Origin Of Intelligent Life

Last Updated Feb 17, 2009

Emergence Of Intelligence

Represented in the illustration (man/woman) is our multicellular intelligence which is emergent from cellular intelligence which is emergent from molecular behavior which is emergent from nonrandom atomic behavior which is emergent from nonrandom subatomic behavior of quarks and leptons that are emergent from forces that science is still explaining. From matter/energy itself comes increasingly complex behavior that molecularly self-assembles into learned and instinctual memory based intelligence that inherently responds to environment by learning to control it for its own needs.

At a level of organization above multicellular intelligence is at least one emergent level of behavior above this where for example the behavior of fish that instinctually stay together form a new collective entity the "school" of fish that moves as one in response to environment. With greater communication skills humans achieved an electronic global collective intelligence where all may move as one in response to weather forecasting stimuli warning of approaching hurricane danger seen through the electronic eyes of satellites.

Our cells have instinctual behavior and use electrochemical communication to control what it can and from this they work together for mutual benefit to produce the behavior of the human entity that they sum to. Each cell only has to do what it wants to do, for this multicellular intelligence to grow and in time reproduce more. Then from a single cell divides another. At first the cell colony is small and simple like the multicellular social amoeba. Each cell is mobile. Over time cells become increasingly specialized. They find their place in the developing colony then instinctually connect together to form organs such as muscle. Chemical signals from around each cell in part guide it towards a useful specialization, and in part it is as a fish out of water will struggle to get back in cells that find themselves in the wrong place will struggle to get somewhere in the colony they can survive. Establishing a place in the growing colony requires being useful, what is useless is cast out so divides no further. Self-organization results in the needs of all cells that remain being met.

What is coded in the genome will influnce what it can morphologically turn into. The behavior of the cell itself will influence the self-organization into a functional design.

Features Of Intelligent Life

Living things (life as we know it) so profoundly change the usual features of the universe that we can tell it is present from outside its solar system by there being an atmospheric Oxygen concentration dangerously close to a massive planetary explosion.[16] The culprit of firestorms and other cataclysm is on at least this planet detectable by strong reflection back into space of the green portion of the light spectrum and absorption of light at 430 (blue) and 662 nanometers (orange) that powers the Krebs Cycle which consumes CO2 gas giving off flammable to explosive O2 gas (Oxygen atoms pair up, a diatomic molecule).

Where there is intelligence at work the blissful world of fully reacted molecules where nothing changes becomes a dangerous chemical chaos. At all levels one intelligence mercilessly consumes another, as long as they are not like pets or live in symbiosis with them in which case are spared being eaten. Even the radio waves from intelligence with technology to control electromotive force producing TV and Radio communication is noise in the usual background sound of stars. You know such a signal came from intelligence when you hear its music, see them dancing and know when it went to a commercial.

Intelligence is very good at detecting another intelligence. Water motion might be causing a storm of nonliving particles to be moving by but something living can go swimming the opposite direction to defy all else following strict natural rules that the living thing can in part control. So when looking for intelligence we look for what is abnormal for a rock or star to do.

Control Of Krebs Cycle By Molecular Intelligence

In living things molecular intelligence is seen controlling what self-assembles from the powerful Krebs Cycle that has become the core metabolic cycle of cells. It is the power plant and factory where a dozen or so catalytic molecules (protein, mineral or other) are drawn to metabolic pathway assembly lines that makes a copy of the molecule it started with every time around the circle. It does this by adding a non-chiral (structurally identical) mirror image of the starting molecule then when the cycle is completed it breaks in half resulting in two identical copies.

At any stage through the assembly cycle a molecule of proper fit may be drawn by molecular forces into a nearby self-assembly interaction to where it fits. At least part of the Reverse Krebs Cycle is catalyzed by volcanic clay/dust/mineral in sunlight making it possible that the cycle was once common planetary chemistry.[11][12]

Where there is no molecular intelligence present the Krebs Cycle would not be able to produce cells and exist regardless of molecular intelligence being present or not to control it. A rudimentary intelligence may actually be challenged to keep up with its production rate but not necessarily be destroyed by periods of overproduction.

Intelligence to exploit this cycle could easily form in its local environment. Once active it would have little problem controlling this existing metabolism. We can here predict self-assembly of a precellular starter mechanism that produces a genome from scratch, instead of a genome first being required to produce this intelligence.

Self-Assembly and Self-Disassembly

Self-Assembly describes processes where a disordered system of pre-existing components arrange to form an organized structure.

Self-Disassembly is when the ordered arrangement is dismantled, the disordered state of a self-assembled system.

The word "chaos" as in "chaos theory" (not complete disorder) would describe self-assembly and self-disassembly constantly taking place at the same time. Disassembly adds disorder. Assembly adds order. Therefore an ordered system may exist in what first appears to be completely random behavior.

In the very beginning before there was life, molecular forces (bonding, polar) were already self-assembling large quantities of simple cell membranes (vesicles)[1] and numerous crystal designs that like snowflakes self-assemble when environmental conditions are favorable includes DNA, RNA, to increasingly complex designs where tubulin protein based crystal designs include ATP synthase and flagellum motors. All of these are made of molecules that like magnets attract to each other then connect together only one way, resulting in a reproducible design. All that is needed for assembly is that the molecular parts be produced in close proximity to each other and favorable environment for formation, self-assembly.

