings binary pulsars to the rescue, a sign of a weak argument, if you ask me. He writes:
While current observations do not yet provide a direct model-independent measurement of the speed of gravity, a test within the framework of general relativity can be made by observing the binary pulsar PSR 1913+16. The orbit of this binary system is gradually decaying, and this behavior is attributed to the loss of energy due to escaping gravitational radiation. But in any field theory, radiation is intimately related to the finite velocity of field propagation, and the orbital changes due to gravitational radiation can equivalently be viewed as damping caused by the finite propagation speed. (In the discussion above, this damping represents a failure of the "retardation" and "noncentral, velocity-dependent" effects to completely cancel.)
At this point, is anybody stupid enough to take anything that comes out of Steven Carlip's mouth or anybody else in the physics community at face value? Remember that these are people who see nothing wrong with using the word "virtual" like a magic incantation to instantly poof away violations in the conservation of energy that break their model. Notice how Carlip talks about being "within the framework of general relativity." In other words, the theory is assumed to be correct a priori. So every observed phenomenon must be explained within the framework of the theory. This is not science, of course. It is religion.

So, if the orbit of a binary pulsar system is decaying, in their minds, it can only be because GR predicted it. Does GR explain what causes gravity? Of course not. But, in spite of this glaring ignorance, they are certain that the decaying orbit is only possible because gravity propagates at c. I can think of a simple and mundane reason that the orbit is decaying. It is more than likely because of friction caused by collisions with other matter (such as an atmosphere) present in the system. I simply cannot accept Carlip's explanation because it is so ridiculously wrong in ways that I have already mentioned.

LIGO is Still a Scam

As I wrote previously, since gravity is undeniably instantaneous and gravitational waves are based on the hypothesis that changes in gravity propagate at c, there can be no doubt that the LIGO project is a scam. Why do I say a scam and not just a mistake? First of all, the intensity of the propaganda effort expended on convincing the public of the importance of the research is unprecedented. The inconvenient truth is that LIGO has exactly zero benefits for the public at large. Zilch. Second, this is probably the first time that the scientific community has come out and made an announcement about a major scientific discovery without corroboration by independent researchers conducting independent experiments. Third, it is very easy to fake a discovery because the LIGO system is designed so that fake signals that are indistinguishable from expected signals can be injected into the system at the push of a button. Supposedly, this is so that the system and the physicists attending to the experiment can be tested. The fake signals are so convincing that the LIGO team once completely fell for it. A paper was written and ready for publication before the fakery was revealed at the last minute. How easy would it be to fake other signals, you ask? Very easy. New Scientist wrote an interesting article about this in February. Here's a quote:
The Laser Interferometer Gravitational-wave Observatory (LIGO) made history last week when it announced the first direct discovery of the ripples in space-time predicted by Albert Einstein 100 years ago. But the LIGO team is infamous for testing its system by inserting fake signals that are only revealed to be false at the last minute.
But even in the event that some signal was genuinely detected, it would not prove that changes in gravity travel at c. That would be circular reasoning. A proof (and even Carlip would agree) would require the simultaneous detection of an electromagnetic signal, such as gamma rays, emitted by the same source. So far, at least two gravitational wave signals have been "detected" but, strangely, none were accompanied by an EM signal. Strange indeed. It may be easy to fake gravitational waves but faking EM waves is a different ball game altogether. I predict that the whole thing will come back and bite the physics community in the ass and destroy any trust that the public had in the scientific enterprise.

The Physics Community Can Kiss My Ass

My detractors will undoubtedly ask, what makes you think you are smarter than the entire physics community? My answer is that I am not smarter at all. I just got more gonads than they do. I have said this before. Unlike most physicists, I am not an ass kisser and the physics community does not put food on my table. And even if they did, I would still tell them to kiss my ass. Besides, I am a rebel at heart. This world is so full of lies, I accept almost nothing from anybody at face value.

Physicists are wrong about gravity. They are wrong about time. They are wrong about space. They are wrong about motion. They are wrong about the Big Bang, black holes and wormholes. They are wrong about dark energy and dark matter. They are wrong about quantum superposition. They are wrong about so many things, it boggles the mind that they can maintain any kind of sanity or dignity. I have written about many of these things before. Click on the links below if you are interested.

See Also

Aberration and the Speed of Gravity (Carlip)
Does Gravity Travel at the Speed of Light? (Carlip)
Why LIGO Is a Scam
Why Space (Distance) Is an Illusion
How to Falsify Einstein's Physics, For Dummies
How Einstein Shot Physics in the Foot
Nasty Little Truth About Spacetime Physics
Why Einstein's Physics Is Crap
Physicists Don't Know Shit
Nothing Can Move in Spacetime
Physics: The Problem with Motion
Why Gravitational Waves Are Nonsense
Physics: The Surprisingly Simple Reason that the Speed of Light Is the Fastest Possible Speed and that Particle Decay Is Probabilistic

Special Note. This is only for the benefit of Christian and Jewish readers. The electric field and the electron were described metaphorically by Ezekiel thousands of years ago. Ezekiel used the metaphor of a wheel within a wheel that moves in unison with four living creatures. The electron is represented by four creatures because it is a composite particle consisting of four sub-particles each having a quarter charge. Surprise! Physicists are aware of the quarter-charge composite nature of the electron but since it does not fit the Standard Model of particle physics according to which the electron is elementary, they have taken to calling the sub-electronic particles, "quasi-particles". Physics via labelling is an age-old, shameless practice in the physics community. More on this in a future post.