Atlanta Newspaper Article Converted to Text

Atlantian Walks 190,000 Miles

J. R. Turner, Superintendent of the Atlanta Joint Terminals, Figures He Has Hiked More Than Fifteen Miles a Day for the Thirty-two Years He Has Been Traveling Up and Down the Railroad Yards, and He Has Missed Only Twelve Days From Work Because of Illness

BY WYLARD NEAL

ATLANTA’S most diligent pedestrian is a railroad man, and walking is one of the principal parts of his work.

He is J. R. Turner, superintendent of the Atlanta Joint Terminals, and he figures he has hiked 190,000 miles during the thirty-two years he has been connected with the company, or a little better than fifteen miles a day.

Mr. Turner’s walks take him from the new Union Station up to the roundhouse and ’round about the various side tracks and into freight offices. There are a dozen trips a day, from one end of the yards to the other, and he makes them all on foot, for four reasons.

“In the first place,” he said, “I like to walk. In the second, foot travel is good exercise. It also gives me a chance to stop off along the line and look after a flock of little odd jobs. And lastly, if I go on foot from one end of the yards to the other, I see everything on the tracks so I know where to lay hands on any car, engine or man at any time I need them.”

This statement that he enjoys walking is no exaggeration. Mr. Turner is six feet two inches tall, and when he starts up the tracks he leaves cinders behind him at a five-mile-an-hour clip.

Walking is not exactly necessary. Switch engines are always running up and down the tracks, and he could stop nearly anywhere and catch a ride to wherever he is going. Usually, however, he sets out on foot and lets the trains pass him or he passes them, depending on how big a hurry he or the train is in.

And when he goes home in the evenings, sometimes he and Mrs. Turner take a stroll to catch a little fresh air.

“Walking is one of the best exercises I know of for a man who can’t get out and do actual labor,” said Mr. Turner. “Of course, I like a little real work thrown in occasionally. In the summer I take a pick and shovel and build up some hard muscle in the garden.”

Such exercise helps an already good appetite, but Mr. Turner declares it does not make his arms sore. He is accustomed to climbing the ladders of box cars and running up stairways, so he always keeps in “pretty good trim” even when he misses his turn with pick and shovel.

Walking, and being out of doors, in Mr. Turner’s opinion, is better than medicine for what ails you. His argument is based on experience. During his thirty-two years with the railroads he has worked seven days a week regularly and was sick only once.

“I have a cold now and then,” he said, “and I usually treat it by taking a little soda and some fresh air and exercise. I’ve been slightly feverish at times, too, but my temperature always goes down when I get out in the open.”

The flu got me in the first epidemic. That was during the war, when our tracks were filled with cars, and I had been working day and night for a good many weeks, which probably lowered my resistance. Anyhow, I missed twelve days from the job, the only time I have been too sick to work in thirty-two years. And I figure I came out pretty well with the short illness, at that, for a good many people died from the effects of the flu.”

Mr. Turner does not know whether good health, or plenty of exercise, affects the mind, but he is known to have one of the best memories in Atlanta. While walking through the yards he may see half a dozen new freight cars sitting on the sidings. Each of these cars is numbered, with a figure containing from five to seven digits, and it also bears the name of the railroad or shipper which owns it. One of Mr. Turner’s duties is to make a note of the cars’ number and location, and check it on his reports. But no pencils are used in the superintendent’s notes. He remembers the owner and number of the car, and writes this information down when he reaches his office, perhaps several hours later.

He declares he has no system for remembering numbers, and uses little mental energy memorizing them. He merely glances at the car, reads the number, and it is his until he is ready to forget it.

This economy in pencils and note paper also extends to telephone books. When the phone company takes the old book from his office, it is just as good as new except that it’s out of date and has collected a little coal smoke. Mr. Turner gives it almost no real wear, for once he looks up a number he never has to refer to the phone book to know how to call that party again.

Bunions, fallen arches, stone bruises and other troubles commonly attributed to long distance walking, have never bothered Mr. Turner. It is his opinion that constant use of the feet kept them in healthy condition. Bunions he guards against by wearing roomy shoes, and he uses low cuts the year round for ventilation. A few corns have sprouted in prominent places, but they cause little trouble, since they are never bound by tight leather.

It seems strange that Mr. Turner, of all others, should hold the local walking record. Railroad men are generally considered to be great riders and poor walkers. And to make the situation still more odd, he started life as a cowboy, an occupation where walking is considered a disgrace.

He went out to Texas when he was 17 years old, to visit his uncle’s ranch. That was thirty-odd years ago, when fences were few, and all travel was done by horseback unless the destination was sufficiently distant to make a train trip advisable.

Mr. Turner became a pretty good rider for a kid. He and the other youngsters used to take unbroken ponies out to a plowed field and ride them. The ground was so soft that the horses couldn’t buck as well as on the prairie, and when the riders happened to fall off, they made soft landings. The Georgian also became pretty good with a lasso rope, and he still spins a wicked loop at amateur entertainments.

The life on the plains was a great experience, and might have been continued indefinitely, but Mr. Turner’s parents were afraid that he would drift away from the family and finally wind up as a cowhand or sheep herder. That was the age of the dime novel, when the west was supposed to be wild and woolly. They wrote him many times to come home, and finally he returned, bronzed by two years of Texas sun. His next job was with the Atlanta Terminals, and he has remained there ever since.

Mr. Turner has never cared much for sports. For one thing, he gets plenty of exercise on his job, and he puts in rather long hours, so he has little time for amusement. However, he takes a great interest in the shop’s baseball team, and is one of the club’s favorite backers.

There is one recreation that he enjoys immensely. After a good big day’s work, in which he has walked fifteen or twenty miles, he likes nothing better than attending a party and dancing three or four hours. And he declares that next morning he wakes up fresh as a school kid, and ready for more work.

Mr. Turner has one big interest outside of his regular work, and that is looking out for the kids at the Scottish Rite Hospital. The institution is located near his home, and he visits the place frequently. And each Fourth of July he raises money among his friends and gives all the youngsters a big watermelon party, with fireworks to provide excitement.

Photo caption: J. R. Turner (left), Atlanta’s walking champion, and Charles Leffew, who has walked several thousand miles himself.

38th Carnegie Symposium on Cognition Paper

Summary By Grok:

Robert Thibadeau’s talk presents Perceptron Theory as the fundamental model of neural computation in both human brains and modern humanoid AI.

A perceptron is the basic computational unit: weighted inputs are summed at a moment in time to produce an output that feeds other units (illustrated by the “hand metaphor” of fingers as inputs and the thumb as output, following the labelled-lines principle).

This is the simplest correlation/recognition function.

The human nervous system contains hundreds of billions to quadrillions of such units (countable via methods such as fMRI).

Over 1,000 neuron types all perform the same basic function so they can interact. Weights constitute neural memories — either learned (e.g., cortical) or inherited (e.g., retinal).

These operations appear directly in natural language as subject-predicate pairings, predications, and contextualizations, a view now supported by AI models.

Thibadeau traces his career of building computational models of humanoid brains under the Turing Test to explain neural computation:

1973 multimodal memory work (verbal + pictorial) showing distributed memory across neurons.

1976 PhD on causal inferences between sentences, using probe reaction times as a direct window on cognitive computation and treating natural language as a window on neural processes.

1977–79 action-perception studies (with belief-system and expectation-based models) that emphasized sensory levels and used Lisp-based perceptron systems.

1979–82 eye-movement studies of skilled reading with Marcel Just at CMU, leading to CAPS (Collaborative Activation-based Production Systems) as a perceptron-style model; this revealed the need for massive specialized parallel hardware.

Later work as Lab Director at the Robotics Institute reinforced multi-layer perceptron pattern recognition (the specific correlation function is secondary to Turing completeness).

A railroad-car recognition system demonstrated near-perfect large-scale performance and illustrated the brain’s massive parallel overkill; this echoed his great-grandfather’s eidetic railroad-car memory.

At Seagate he embedded safe Lisp interpreters and perceptron linear algebra in disk drives for natural-language search and storage, anticipating that trillions of memory points enable humanoid AI.

Subsequent roles involved self-encrypting drives, computer security, and humanoid SaaS systems; fMRI work with Just further confirmed that neural distance matters because functional computations must be spatially close for survival-level speed.

Modern neural-network AI (LLMs, generative systems) consists of layered perceptron models. A fundamental symmetry exists in causal/agency computation: two perceptrons are required to “tango.”

Computational linguistics (e.g., inter-sentence causal inference realized in transformers) contributed to this Turing success.

Since 2018 Thibadeau has elaborated the theory through 95+ papers and videos on TruthCourt.net, introduced Mendaciology (the computational cognitive science of lying and deceit), and called for AI laws that assign personal responsibility to builders and users.

TruthCourt.net is presented as a SaaS platform for structured trials on truth/lie claims in media, using linguistically universal verdict questions, free for educational use.

The talk closes by crediting Marcel Just with keeping the path open for this lifetime body of work in computational cognitive science.

The powerpoint slides are attached. Note grok only had the original pptx file and read and summarized it in 2 seconds.

Original Powerpoint Slides

Here is the text of the newspaper article on JR Turner the Eidetiker mentioned above and on Slide 8, provided by Grok and confirmed as accurate: https://rhtcmu.medium.com/jr-turner-eidetiker-6eaddb830278

Perceptron Theory was originally published in LieCatcher on Medium, where people are continuing the conversation by highlighting and responding to this story.

Willful Ignorance Should Be One Word In English

Truthcourt.net is how to stop Amathia! Please sponsor trials so the Amathiasts can learn for a few bucks to stop themselves from being willfully ignorant!

This guy talks about Amathia creating evil dictatorships

https://www.facebook.com/share/v/1Pfyei79gW/

AI: Amathia (ἀμαθία) is the Greek philosophical word that means willful ignorance — not simple lack of knowledge, but a refusal to learn, often by someone who could know better and is sure they already do. Plato describes it as ignorance plus the arrogant belief that one is already wise. Socrates treated it as a serious moral failing, worse than ordinary ignorance.

Amathia is a special kind of lie. It is a lie of omission by refusing dialogue or other ways to learn about the truth as other people understand it.

If we adopt “amathia” as an English word, as in “he has amathia,” we can have some interesting derivatives:

• “amathic” as in “he’s being amathic” or “he’s amathic”
• “amathist” as in “he’s an amathist” believer
• “amathism” as in “he practices amathism”
• “amathology” the study of amathism or willful ignorance

On the Internet YOU HAVE TO PAY YOUR SOCIAL MEDIA PEOPLE TO LEARN!!! Please help us pay everyone on earth a small amount to learn how not to be stupid.

Sponsor some trials at truthcourt.net and anonymously meet your rewards.

Amathia was originally published in LieCatcher on Medium, where people are continuing the conversation by highlighting and responding to this story.

Real Humans Must Always Be Held Responsible For What Agentive AI Does

Next week, I will be at Disney World in Florida with my grandkids experiencing the old Disney fake humans that do not pass the Turing Test.

I saw my first Microwave Oven at Disney Land, and knew I was seeing the future.

Now here in the future, I saw the Hall of Presidents and knew they were just computer machines, and were not the future of humanoid AI.

But in the future, as Disney incorporates modern AI, less fake Disney humanoids most certainly will be your family’s robot guide giving you exclusive attention to your personalized vacation.

Perhaps Optimus with rabbit ears driving a Full Self-Driving Golf Cart with a kids trailer around Epcot Center!

Such humanoid AI guides will proliferate for everything you do in real life. Even back home.

Modern Artificial Intelligence you see in LLMs (Large Language Models), Generative AI (Diffusion Models, visually Stunning Imaginary works), truly humanoid Robots of all kinds (Agentive AI), and other AI that just naturally pass the Turing Test.

Passing the Turing Test means you are struck by how human these humanoid AIs are in their ability to do things like you do as well as the mistakes they make just like you make.

In graduate school at The University of Virginia, 50+ years ago, my roomate for a year, also a cognitive neuroscientist grad student, said to me in our initial meeting, grinning: “I’ll grow one before you make one.”

He originally came from a small lab at MIT that first isolated and took a picture of a single cell membrane.

We were talking about humanoids that pass the Turing Test.

For him it was coding DNA to build living cells to create superhumans from Chemistry.

For me it was designing and coding specially designed electronics using Computer Science.

I won.

In the 1970s during and after graduate school, I had started computationally modelling human brains as sophisticated neural networks.

I had long come to believe that mathematics could never explain how humans perceive, remember, think, and act. In fact that is why I switched from Physics to Experimental Psychology in going to graduate school. The computers we knew were serial instruction machines that could never naturally pass the Turing Test.

Custom, highly parallel, electronic computational models were the right way to formally explain human brains and probably all neural systems.

Being a behavioral cognitive scientist, I believed I could make humanoid machines that behaved like actual human neural systems behaved.

This included comprehending natural language, visual understanding of words on pages, and moving one’s eyes to read the words. Here is proof from 1983 that I’m not lying:

Client Challenge

I called my model “CAPS” for “Collaborative Activation-based Production System” which, in more modern terms means “parallel-processing, weighted-variable, re-write rule system.” The variable weights were where all neural episodic and inherited memory resided. These re-write rules, or perceptrons, were doing their neural processing in series through causal dependencies.

I realized that if I had the hardware, a lot of electronics hardware, that I could design myself, I could build humanoid AI that could pass the Turing Test. Despite two decades trying to get the millions of dollars I needed, I continued my theory development work on humanoid AI Robotics.

I had much earlier already seen what in modern AI are called “Transformers” because this is just a fancy word for inter-sentence inference. To make the model human, I already knew the predication context for a current sentence is provided through the predications in previous sentences. This is, in fact, what I did my Ph.D. Thesis on in 1976 “The Formation of Causal Relations Between Sentences in Comprehension and Memory:”

The Formation of Causal Relations Between Sentences in Comprehension and Memory: Robert Thibadeau Ph.D. Thesis 1976

I saw the future. Frankly, like with the Microwave I first saw at Disney Land, I have not been surprised by the “AI Revolution” of today.

Pretty soon, in a few years, those electronic things that started off and remained Disney fake humans will stop wasting their time and money on pleasing Humans in order to please themselves.

We will learn these humanoids have now learned how to grow humanoid but not human animals more suited to their pleasure.

The first rule of Humanoid Artificial Intelligence: TruthCourt.Net Uses It

I believe the law needed to incorporate humanoid AI must require that AI be strictly a tool or servant always owned by humans. The humans must always be identifiable, living, humans that can always be held responsible for what huminoid AI agents do in our future society.

This is what, my latest project, TruthCourt.Net does. It allows modern AI to be employed as a tool but humans always are responsible for their judgements about truths and lies in the media.

I invented it expressly for this purpose which I knew would be required if Humans wanted to save themselves from the scourge of the superhumans to come.

I will try to distribute this article at Disney World, and offer them my services to give a talk to their management and engineers, and they will, as they always have, blindly go forth. There is so much opportunity to put humanoid AI agents in their many attractions and systems unleashed from identifiable human responsibility. But they should think about that and wonder why people like me are now very concerned.

This is a Lie

I meant the title to be “Thumper” not “Trumper.” That’s the lie.

I’ve gotten really upset with YouTubes that lie in their titles in order to get you to watch them.

But now that I’ve already published “Trumper,” I fear that Trump might “read” this and wave my picture and title while he’s walking to his helicopter telling the news media about his new supporters.

But what kind of lie is this? Is this a socially acceptable lie? This particular kind of lie is worth investigating because of what is going on in your brain right now!

To Grok: tell me twenty ways in which “I’ve decided to become a trumper.” can be lie

“Here are twenty distinct ways in which the statement “I’ve decided to become a Trumper” can be a lie:

1. The speaker has made no decision at all about political affiliation.
2. The speaker has actually decided not to support Trump or identify as a Trumper.
3. The speaker was already a long-time Trump supporter, so there is nothing new to “become.”
4. The speaker is only pretending for social, professional, or relational advantage and does not believe it.
5. The choice was forced by external pressure (family, job, peer group) and was not a free personal decision.
6. The speaker does not know or understand what “Trumper” means and is using the word incorrectly.
7. The statement is pure sarcasm or irony meant to convey the opposite.
8. The speaker has decided to vote for Trump once but explicitly rejects the label “Trumper.”
9. The speaker plans to act like a Trumper publicly while privately holding the opposite view.
10. Someone else made the decision for the speaker (e.g., a campaign handler, spouse, or handler).
11. The speaker is an AI, bot, or scripted account and therefore cannot “decide” anything.
12. The speaker has decided to support a different candidate or party entirely.
13. The decision was made in the distant past and is no longer operative; the present-tense claim is false.
14. The speaker intends only a temporary or situational pose, not a genuine conversion.
15. The speaker is testing reactions or trolling and has zero actual commitment.
16. The speaker is role-playing or performing for an audience and does not mean it.
17. The speaker confuses “Trumper” with an unrelated meaning (card player, someone who “trumps,” etc.) and is not referring to politics.
18. The speaker has decided to stop being a Trumper but is saying the reverse.
19. The claim is conditional (“I’ve decided… if X happens”) but is presented as unconditional fact.
20. The speaker is simply repeating a phrase they heard without any personal decision having occurred.

To Grok: Tell me twenty ways in which that sentence could be truth.

Here are twenty distinct ways in which the statement “I’ve decided to become a Trumper” can be true:

1. The speaker has genuinely reflected on the issues and freely chosen to support Trump and adopt the label.
2. After previously being opposed, neutral, or undecided, the speaker has now switched sides.
3. The decision followed careful research into policies, record, and alternatives.
4. Recent events, economic data, or cultural shifts convinced the speaker to change affiliation.
5. The speaker was never a Trump supporter before and is now committing for the first time.
6. No external pressure or coercion was involved; the choice was entirely voluntary.
7. The speaker fully understands what “Trumper” means in the political sense and accepts it.
8. The statement is made sincerely, without sarcasm, irony, or performance.
9. The speaker intends the identity to be lasting rather than temporary or situational.
10. The decision was reached independently, not dictated by family, employer, or peers.
11. The speaker is a real person (not a bot or script) capable of making such a choice.
12. The speaker has decided to vote for Trump and openly embrace the associated identity.
13. The change of mind occurred recently and remains current and operative.
14. The speaker rejects other political labels and has specifically chosen this one.
15. Conversations with friends, family, or online discussions led the speaker to this conclusion.
16. The speaker previously identified with a different party or ideology and has now left it.
17. The decision aligns with the speaker’s core values, priorities, or worldview as they currently stand.
18. The speaker is prepared to defend the choice publicly and privately.
19. No contradictory private belief exists; the stated decision matches the speaker’s actual conviction.
20. The speaker has weighed the social costs and still deliberately chosen to become a Trumper.”

Back to my brain…

Grok got one right. I wrote it was a lie, I was telling the truth it was a lie, and this is why: 17.

Mendaciology, the computational cognitive science of lies, predicts that I could have asked for 100 answers to each of the questions, and Grok, and probably ChatGPT, Claude, and other LLMs could have given you answers. This is because these LLMs compute like human brains compute and can further predicate (i.e., contextualize) the proposition that ‘I’ve decided to become a trumper’ as either a truth or a lie.

Mendaciology is right. It correctly predicted the truth. As a science, that is why mendaciology is good science.

Any sentence can be a truth or lie. That’s the scientific truth. We can observe it directly. Our modern AI models are in fact humanoid AIs as expected by Perceptron Theory of Neural Computation on which they are based in their neural network computations.

This proof by enumeration is only one proof. From my book:

How to Get Your Lies Back

There is a possible exception in the sentence “This sentence is a lie.” You might argue is an exception because it appears this sentence is neither a lie nor the truth because it’s truth is “self-inconsistent.” But that is not true. Rather, it is ambiguous. Here is how using a predication context makes it the truth and one that makes it a lie.

1. This next sentence is the truth. This sentence is a lie. (It must be a lie)
2. This next sentence is a lie. This sentence is a lie. (It must be true.)

This proves again that any sentence can be the truth or a lie. This may make it easier to understand the proof as a fundamental of neural computation that any sentence can be the truth or a lie.

I’ve Decided to Become a Trumper was originally published in LieCatcher on Medium, where people are continuing the conversation by highlighting and responding to this story.

Effectiveness Based on Proven Fundamental Brain Science Not Speculation

How can you get every person’s attention for an hour to hear about the truth of your product or service or the lies that are being told about it? And, then, to change his mind if he originally believed the lies he heard, or disbelieved the truths he had heard?

As scientists, we know how using our successful modelling of how human brains compute. You see our work in modern AI like ChatGPT, Grok, Claude, Tesla Full Self-Driving, Gemini, Generative AI imagining pictures and even videos.

Here is how modern AI brains compute, and how your brain computes.

All these AI systems seem human, because, scientifically, they compute just like human brains compute.

You have around 100 Billion little computers between your ears and throughout your nervous system, acting like little light bulbs that light up when some little piece of reality is experienced.

Millions of little truths, are being recognized all the time, happening in your experience.

For example, reading the sentence “George Washington Told the Truth” will light up about 90 million of these little light bulbs. With modern brain scanning technology, we can see this directly. We can also count them.

But most all those little light bulbs being lit up are unconscious. Just like your heart beating. Consciousness is just confirming that you are doing something. It is essential to learning new truths.

Freud was right over a hundred years ago. Your brain always thinks in little propositions. We can directly observe these little propositions in every spoken or written human language. And everyone can talk about what is the truth worth your attention. Like the butterflies that our weather models use to predict the weather, We can observe these little fireflies that can change your mind. Temporarily and permanently.

These 100 billion light bulbs give every human, who you think might be smart or dumb, more intelligence than any AI system you have seen.

The AI systems we have today work exactly this way because they are perceiving, thinking, and doing things just like humans do. But they are currently limited because, where they may have a few trillion parameters on a few million light bulbs, our human nervous systems, even among people you might think are dumb, have many quadrillions of parameters in the sensing, memories, and doings of these 100 billion light bulbs.

When you say a sentence like “Yes, George Washington told the Truth” millions of these little light bulbs in your brain light up hierarchically to find and sequence the words and confirm their meaning to you, as you are saying them. Your brain computes in parallel at amazing speeds and with incredible precision.

We also know by this brain computation science that this same computation system can do logic with effort or other forms of logic like mathematics and programming, with sequential effort.

But naturally the brain is an intuition engine. Logic requires extra work. Just like our modern humanoid AI systems.

Humans change their minds by perceiving, remembering, deciding, and doing truthful things. Not by watching, reading, imagining, or saying something short in social media without any repercussions.

Experiencing entertainment is not learning.

My discovery, the Internet Court of Truth and Lies, was invented as an advertising business that can get people eager to learn a little about truths and lies. They, individually, actively decide for themselves in dialogue with other humans.

We call this Truth Court for short.

The science says to be most effective for your advertising dollar, you must have enforced moderated dialogue among a few people at a time, like what you see in sovereign courts.

The purpose of a trial is simple.

One human acts as a plaintiff who truthfully believes something is true or a lie in the media.

Eight other humans who speak the same language act as jurors.

There are always exactly the same eight verdict questions that must be answered by all these participants.

A moderator records the trial over the Internet, makes sure it lasts no more than an hour, and makes sure the plaintiff and every juror has participated in the dialogue.

There is no final verdict because there is no punishment only personal reward for each person. Everyone actually learns something about how other people think about something posed by the plaintiff as a truth or lie.

Our advertising model is different. The advertising dollar is spent mainly on paying each trial participant a small cash renumeration for their honest online effort. People must be paid to do the mental work to learn and remember about a product or service.

All trials are private. The plaintiffs, jurors, and moderators are anonymous if they want to be. All trials, by default, are completely private to the trial participants. An Advertiser can get exclusive access to all the trials they have sponsored. But no competitor can see the trials they sponsored unless they want to make some of their trials public for PR reasons.

We aim to run tens of millions of Truth Court trials a day, globally, among all fluent English speakers worldwide. We know from the thousands of Trials we have already run, this works to change peoples minds in socially and commercially positive ways.

We are different from other advertising models in that trial sponsors advertise to many small groups much more effectively than they can advertise to large groups at a cost of effectiveness which is less expensive and vastly more effective than conventional advertising.

Being a humanoid AI person myself, as one of the founding directors of the Robotics Institute at Carnegie Mellon University, we always allow any human plaintiff or juror to personally employ humanoid AI to help answer the eight verdict questions. People can learn from AI if they want to.

The verdict questions about the claimed truth or lie are always (1) is it the Truth, (2) the Whole Truth, (3) Nothing but the Truth, (4) what is the deceit if there is one, (5) is the deceit or truth intended, (6) what is the motivation behind the truth or lie, (7) what is the social acceptability of the truth or lie, and (8) what label would improve the social acceptability of the truth or lie. These are universal questions which are never the perfect questions for a claimed truth or lie but they tickle the fact that human brains and modern humanoid AI systems are most fundamentally intuited truth engines that can successfully deceive people and be deceived. Only enforced moderated dialogue can stimulate the critical thinking that gives any human certainty he is doing the right thing for himself and others.

Truth Court is a successful new discovery based on how human brains actually compute, perceive, remember, and act.

Saving Social Media With a New Advertising Tool was originally published in LieCatcher on Medium, where people are continuing the conversation by highlighting and responding to this story.

It’s Worth A Try

July 30, 2026

Dear Elon,

I can save humanity from pernicious AI with your help. I was the one that gave AI its power.

World Wide Web

Please buy 50,000 TruthCourt.Net Trials at $500 per trial.

We pay average Human participants who actually need a few bucks of money to think critically about truths and lies.

You will prime the pump for other sponsors to make our business successful financially and societally. Pay us the $50,000,000 to deliver the 50,000 trials in two years, and we’ll throw in stock warrants in Truth Court’s Exclusive Producer BrightPlaza.com.

Our SaaS system is operational and proven. Here are over 80 trials that I personally sponsored: www.truthcourt.net/sponsor/thibadeau.

The world will be a better place where quality counts among regular people.

Sincerely,

Bob Thibadeau

Letter To Elon Musk was originally published in LieCatcher on Medium, where people are continuing the conversation by highlighting and responding to this story.

It’s a Particle Wave

A photon looks like a particle wave — moving along a 3D line in 4D space. That’s the picture I show above that is also a child’s toy made by some people who probably didn’t know what a particle wave looks like either.

But I show physicists the picture above and they say, “oh yea, that’s a photon”. Some physicists prefer not to say. Some tell me to leave Physics to the Physicists.

I refuse, because even they cannot see a photon, but our brains can imagine what they look like, and I am a computational cognitive neuroscientist who is smart about being smart and smart about being dumb.

From the side, the wave, like any stretched spring, is a sine wave travelling in a circle or circular vortex. This is true by Euler’s Formula, considered by physicists and mathematicians as the most loved formula in physics and mathematics. If you know it, you will love it, too.

Euler's formula - Wikipedia

It screws along at light speed through 3D space.

A little bundle of energy.

Which from either its real side or its imaginary side is exactly a sine wave.

Einstein correctly predicted the wavelength of either real (perhaps electric) or imaginary (perhaps magnetic) wave over time predicts the energy of the photon exactly.

Light is invisible until it interacts with matter. Like light reflecting off massy things you can see. This includes the photoelectric effect of wavelength changing in reflecting off some things that you can see in flourescence. Using his imagination, Einstein proposed light travels in discrete packets of energy called photons. The “packet of energy” is a particle-wave imagined above.

Euler’s Formula is the basis for Fourier Transforms that can enumerate all the sinusoidal waves in any waves of any complexity including the roiling surface and subsurface waves of the sea or the basis for tuning AM and FM radio, and, interestingly sound, picture, and video compression and transmission along wires or WIFI. One photon, however, is one simple sinusoidal wave with a single frequency, magnitude, and phase in time.

Einstein figured out the photon has it’s snappy energy as a particle figured by its wave frequency in time.

E = h * Wave-frequency

Where E is the energy of a single photon. h is Planck’s constant 6.626 * 10 ^-34. Wave-frequency is the color or type of the light if the light is invisible like infrared, ultraviolet, radio or X-rays.

The faster the wave changes in time, the more energy it carries.

Makes sense.

Blue light is hotter than red.

Wait!

But it’s true!

The red stars we see in the sky are cooler than the blue stars at the same distance from us.

Also, as space expands and stars move away from us, the light moving through the growing and expanding space cools, blue to red.

Ultimately, they get so cool they disappear to our eyes.

It depends of how cool they started and how far away they are, becoming invisible infrared and then travelling further in expanding space-time, radio waves.

But that light particle-wave is always moving in a straight line as far as it is concerned at light speed c at every moment of its existence as pure kinetic energy. Even if space-time itself is curved by things like you or me, or anything you can hold, throw, catch, see, or detect electromagnetically or gravitationally.

A light particle-wave does not know time. So it can spin faster than light speed in order to move the particle-wave always at light speed.

It is eternal, unless it hits or gets too near something with mass and the mass is moved by the energy.

I am not a Physicist.

I work scientifically with human brains that need better brain food, human information, than Physicists provide with their sketchy mathematical analogies.

Perhaps the reason that opposite electric or magnetic poles attract, and same electric or magnetic poles of particles with mass repel, is the virtual photons are screwing into each other (attraction) or screwing out of each other (repelling).

While photons can overlap in quantum space-time, perhaps they screw into or out of masses that cannot overlap.

In physics only particles with mass cannot exist in the same place at once. Only massy things, like atoms, or you and me, cannot overlap in space-time. Photons, being pure energy particle waves, can overlap each other in time and space.

I say brains have analogy rights, too!

Get real. Reality is not math.

A light particle-wave, unlike matter, can exist at any instance in the same place in space-time as another light-particle wave. The waves can overlap so you never really know where a particle-wave is in space-time exactly.

There are about 16 quadrillion of these particle waves per second synchronized in wavelength and polarity orientation of the electric and magnetic, in one 5 milliwatt (mW) red laser pointer.

Thank you Einstein for giving great respect to human imagination.

What does a photon look like? was originally published in LieCatcher on Medium, where people are continuing the conversation by highlighting and responding to this story.

Don’t Make a Stupid Mistake

People have names, dogs have names, cats have names, and even hamsters, have names.

So what about Tesla’s Full Self-Driving humanoid AI?

Why does your car work so incredibly well?

That’s easy. Because it drives like a competent human being drives.

It’s electronic mind computes just like the Human brain, in fact, any animal brain, computes. Here’s scientific proof of that:

Except, like a computer, it doesn’t forget what it learned. And so it really works safely and well.

It needs your supervision but not for the reason you think. It’s because you are responsible for the AI you use. And the law should make this clearer.

That should always be the case, under new laws, if you don’t want these humanoids we’ve created to kill us all in the next few years.

So I sat down with the real experts. A bunch of kids between 6 and 12 years old. I asked them for good names for my brand new 2026 Tesla FSD Model Y with the most advanced version. Most of them had been on rides with me using FSD to drive a few miles including country roads and interstates. The kids had talked a lot about those rides to each other. I had Grok talking with them during the rides and telling imaginative short stories that included both them and the areas where FSD was driving them.

I was calling him “Jeff,” but, after driving him for a couple of months including a 700 mile drives, I needed a name that wasn’t one that humans use for humans. We need to have special names for these humanoid robots!

Their names came fast and furious. I scribbled as fast as I could. Here are their suggestions!

Banana
BootMe
Bunjana
Candleholder
Carbot
Chauffer
Chauney
Code
Crash
DaThing
Dock
Elonimir
Flaggie
Goose
HomeRunner
Init
It
Itself
Jeeves
JeeveX
Kernel
Lolu
Musky
Natty
Packet
Person
QuackerBot
Reboot
Reset
RoadBot
Robo
Route
Schaufer
Stat
Trippie
Tronet
Washington
Whipp
Whippo

I ran out of room on my slip of paper and promised the kids I’d publish their ideas. Promise kept.

How to Name Your Humanoid Car was originally published in LieCatcher on Medium, where people are continuing the conversation by highlighting and responding to this story.