With the cellular production of the easily crystallized tubulin protein, threadlike microtubules are nucleated (seeds formation of growing from there crystal) in a given direction from the centromere (brain-like structure inside animal cells) that contains two relatively large self-assembled crystals at 90 degree angle to each other. In this case self-assembly and self-disassembly together allows a centromere to "probe" around the inside of the cell by forming then reforming microtubules in a slightly different direction so it can scan out an area inside the cell.

A cell moves by motor proteins traveling the length of the microtubules delivering what self-disassembles at the back end to where microtubules are radiated where it self-assembles back together. In this way an animal cell can move its insides from one place to another to produce the movement seen in migrating stem-cells and amoeba.

Being able to push away from something with a microtubule crystal keeps the nucleus of a cell well centered with radial (sent in all directions) gentle pushes off the membrane.

Hydrocarbon Chains

Vital to the structure of living things is the way carbon atoms tend to bond together to form a "backbone" or "skeleton" to produce hydrocarbon chains (also rings) as here illustrated by a selected number of the fully hydrogen saturated "alkanes".

Boiling point increases with lengthening of the carbon chain. Unless kept under pressure Methane to Butane quickly boils at room temperature to form a gas. The next longest chains are liquid at room temperature and are used as fast drying solvents for example in dry cleaning of clothes. Then comes oils, greases, waxes, and hard solids such as asphalt used to make roads. Hydrocarbon chains may be hundreds of carbon atoms in length even bonding together to become an almost endless.

Chains with more than three Carbon atoms can form many different structural isomers to make rings, cubes, triangles or branch off. These "isomers" have similar (but not exact for example branched design more easily burned) chemical properties as the straight-chain "n" forms shown.

An abundant source of hydrocarbons is crude oil where decomposed organic matter provides carbon and hydrogen under conditions favorable for their formation. The same hydrocarbons are also found in living things. Methane (swamp) gas is a digestion product. Others (shown below) are used in insect attraction or defense and form waxy coatings on some plants or by bees to make beeswax. The "tails" of membrane forming lipids (phospholipids) are hydrocarbon chains which accounts for their being hydrophobic (repelled by water while attracted to grease/oil) on their tail end.

Hydrocarbons are relatively inert (do not change in form over time) but is easily ignited to "burn" in atmospheric oxygen. This replaces the carbon-carbon bonds holding the hydrocarbon together with oxygen-carbon bonds to produce CO carbon monoxide and CO2 carbon dioxide with the now free hydrogen atoms also being mopped up by oxygen to produce H2O water molecules.

Hexagonal Close Packing Of Spheres

A very reoccurring design is the result of being how something that is round fits together to use least space. In the bubbles floating on the surface of the water shown here the spherical membranes attract to each other to become one, resulting in straight line honeycomb design.

YouTube Video

When covering a surface that is not flat (like eye of a developing fly) there are patches of this regularity that fit together well enough that there are no holes in the membrane. Stress of misalignment produces force that squeezes out or pops what does not have a space left for it.

During development of the eye of a fly somewhat mobile stem-cells snuggle together on both sides of the head with ones that could no find a space are likewise squeezed out. Stem cells below these developing light receptor stem cells adapt to where they ended up by differentiating into neurons that instinctively interface to such a sensor. The result is a surface of photosensors backed by a neuron/synapse network that will self-organize with each other to produce the sense of vision for the neuron levels that control the muscles.

In some developmental environments neurons form hexagonal columns that instinctively respond to sensed or stored angles. A plausible Ring Memory circuit to make use of this structure will later be modeled.

The Prebiotic Ocean Aquarium Model

It is helpful to know what we might see (by modern scientific dating) 4.4 to 4 billion years ago while taking a walk along the shore of the planets first ocean. We could predict there would be the CO2 atmosphere typical of a lifeless planet with high concentration of volcanic gasses. Sunlight would be dimmed by clouds, dust and smoke to a reddish color like seen at sunset where light travels a long distance sideways through lesser amount to gain the filtered color. A heavy asteroid bombardment 3.9 billion years ago may have vaporized the ocean which soon later reformed. Sunlight returned to grow stronger then 3.8 billion years ago there were simple cells called prokaryotes (lack cell nucleus) possibly first powered by simple Reverse Krebs Cycle photosynthesis possibly in part photocatalyzed by common mineral in dust and clay deposits.

The next best thing to being there is to model the prebiotic environment in an aquarium for molecules that were likely in the ocean at that time. Plan it to be 1/4 to at most 1/2 full. To make good use of area and see sedimentation one corner is deep ocean while other shoreline area made by placing bricks, flat stones or gravel along two sides. Used below is redstone (also called brownstone a type of slate) that has depressions made by small dinosaurs to form the coves where molecules collect. Any material with similar contour and contrasting color can be used.

Bubbles from an airstone provides the churning water turbulence to produce sea foam along with currents and some wave action. Large storms and hurricanes are simulated by periodically stirring without creating a tidal wave that wipes the beach clean of what was gathering there. Although optional, airstone on end of rigid tube is also a convenient stirrer. Make of length and weight (like glass pipette or stainless tube) where airstone stays down in corner on its own when hose on other end is rest on top of opposite corner. This additional bubble churning would be present especially in surface storm waves that carry them down deep with the smallest bubbles brought down deeper by subsurface currents that can give them days of contact time. Organic molecules in the chemical environment are attracted to then collect on the gas/liquid interface with that load resurfacing with the bubble along with what else others deliver.

The large scale dynamics of a low-pressure area is a phenomena where Coriolis forces are significant. In the Northern Hemisphere the direction of movement around a low-pressure area is counterclockwise. This does not influence a small in proportion to the Earth volume of water as in a sink or aquarium or toilet where water is here sent at an angle determining spin. Which direction the model is stirred is not predicted to influence outcome but in what this is modeling Coriolis forces produce a direction to account for that is determined by which hemisphere you live in.

To the water we add organic molecules. Membrane forming amphiphiles as were found to be produced by hydrothermal vents found in soap and egg yolk. A box of Jell-O gelatine and rest of the egg (albumen protein) to add structural proteins that would have formed where there are amino acid molecules present forming peptide bonds. A small pinch of meat tenderizer (protein enzyme) is a protein catalyst with an active site that cuts other proteins into shorter pieces. This is why when pineapple or other substance containing a protein that can do this is added Jell-O will not gel. Add to decrease size of protein strands. Or do not add so that in some chemical environments proteins will tend to coagulate to form a more solid plasma.

After some hours of time the airstone can be turned off then a spot cleaned on the side of the glass to look down the model beach to see what the "protein skimming" removed from the water that is now washed up on the shore. This is a photograph of strawberry-kiwi (pink) flavor Jello with small amount of egg white (albumen protein).

Proteins separate out slightly differently, as seen from above in a cove.

On the left below you can see the side of the aquarium glass. Dullness is from collecting airborne particles given off by the airstone bubbler "spray".

The onion has a genome larger than ours and is rich in RNA and DNA. The odor is strong, as would be bacteria that quickly multiplies where peas or other source is used. Use kitchen blender on high a good 15 minutes then strain. Coffee filter step may be added. To wash through more cell parts fill filter with water several times while stirring.

A good teaspoon of dirty motor oil from a four cycle Briggs & Stratton 5hp roto-tiller motor is here added to show some of its properties in this environment. In present day oil in this concentration is toxic but life required what it can in time be broken down into.

Below, plasma washed from shore is trapped in oil inside compartments (water in oil vesicle) that stay together in the center of small oil slick. The center is similar in form to a cell nucleus but in two dimensions instead of usual 3D compartmentalized sphere. The thin slick around it shows visible a surface membrane not visible with other membranes that would be present like this but not visible.

Turning on airstone distributes smaller sizes.

What is likely an entangled mat of onion DNA is spinning counterclockwise. Oil is here being attracted to but does not spread into.

As was demonstrated, forces present in the ocean separate and concentrate organic molecules to form plasma. This can still be seen in present day after ocean storms bring large quantities of sea foam to shore. Even though organic molecules are well dispersed in an ocean sized volume of water skimming brings them together where tides would then provide regular wetting and drying cycles to help induce peptide bonds.

Membrane forming molecules are predicted to self-assemble into a protective layer on a mass of plasma as it will do to a cell size amount. This should help prevent dehydration between tidal cycles but has not been confirmed by experiment. There are no known science papers pertaining to ocean models that study tidal areas this way. Any additional information may be useful to science. Classroom projects can here take this experiment a step further to compare with and without amphiphiles (egg yolk) to test a research question such as "Will adding amphiphiles help a mass of tidal plasma stay intact?" either True or False. Send results to GaryGaulin@aol.com to for real add your original work to what science knows about the origin of life.

To verify this experiment works in a early Earth CO2 environment half a trash bag of auto exhaust (handy source of primordial atmosphere) can be circulated inside tank. A piece of glass or plastic works as a lid. With CO2 being heavier than air it tends to stay inside like it were liquid. Can verify it is still present (apparatus works) with small flame lowered in that will go out when it reaches the CO2 layer.

Hydrothermal vent chemistry research shows heat and pressure forms important starter molecules. But that does not conclude that life had to form right there. Or in any other place useful molecules were produced. These molecules drift to other places. Directional currents allow part of cell construction to be accomplished in one location then more at another downstream to at an end produce self-replicating cells.

To help search for precursors of cellular life we can experiment with the self-assembly of cellular organelles. Together, organelles accomplish cellular division. The large genome they together replicate helps maintain their comfortable environment, a way to get around in other environments. A DNA based genome is useless without the organelles that produce them. Therefore within range of microscopes we can begin to examine this plasma for formation of membrane bound organelles found in present cells. Information in this area gained from ocean models like this is also valuable, please send results.

Important organelles to look for and their function are explained in this from Wikipedia:


We cannot yet conclude exactly how life began, but we can conclude that in a very chemically active ocean chemistry the beaches may have been less than pristine. Where that happens today it is quickly consumed by plants and bacteria to from there go further up the food chain to become the crabs and seaweed we now see on the beaches. But long ago before cellular life began this would have instead remained unconsumed. Once it was, the cells began to consume each other.

In origins science there is no one single theory or hypothesis that might win above all others they are together useful. There are some that directly compete to be a plausible "world" including "Protein World" or "Metabolism World" with "RNA World" doing very well yet might not be able to exist on its own without metabolism to produce the RNA nucleotides and proteins that metabolism helps produce that would help maintain fluid chemistry. At some point photosynthesis began which makes research in that area useful no matter whether the first cell used it or was later aquired. From abundant molecular organelles being first established cells could have quickly taken hold inside sheets of mica and other environments, where here there would not be one single cell all others came from yet they would share organelles and whatever genome they might have.


Prochlorons are very primitive prokaryotes with many photosynthetic membranes on top of each other and genome inside. It is here hard to tell whether they are a cell or an organelle. The chloroplast organelle is steps up in membrane efficiency and likewise genome replicated.


Photoelectric membranes might by electrical force cause movement. Interesting water-soluble pigments are found in red-cabbage, apple skin, blueberries, cherries, raspberries, plums, poppies, cornflowers, flowers, grapes, and more. Green plants may have chloroplasts and chlorophyll to experiment with.

Red Cabbage has an interesting pigment that also acts as a pH indicator. Clear solution isolated by covering with water then heating until color stops getting darker. May be used to tint aquarium water. Or note color change when a drop of indicator is released right above surface. Can be used as blender water for other cell types like onion to show healthy starting pH of their cells. Color range of red cabbage pigment and more detail is here:


To better detect what is trapped inside: First form starting vesicles in red cabbage either clear or to trap organelles strained from blender. Their good health can in part be judged by color remaining same as when strained. We have a narrow range centered around 7.4 but other kinds of cells vary in pH. When blending raw what is strained would be it's starting ay be health color. From there it will decompose into another color, limits for what is being experimented with. Possibly same color after straining, should remain in center. Where acidic bacteria may have digested the organelles, but that is open to future experimentation.

The aquarium challenge is to hold the food coloring in the center as long as possible. Try to get membrane over membrane much the way candles are made by dipping in molten wax after cooling. Maybe drying out on shore will help it gain another membrane. Anyone know? It's like

When water becomes fouled, without disturbing the shoreline drain with siphon. Add new water possibly with another kind of membrane forming substances to see if it then builds on the membrane to form multilayer protoprochlorons. One membrane may not be efficient, but many on top of each other greatly increases how many light photons each one can collect.

A glass or plastic top can be added. This will seal in odors and added gasses. Help keep out bacteria.

Broken plastic edging around top was removed with blade to cut through remaining glue holding it on. Heat was applied to the very edge of plastic on very top (but not glass!) to lift sides off without stressing glass then only have to do very top edge. Before gluing round off edges with clean sandpaper backed by something heavy like piece of wood board to not have to put hand over glass, first going against the edge not with it so it is not dangerously sharp. Take outside or where glass dust will go out like emptied large chimney fireplace open to outside air as a small volume dust or fume-hood. Sand and clay dust used to make ceramics are sharp crystals likewise an inhalating hazard A worklight placed up out of the way almost inside chimeny roughly in center will increase airflow helpful when relatively warm outside in comparison to inside. Sand very top of glass all around very top about 1/4 inch (up to 1 cm) inside and outside so sealant will stick all around to replace the edging that was removed. Not all of the glass needs to be scratched up for it to adhere so there should be little dust to begin with. This provides small scratches the sealant can anchor into in enough places it will stay adhered. Where there is already sealant stuck on good just clean then glue to that. When done sanding, fold glass dust up in paper then thrown away without breathing any dust!

With waterproof tray when needed an unused fireplace makes a sake chemistry workspace especially when heat is used. They tend to drip water when stormy and dirty so cover anything to keep clean when not in use.

Aquarium can be sealed with a glass or plastic top. Silicon sealant is then molded around to make new edging as thick as glass on sides and a little less on top (around 1/8 inch above where glass ends). When that has cured to rubber stage (few hours or overnight) a final bead of sealant is applied to very top then waxed flat surface like clear plastic or glass 1/4 inch or more larger than aguarium gently placed on that. Do not twist cover where not centered perfectly, just place to get a bead at least as tall as glass widthwise then do not touch for at least an hour or more! Needs air all around the side of the bead to cure in short as possible time. After that has cured more can be squirted into the remaining void under cover all around to take care of where it was not wide enough to make strong seal

Never put an aquarium pump inside the aquarium. It will quickly become wet inside then be a hard to notice shock hazard. For safe circulation of either air or water a $15 "Aqua Lifter AW-20 by TOM" is here used.

Pyrex glass pipettes were used as feed through tubes. Can use any good strong glass tube even just plastic tube where there is none that will work. Metal not recommended but stainless will work. In a plastic cover holes can be drilled in top but through the side easier to work with. Notch taken from top of aquarium glass was first scratched from outside with glass cutter, then snapped off along that line pulling inward along back of that line to stretch the crack open until it snaps off.

After putting everything inside aquarium can be sealed all around with plastic carton sealing tape so it will have a second barrier through that. Can then take outside to charge with auto exhaust atmosphere. Worse case only that small volume of mostly CO2 will be released.

Glove with long sleeve can be attached through hole in top of cover to move things around inside and this will also act as expansion space that must be added to maintain zero pressure. Otherwise use beach ball or other ballon to relieve pressure in case gas is generated or consumed pulling a vacuum. Do not overdo connecting all hozes so something will pop off when pressure builds with fermentation trap or U-Tube to vent to relieve excess pressure directly out to air.

Although not a problem for simple experiments in nonflammable CO2 exhaust (not with gasoline fumed from being flooded) or for keeping in foul smell, in a larger experiment accumulation of possibly explosive gases must be considered whenever mixing chemicals. Beyond lab safety instruction here included you have to be responsible for the safety standards in your own lab. Work safe!

Intelligence Generation and Detection

To demonstrate simple intelligence and its mechanism the following computer model demonstrates self-organizing, self-learning intelligence. On startup this guess/memory intelligence is like a newborn. It does not know up from down or left from right or what it’s seeing, experiencing. But from trial and error quickly learns how to coordinate motors with sensory information to get where it wants to go. It also "protects itself" by learning strategies to prevent bashing into walls. Adding another bad experience to the environment would likewise result in it learning strategies to help protect itself from that danger. If it finds biting with the mouth made to feed makes danger go away then it will become a biter.

The model has a simple ring circuit that adds a sense of awareness of what’s around itself. It is not necessary to include this for it to be intelligent, but the circuit easily adds a great deal of awareness.

Source code (but not compiled .EXE program that Visual Basic programmers do not need to run) is available on Planet Source Code here:

If you do not have a Visual Basic compiler then here is a zip file with the IntelligenceGenerator5.EXE file.

Download the zip file to your hard drive in a new folder with a name like IntelligenceGenerator5 then locate and open folder to unzip then run. There should not be a problem or do I expect one but if you worry about viruses getting into it then before running you can check to make sure this is the program I uploaded. Here are the "Properties" (right click on program icon) I have here of the IntelligenceGenerator5.EXE file to look for that should match what you received.

Modified: November 14, 2008, 8:24:42 AM
Size: 108 KB (110,592 bytes)

Intelligence Mechanism

Intelligence requires a Memory with mechanism that together controls at least a chemical cycle. In electronics an addressable array of changeable data switches are loaded with on or off information used by a program that performs machine intelligent tasks. In multicellular living things this memory system can be present in a brain where sensory cells address networks of neurons that wire to each other across synapse in a way that it can hold a memory of what was previously sensed by its sensory inputs.

Electronically, the Memory system it can be though of a RAM memory chip with wires each with a bit of input sensory information (eyes, ear, bumpers, etc.) are connected anywhere to the RAM Address Input bus. This gives each unique environmental situation a unique Memory location where a response can be stored.

The basic mechanism that produces the phenomena of intelligence can be modeled with a simple loop.[3] We will here give the intelligence control of tank-like 2 motor drive system. Motor Forward and Motor Reverse is controlled with two bits where motor is off when 00 or 11 while motor is moving one way or the other when 01 or 10 with it not mattering which order the control bits are connected to memory. Intelligence inherently self-organizes all inputs and outputs then “learns”.

In the first line of program code we have what the intelligence is to control and could be real motors. With molecules this could be the Krebs Cycle. The "Call" instruction causes top to bottom program flow to jump to where another routine generates a virtual environment containing the robot then jumps back when finished. Where real motors are used the four motor control bits are only sent to motor controller circuit, then returns.

The second line adjusts a Confidence level in response to the condition of the "Stall" environmental input sensor that is 1 (true) when wheels stop turning as it would when wall stops it. Other sensors such as eye pixel, battery low sensor and another for having found charger is added with another If..Then.. statement. Conf(Addr) is a one bit memory array location that stores Confidence level from 0 to 3 at address specified by the "Addr" variable. Due to the way electronic counters operate (but not synapse) the program assumes that Conf(Addr) will not go below zero or above limit, in this case three. The RunMotors subroutine would here change -1 to 0 and 4 to 3 so it stays in range.

The third line uses binary powers of two so that there is a unique Memory Address location for each possible input sensor combination. Networks of neurons already connect in a way that forms a unique branching paths so do not require a numerical address like this, but a computer memory here simulating them requires a number be given. Other inputs can be included in this addressing with the next power of two such as adding "+(EyePixel*32)" to include photosensor to see light from a battery charger. Memory size doubles for each bit added which is at first not a problem but can become unnecessarily complex. Not all sensory information need be included in addressing, just what is needed to make an efficient addressing system to sort visual experiences into unique locations in the memory. When there are a large number of inputs they are first summed in different layers of detail.

The fourth line takes a guess when confidence in an action is below one (zero) by randomly setting the four motor control bits then confidence level to one to indicate low certainty. This part of the mechanism is also intuitive when one tries to imagine what would happen where we could not take a "guess" when necessary. We would forever get stuck right there, maybe repeating the same unsuccessful action like bumping into barrier over and over again until dropping from exhaustion. Flies sometimes do this for a while against a pane of glass to reach a light source on the other side. At some point it has to realize that it is not having any success then try something else, or perish. Even a dumb guess can still be a correct response to environment. This happens in the computer model when stuck against the wall being able to go no further. It has to be able to take a guess how to get out of that fatal (when starves there) situation as would happen to a simple organism in a changing environment with a genome that has 100% replication accuracy which would never try anything new. Or where guesses result in too many useless responses. The ability to take a "good guess" then stay with it must be present for either a genome or intelligence of the computer model to adapt and survive.

1: Call RunMotors
2: If Stall=0 then Conf(Addr)=Conf(Addr)+1 Else Conf(Addr)=Conf(Addr)-1
3: Addr = LMF + (LMR*2) + (RMF*4) + (RMR*8) + (Stall*16)
4: If Conf(Addr)<1>

This model is analogous to finger muscle control that through training becomes coordinated in a way that they have the keyboard layout stored as motions to reach each key. In both cases intelligence successfully learns to navigate a 3D space without requiring a physical map. We are therefore able to type without consciously thinking about the level of intelligence that does the actual typing. There is in essence more than one intelligent mechanism at work in a brain. There are a number of them functioning at the same time.

We can sum up this mechanism by first needing something to control such as motors, muscles, inner cellular structure (stem cell migration) or the Krebs Cycle. Second there must be a way for success and failure of an action to be measured which can be from visual feedback to correct typing errors, molecular using chemical feedback, or in extreme cases not being able to endure the environment simply eliminates it. Third there must be a memory with a structure that saves actions in a unique location in memory for each combination of sensory input signals such as network addressing of a brain or genes located in a unique functional location in a chromosome in a unique chromosome territory inside the nucleus. Fourth there must be a way to take a guess in order to try a new action which at the genome level involves code changes where in somatic hypermutation (cells of the immune system) regions of the genome undergo recoding at some million times the normal rate to find a way to destroy an invader.


When you encounter a new problem you never saw before, you know when you have no solution in memory. Your confidence in having the correct response is 0, because you have no response at all for that yet. The best you can do is guess. If that didn’t work then you guess again. While growing up we had to try holding cups upside down and other angles to figure out that unless it stays "upright" the contents spill all over the floor. And coordinating muscle movement to walk then run involves a lot of falling down.

In the computer model all locations in memory likewise start off with Confidence = 0. Confidence is incremented up to a maximum of 3. When a guess leads to what it instinctually wants, it’s stored with Confidence = 1. If it works again, then it’s incremented to 2. Then finally to 3. The confidence range of 0-3 is all that’s needed. Going beyond that range is not necessary.

How confidence is incremented leads to various behaviors. All at once going from 0 to the max of 3 leads to overconfidence. If its confidence is easily brought back to 0 then it will have little confidence in any of its responses but that can lead to trying new things.

You can experiment with how the confidence is increased or decreased to obtain new personalities. In fact, there are so many ways to define behavior, there is no one right to structure the If…Then… instincts.


Instincts are greatly influenced by how the intelligence is wired together. In biology, a single egg cell divides into many. Some differentiate to form colonies of neurons. Cells that grow connections to others in the colony to form a brain that sends connections into the arms, legs, fins, whatever there is they can connect to.

Neurons start off with more connections than are needed. Then they fight it out. Use it or lose it. Connections that didn’t help you see or crawl, dissolved. In this way, the brain starts off in a condition where it can wire itself into the organism its inside.

Whatever is there for a photoreceptor, will work just fine. Even an eye-spot made of a centriole crystal is better than nothing. On up from there are telephoto eyes of birds of prey. Whatever there is developing for an eye gets wired into.

There is no one right way to form a brain. With all the different sponges and insects and worms and other designs, there are millions of ways to do it. Each results in a different set of instincts.

What an organism does when it hits a wall is instinctual. In this model instincts tell it bashing into a wall is not a good thing, by the Stall condition connecting to the random guess (in higher order is educated guess) circuit that forces it to try something else. We would bump our head, it hurts, the pain condition forcing us to try something different so that doesn’t happen again.

The human brain is much more complex than the one in the computer model. But the overall interaction is much the same. We have a memory that responds to what is being sensed, with action signals sent to muscles, where feedback circuits wire back success or failure and includes pain receptors to add a more automatic "don’t do that" reflex that more suddenly puts what muscles are doing into reverse.

To an organism that must attach to a surface, bumping into one is a good thing. It will instinctually want to stay there. Its body design (phenotype) will be made for attaching to a surface, presenting another challenge for neurons to coordinate.

How the neurons connect, is not ahead of time mapped out in DNA code (genotype) that only makes the neurons who instinctually form colonies of cells that instinctually like to communicate with each other. How that circuit ends up looking like, in part depends on what the neurons have to grow into. Out of their self-organizing conversation comes our human intelligence, or that of a snail.

Instincts are easily added using simple "If...Then..." statements that adjust Confidence level. The following is how the Intelligence Generator computer model accomplishes this:

'INSTINCTS are described by the following If..Then.. statements.

Conf = -1 'Decrement Confidence by default.
If Ful = 0 Then 'If Ful=0 then it's hungry.
If SpT = 1 Then Conf = 3 'Spinning towards food can't see.
If SeF(WantsF) = 1 And TwF(WantsF) = 1 Then Conf = 3 'Moved towards food
If SeF(WantsF) = 1 And Fwd = 1 And FwdWas = 1 Then Conf = 3
End If
If Fdn = 1 Then Conf = 1 'If now feeding.
If Stl = 1 Then Conf = -3 'Hit the wall, stalled.

Seeing food and hungry while motor direction is moving it closer, is here a successful response. But if it is hungry and what it's doing is not getting it any closer, it’s failing, so the intelligence must take a guess. Random motor settings are tried. If settings work then they remain in memory, else it takes another guess what to do.


Since the amount of memory required (size of RAM chip if electronic) doubles for every bit of sensory input added, its size can increase very rapidly. But memories such as these do not require details. It’s not necessary to connect every single pixel of eye information directly to memory. Like in human vision many photoreceptor pixels are combined into a single signal before being sent to the brain via optic nerves. The visual information is not all at once processed, it is processed in "layers" of simple circuits.

In this model is a second memory adds an awareness of what’s around it. This way it can sense what is out of its field of vision.

When you click the "Circuit" checkbox you see what is in essence a ring of six neurons, numbered from 0 to 5. The state of what it sees ahead at Angle 0 shifts from neuron to neuron around the ring. The reference angle for body position could come from the sun, magnetic field, nearby landmark, balance circuit, or other source of rotational feedback.

Direction of food is shown as blue pointer. In this example the food angle just switched from being to the left out of field of view, to being straight ahead. The right motor is running forward (green) while the left is stopped (gray). So it's here Spinning Left (SpL) and Spinning Towards (SpT) what it "sees" to the left of it.

Sensing direction like this adds another level of intuition. Will now learn how to turn in the right direction to follow what when out of its field of vision. Like when something goes fast in front of you, turning the right direction is automatic. You don't look left then right back and forth until you by chance see it again. It's possible to grab the last feeder with your mouse so it has to chase after it. Will notice that it learns to turn in the right direction towards it.

What this adds is shown below, where the chart with Angle and SpT bit added to the Address is shown above the graph of the same age without it. The Food Supply slider control was set to 1 (lowest amount) so it has to work hard to keep itself fed.

In the graph you can also see how well it keeps itself fed, the red line No intelligence at all would have the graph showing a battery level that flat-lines at 0, which in essence, is dead. But notice the red line here. See how it very quickly learns to find food so it doesn't go to zero full, starved, like it would be if stuck in a circle never bumping into food.

We can see from the lower red line of second graph that it had more trouble staying fed. Confidence is also noticeably lower due to it being a slower learner. Shown below, is the graph when its ability to see food is entirely taken away.

In the above three examples, the only thing that has changed is the amount of environmental sensory information. And this intelligence is not at all fussy. Adding any kind of sensor, that does almost anything, can greatly increase its awareness.


Where you did not know how a memory system works, there would be no way to understand the order that exists in the chaos (in this case of numbers on screen). But an intelligence self-organized inside. It is not complete disorder.

To see this intelligence in the chaos requires displaying the ordering of memory and what it contains that is not random for example where unused memory starts off randomly set (instead of cleared to all zeros) which works as well since it at most amounts to having a random guess already waiting for yet to be experienced memories.

In the computer model memory is displayed by clicking the "Show Main Mem" button. All nonzero memory locations are then listed in the text box below it.

Motor controls Right Motor Forward (RF) and Right Motor Reverse (RR) are in both Address and Data. The address is sensory feedback information, back from the motor circuit that lets it know it’s moving. It could be stuck against a wall, stalled, not turning.

There is no motor On/Off register. Motors are off whenever both RF and RR are 0 or 1, or both LF and LR are the same value.

The "Addr" is the unique address. All other memory locations are unused.

"An" is the angle relative to itself and food.
"Sf" indicates that is sees food in front of it.
"Tf" indicates if it moving Towards food.
"Sp" indicates it’s Spinning towards food (SpT).
"Fd" indicates it’s Feeding, or in other words found food and hungry.
"Fu" indicates it’s Full.
"St" indicates it is Stalled, against wall

Classes Of Robotic Self-Learning

It is useful to define intelligence as in robotics according to David L. Heiserman 1979 in regards to the self-learning autonomous robot, for convenience here called "Rodney".[4] The Intelligence Generator/Detector described above is Beta class.


While Alpha Rodney does exhibit some interesting behavioral characteristics, one really has to stretch the definition of intelligence to make it fit an Alpha-Class machine. The Intelligence is there, of course, but it operates on such a primitive level that little of significance comes from it. ....the essence of an Alpha-Class machine is its purely reflexive and, for the most part, random behavior. Alpha Rodney will behave much as a little one-cell creature that struggles to survive in its drop-of-water world. The machine will blunder around the room, working its way out of menacing tight spots, and hoping to stumble, quite accidentally, into the battery charger.

In summary, an Alpha-Class machine is highly adaptive to changes in its environment. It displays a rather flat and low learning curve, but there is virtually no change in the curve when the environment is altered.


A Beta-Class machine uses the Alpha-Class mechanisms, but extends them to include some memory - memory of responses that worked successfully in the past.

The main-memory system is something quite different from the program memory you have been using. The program memory is the storage place for Rodney’s basic operating programs-programs that are somewhat analogous to intuition or the subconscious in higher-level animals. The main memory is the seat of Rodney’s knowledge and, in the case of Bete-Class machines, this means knowledge that is grained only by direct experience with the environment. A Beta-Class machine still relies on Alpha-like random responses in the early going but after experiencing some life and problem solving, knowledge in the main memory becomes dominant over the more primitive Alpha-Class reflex actions.

A Beta-Class machine demonstrates a rising learning curve that eventually passes the scoring level of the best Alpha-Class machine. If the environment is static, the score eventually rises toward perfection. Change the environment, however, and a Beta-Class machine suffers for a while, the learning curve drops down to the chance level. However, the learning curve gradually rises toward perfection as the Beta-Class machine establishes a new pattern of behavior. Its adaptive process requires some time and experience to show itself, but the end result is a more efficient machine.


A Gamma-Class robot includes the reflex and memory features of the two lower-order machines, but it also has the ability to generalize whatever it learns through direct experience. Once a Gamma-Class robot meets and solves a particular problem, it not only remembers the solution, but generalizes that solution into a variety of similar situations not yet encountered. Such a robot need not encounter every possible situation before discovering what it is suppose to do; rather, it generalizes its first-hand responses, thereby making it possible to deal with the unexpected elements of its life more effectively.

A Gamma-Class machine is less upset by changes and recovers faster than the Beta-Class mechanism. This is due to its ability to anticipate changes.

Biological Intelligence

For there to be intelligence, there must first be predictable behavior for it to be emergent from. In living things atoms possess this predictability as long as random motion from heat entropy (or other destructive phenomena) does not overwhelm the nonrandom behavior. Without this ability to do the same thing every time is like a computer with a memory that continually changes, where here a document you are writing would become a screen of unreadable characters faster than you can correct the errors. Or like being being built with logic gates that randomly gate what must always be sent to the screen to the printer ports and other useless places. Intelligence can only emerge from predictable (nonrandom) behavior.

Very rudimentary intelligence can build and maintain cells much like we do together to build cities.[7] Molecular intelligence achieves the complexity of cells. Cellular intelligence achieves the complexity of multicellular organisms such as humans where their city-like environment includes heat generation and constant internal temperature regulation so we do not freeze solid in cold like insects and other cold blooded living things. On our level of human intelligence we build cities that can from outer space be seen giving off light that is not the spectrum of a firestorm, making it possible to tell that the intelligence invented electric light bulbs from their unique spectral signatures.

Molecular Intelligence

In living things molecular intelligence survives time and environment through continual replication of a genetic memory where output actions are stored as coded genes that catalyze production of proteins. Genes are stored in their respective chromosome, or in simple genomes form a circular loop called a plasmid. But without a mechanism that can make intelligent use of the genetic memory, all you have are a number of coded DNA crystals that like dust will blow away with the wind.

The complexity of a genome is achievable because of the way genes are replicated and on occasion change coding in a way that it becomes a new successful response to environment. Even where it is not helpful change it would not necessarily be fatal due to still having genes to perform the vital tasks. No fuction is lost this way. Responses are thus remembered in the form of new genes from previously coded ones. In time a large amount of adaptive knowledge is this way accumulated.

A cell is a symbiotic relationship between a number of organelles. There are mitochondria that have their own circular genome which possess their own rudimentary intelligence, but are still just one more organelle inside a cell. Cells often have more than one chromosome each working with the other but each are an independent molecular formation that function as an independent unit. They also take up territories like this computer model illustrates. During the replication stage of the cell cycle these chromosomes supercoil to the shape shown in ‘A’ of the below illustration which makes it possible for the copies to be separated from each other without tangling.

After replication the chromosomes resume production of messenger RNA’s which forms a structure similar to the computer graphic image shown in ‘B’ of the illustration. Each compartment works independently and with neighboring compartments. In this way there is an organization present that allows each compartment to specialize in a certain gene driven function. Genes can also this way be deactivated (turned off) by pushing them out of the area where messenger RNA is formed.

In molecular intelligence successful responses to environment remain in memory in the population (gene pool) to keep going the billions year old cycle of life that through continual reproduction of previous state of genetic memory with deterministic modification one step at a time builds upon a previous design. A cladogram of resultant lineage thus shows a treelike progression of adapting designs evidenced by the fossil record where never once was there not a predecessor of like design present for the descendant design to have come from.

Cellular Intelligence

On the next emergent level after molecular intelligence is cellular intelligence that can in part respond to environment through sensory molecules that address genetic switches (epigenetic) that change during the lifetime of the genome but the coding itself does not change. Vernalization stores seasonal climate information to know proper time to begin springtime regrowth or bloom. All together these mechanisms that help a cell survive without a change in the genome coding is here the more moment-to-moment cellular intelligence. There may be a light sensitive brain-like mechanism involving centrioles that would certainly represent cellular intelligence.[6] Observing microscopic single celled hay infusion protozoa show instinctive behavior inherent to design that may not in itself be memory driven intelligence.

Stem cells have a surprising ability to solve problems. Including the ability to turn into numerous kinds of cells depending on the needs of the environment they find themselves in.

Human Intelligence

In the next emergent level after cellular intelligence, is multicellular intelligence. Human intelligence is electrochemically produced by neurons that also control muscles and other processes. In addition to intelligence human intelligence also possesses consciousness. Although consciousness has been traced to a relatively small region deep inside the brain, how this awareness works is not yet known.

At our level we are consciously rewarded by "success" and feel punished by "failure". For that reason games and sports are very popular to achieve the euphoria that accompanies success. By being able to "feel for others" we can share in the success or failure of another intelligence simply by watching them. We therefore have heroes who succeed and villains who fail us. Academia uses a reward system by "degrees" which often prevents employment to those who did not "make the grade" even where there are self-learners who have more knowledge and experience from learning while growing up. Intelligence is here again controlling something for it's own benefit. In this case learning and knowledge itself, with no regard to whom or what is consumed this way.

We have such a need for knowledge many feel incomplete especially when it comes to the "big questions" like where we came from and in time will go. Scientists may try to answer that by searching for new knowledge scientifically. Others may seek similar knowledge from history or religion. This powerful need for knowledge is also why this theory exists.

Genetic Recombination

Without some form of genetic recombination offspring would all be perfect clones of the parents. Adding pseodorandom (predictable randomness) crossover exchange to replication will cause the intelligence to try new things thay may work a little better in the next generation. This learning rate may be millions of times faster and account for why for so long all that existed was simple single celled organisms. The genome mechanism would first have to learn how to take a "good guess" which requires an organized exchange as is seen in chromosome crossover.

Genetic recombination might be necessary to go beyond the complexity of bacteria. Its arrival would be followed by a sudden appearance of multicellular organisms as the fossil record evidenced happening in the Precambrian.

Determinism Towards First Humans

Selected genes are continually replicated while others are disabled, analogous to the computer model's intelligence one step at a time heading towards the feeder by setting a successful response it is confident in then staying with it. And when conditions change such as where the feeder was moved while heading towards it then there will be a response ready to try that will likely work right away. This helps explain how a land animal with legs could in a relatively short amount of time become a whale with flippers. Determinism can also be seen in a giraffe neck and related physiology now changing in the longer direction. Their offspring do not have random length necks and hearts that give out early.

New designs at the multicellular level are also in part guided by what the organism itself intelligently and consciously finds desirable in the variety available to select as a mate. Examples include the peacocks where females selecting the largest most attractive tail design, led to males with brilliant displays, even though this makes it more difficult to fly from predators. In humans the looks of "sex symbols" sometimes computer enhanced to represent the conscious ideals not yet common in our morphology.

Without intelligence driven mate selection species would not bond with their own kind. This would either produce no offspring at all or a possibly sterile hybrid (mix of both) which in either case would result in fewer species over time.

Occasionally, chromosome complexity increases when two entire chromosomes fuse at opposite ends to become one. This has made humans unique among their kind where such a fusion makes a total of 46 chromosomes, instead of the 48 of all great apes. