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Chapter 13; A Beautiful Age – regarding Nano processors


Still, even with all these advances in microprocessor design, you earth-humans took forever to meet the challenge. Earth-humans needed to develop an organic brain or at least an organic memory that would grow as conditions required.

Finally, the earth-humans started growing computer memory from slime mold and bacteria.[1]

by Rex Merrifield

One organism of interest was Physarum polycephalum; slime mold. Slime mold could find food by sending out a series of protoplasmic tubes that act as a transport network for nutrients.

Slime mold had the ability to map the optimal route between feedings and foods to create the most efficient way of searching for and transporting nutrients throughout the organism. Postmodern earth-humans realized that they could train slime mold to recognize the difference between commands and data. Thus, the perfect Nano processor and memory problems were solved simultaneously.

Conventional microprocessors were extraordinarily efficient at performing repetitive tasks, However, consciousness and cognitive logic required highly efficient parallel processing.

Slime mold therefore, because of its parallel protoplasmic tubes, provided a third millennium solution.

It only needed to pass tests on navigating through mazes, calculate efficient networks, construct logical gates, and surpass all 64 bit microprocessors used for robot control.

‘Conventional computers have served us very well, and are good at doing specific things, but they are actually quite dumb.’ [2]

The earth-humans developed an electronic stackable ceramic substrate with a surface mounted network of slime mold tubules coated with conductive substances. Inputs included chemicals, light/electrical signals and other ambient attributes; the results were assessed electrically or optically.

The earth-human goal was to connect to – –  or insert these devices in – –  humans. They were intended to become prosthetic self-growing computing devices – – and memory – – that enhanced humans’ sensing and cognitive abilities.


Once the prosthetic devices were operating at full capacity the postmodern earth-humans recognized that the microprocessor was necessary but not sufficient to build a cyborg capable of being conscious, having a conscience, and able to cogitate its own surroundings

For another thousand years everything progressed rapidly with digital control, yet, the ability to have a cyborg reach the conscious state remained elusive.

Finally, in a small upstate New York Community – – which once had been a city with thriving computer industry and universities – – the Rainbow Computer was born. This event almost coincided with the earth being overrun by flora. Thankfully the religion of global warming – – renamed to climate change – – renamed to climate swerve – – had not been fully implemented; otherwise the concept would have been lost.

The Rainbow Computer was a computer that ran on the logic of color instead of a number system. Numbering systems such as the ‘representational’ binary, octal, decimal, and hexadecimal – – not to mention the floating point – – had been operating at their higher limits for hundreds of years. The microprocessors could no longer keep up with newest requirements.

Did you notice how I snuck in the work ‘representational’ in the previous thought transmission?

We really should discuss this because everything in the world was solidly defined in the world before Einstein[3] and this theory of ‘relativity’. Then everything became relational with respect to two bodies relative to each other; in space or time.

Therefore, I really need to thought-transmit to you the obvious concept of ‘representation’. 

Many earth-humans have defined representation. They discuss the representation of currency as denoted in future value or possibly as absolute value when disregarding inflation. Other symbol-representations are a globe symbolizing the earth, the wing signifying flight, or an icon of the sun standing for light or heat. You now that a globe is not the real earth and a wing is not real flight and that an icon of the sun does not produce real light or heat.  Yet, when it comes to numbers, why do you earth-humans think that a number must be a value such as represented in a binary, octal, decimal, hexadecimal or floatingpoint number?

Why can’t a number be represented by a color? The colors we currently accept are red, orange, yellow, green, blue, and violet. But wait! The color division used by Isaac Newton[4], in his color wheel, was: red, orange, yellow, green, blue, indigo and violet. In current divisions of the spectrum, indigo is often omitted

If Newton could pick his own colors – – and even adding indigo as an extra one – – why can’t we use colors to represent numbers? I now thought transmit to you a simple concept of allowing colors to represent numbers.

Red Orange Yellow Green Blue Violet
1 2 3 4 5 6


Here is a thought experiment for you earth-humans. How much does orange plus green represent?

SIX!  That is correct.

How about adding red plus orange plus violet

NINE! Super. You are on a roll.

Now what is the square root of red plus orange plus violet?

Three!   Correct again.

So, you see, doing math with colors is rather simple.

Let us take this representation a bit farther.

red + blue = violet = 6

blue + yellow = green = 4

yellow + red = orange = 2

yellow + orange = yellow-orange = 2.8  

Here we have the dominant color of yellow and orange is adjacent, therefore the value is adjusted downward.

orange + red = red-orange = 1.3

red + violet = violet-red = 5.9

This example shows that the large gap between violet and red make the value tend towards the dominant color but not quite make it to pure violet.

violet + blue = blue-violet = 5.8

blue + green = green-blue = 4.9

green + yellow = yellow-green = 3.8

And you can use your own system to figure out the remainder; these are my estimates.

green + orange = brown = 3.999

orange + violet = brick = 5.98

violet + green = slate = 5.7

slate + brick + yellow-green = 15.48 = B + C

After that exercise, I don’t think it would be too difficult for you to imagine that a programmed instruction could also be a color. As an example; ‘Add data element B to data element C and leave results in C.

As I previously thought-transmitted to you, someone in upstate, New York – – the individual’s name has been lost – – had proposed a computer that does not use a numbered base such as binary, octal or hexadecimal. It was a computer that ran on the logic of color.

The Lab Color Space was chosen for its fine granularity.

And also for its predefined ability to assign values to each color, its hue, its saturation, and its luminescence; no matter where on the Lab Color Space we choose.


The Lab Color Space was allowed to be broken into several partitions; e.g.,  1, 2, 3, 4, etc.

Any section could be used for mapping knowledge bases, micro instructions, programming instructions and floating-point instructions. Therefore, any section could be arbitrarily sub-divided into four other sections.


This division could go on infinitesimally according to the user’s needs.

The cyborg designer then arbitrarily assigned separate categories of instructions to different sub-divisions, examples; 1a = area for mapping knowledge bases, 1b = area for micro instructions, 1c = area for system programming instructions and 1d = area for floating point instructions.

Note the fine color granularity in each of the subsections in quadrant 1. This allowed each hue to be mapped to not only a specific type of instruction but for an individual instruction.

Example; Let us assume that we wished to add two floating point numbers together. The instructions and data would be accessed. There would be an associated color that has been identified when this instruction was link-edit-resolved for future access. This link edited color would automatically pass control to the “knowledge base map” which in turn would pass control, via the solar-cloud, to a knowledge base that had stored all previously calculated combinations of floating point instructions. That would allow the computer to know whether this specific operation and numerical values had previously been calculated. If so, the answer would be automatically retrieved. This would save calculation time. If not, the calculation would be accomplished and then stored on the solar-cloud data base for the next query. This would be a species of Object Oriented Programming with the results of the calculation stored for the next user. In other words, all cyborgs that wished to do a specific calculation would immediately know if it has been previously computed or not. If yes, then the result would be automatically retrieved thus avoiding a potentially time-consuming calculation. If no, then the calculation would be completed and stored universally for the next instance that ANY cyborg wished to use the results. Thus, it would become another data element holding pre-calculated data.

This predetermined that there were knowledge bases of all types; logarithmic tables, trigonometric tables, previously calculated tables, archaeological data bases, literary data bases, optical character conversion, facial recognition data bases, etc. Over time, all epistemological elements could be efficiently stored and quickly retrieved.

I am sure that you earth-humans are now asking ‘How about color reception and Management?’

The postmodern earth-humans replaced the Arithmetical Logic Unit (ALU) of the digital computer with a logic device that recognizes color as well as grey scale; they called this the Color Logic Unit (CLU). This required a “front end” system of rods and cones to simulate the construction of the eye.

From POPULAR SCIENCE; Article by Earth-human Emily Elert February 14, 2013

Technology Section

World’s First Bionic Eye Receives FDA Approval

The new retinal prosthesis, called Argus II, can restore partial sight to people blinded by a degenerative eye disease.

This morning, I (Emily Elert, article writer) was speaking with Brian Mech, the vice-president of the medical device company Second Sight, when his land-line rang. Mech had just been telling me about the fifteen years his company has spent developing the Argus II, a retinal prosthesis that restores partial sight to people with a degenerative eye disease called Retinitis pigmentosa (RP). It had been a long process, Mech said, but he can count on one hand the number of days he hasn’t woken up excited about the work ahead. And they were nearing the end–Europe approved the Argus II in 2011, and the FDA was expected to give the green light sometime soon.

The “Argus II” had been FDA approved. Therefore, it and other follow-on systems were used to solve the problem of optical recognition. The Argus II worked by substituting a small array of electrodes for the light-sensing cells that normally react to light by sending an electric signal toward the back of the retina. Those signals are relayed to the optic nerve behind the eye, and travel back along the nerve to the brain.

This thought transmission described the front end of the CLU. The back end must make sense of these signals. For the back end of the CLU the postmodern earth-humans needed a comparator unit to simulate the brain receiving optical messages.

For purposes of reliability they decided that three comparator units must be used in parallel and that a voting system selects the correct results. For example, if comparator #1 and #2 agree but the results of #3 are different, then the results of #3 are disregarded. Likewise, for any two comparators that agree. The odd result is always disregarded.

The following paragraph defines the basics of color comparison; spectrum management and refinement. This was called Multicolor Cavity Soliton. The first discovery had been recorded in PubMed, US National Library of Medicine, National Institutes of Health, July 25, 2016 by Luo R, Liang H and Lin Q.

The postmodern earth-humans discovered a new class of complex solitary wave that exists in a nonlinear optical cavity with appropriate dispersion characteristics.  The cavity soliton consists of multiple soliton-like spectro-temporal components that exhibit distinctive colors but coincide in time and share a common phase, formed together via strong inter-soliton four-wave mixing and Cherenkov radiation. The multicolor cavity soliton shows intriguing spectral locking characteristics and remarkable capability of spectrum management to tailor soliton frequencies, which were very useful for versatile generation and manipulation of multi-octave spanning phase-locked Kerr frequency combs, with great potential for applications in frequency metrology, optical frequency synthesis, and spectroscopy.

An earlier article had been published by INSPIRE, the High Energy Physics information system catalog. This article was called Multi-color Cavity Metrology and was written by Kiwamu Izumi, Koji Arai, Bryan Barr, Joseph Betzwieser, Aidan Brooks, Katrin Dahl, Suresh Doravari, Jennifer C. Driggers, W. Zach Korth, Haixing Miao, Jameson Rollins, Stephen Vass, David Yeaton-Massey, Rana Adhikari in May, year of your Lord, 2012.

Long baseline laser interferometers which had been used for wave detection. These proved to be very complicated and hard to control. To maintain sufficient sensitivity to astrophysical waves, a set of multiple coupled optical cavities comprising the interferometer had to be brought into resonance with the laser field. A set of multi-input, multi-output servos then locked these cavities into place via feedback control. This procedure, known as lock acquisition, had previously proven to be a vexing problem and had reduced greatly the reliability and duty factor of the past generation of laser interferometers. In this article, the above scientists describe a technique for bringing the interferometer from an uncontrolled state into resonance by using harmonically related external fields to provide a deterministic hierarchical control. This technique reduced the effect of the external disturbances by four orders of magnitude and promised to greatly enhance the stability and reliability of the current generation wave detector. The possibility for using multi-color techniques to overcome current quantum and thermal noise limits was also discussed.

We now have a front-end color recognition system and a method for determining and correcting color reliability. This correction method is much like the correction algorithms that discover and correct ‘parity’ or ‘cyclic redundancy check’ errors’ in the older digital computer systems.

Selecting a Color Space

Each color quadrant is defined by its intention (instruction type or data) we only need to focus on the finely granulated colors that we so choose from our Lab Color Space.

Three “guns” select the finely granulated colors that the user chooses. The guns are lasers which are ‘aimed’ by a plasma based optical system. This avoids mechanical movement. The lasers can then be pointed at any minute point on the Lab Color Space to select any instruction and its required corresponding data. Laser #1 selects the instruction, laser #2 selects the first data operand (if data is needed for this instruction) and likewise laser #3 selects the second data operand if data is required. This eliminates the need for registers to be loaded prior to the instruction being executed; all is done in parallel.


The three receiver/decoders described as the Color Reception and Management system, ibid, are also matched by a plasma optical system that receives the colors (instructions and data) that were projected by the laser guns.

The following diagram shows cantilever dynamics and the optical detector through AFM split photodiode detector handling macro parallax conditions.[5]

Remaining parallax distortion caused by the parallax between the laser and the Lab Color Space will be controlled by the Multicolor cavity soliton and Multi-color Cavity Metrology methods as thought-transmitted earlier.

This system was originally developed to assist in mapping thought processes. The postmodern earth-humans had no ability to map a multitude of neuron firings in parallel. The ability to follow eye movements coordinated with single neuron firings could be mapped and therefore yielded superior results. Eye movements would control the laser pointer(s) and the computer would operate on instructions and data.

Consider the example of the ophthalmologist’s “Field of Vision” test where many points of the retina/optic nerve can be tested to find blind spots in the patient’s vision.

The postmodern earth-humans mapped what the brain was doing as an earth-human scanned a piece of artwork. They also mapped the eye movements of an architect to determine what he found worth spending time on as opposed to what he determined to be unusefull. This type of mapping also proved invaluable in medical, psychological and psychiatric diagnosis.

 There existed an infinite number of diagnostic applications from mechanical diagnosis to aptitude diagnosis.

The first Rainbow Computer utilized flat planes. The postmodern earth-humans soon realized that CLU (color logical unit) had the speed to complete several analyses when the entire unit utilized spherical surfaces.

This additional speed was added to the Rainbow Computer by using a multiple “eye” laser system that would algorithmically choose the next set(s) of data or instructions that “may” be required. In other words, this was a ‘look-ahead’ system that would prepare the next instruction and data while the current instruction was being executed.

This unit used a spherical color space configuration that would enclose multiple Argus II systems.

[1] 16 February 2015     HORIZON; The EU Research and Innovation Magazine

[2] Professor Martyn Amos, February 2015, Manchester Met. Univ., UK


[3] Albert Einstein (14 March 1879 – 18 April 1955) was a German-born theoretical physicist. Einstein developed the theory of relativity, one of the two pillars of modern physics (alongside quantum mechanics). Einstein’s work is also known for its influence on the philosophy of science. Einstein is best known by the general public for his mass–energy equivalence formula E = mc2; which has been dubbed “the world’s most famous equation.”

[4] Sir Isaac Newton (25 December 1642 – 20 March 1726) was an English mathematician, astronomer, and physicist  who is widely recognized as one of the most influential scientists of all time and a key figure in the scientific revolution. His book ‘Mathematical Principles of Natural Philosophy’, was first published in 1687.

[5] Date     10 May 2009  Source   Own work   Author   Tertib64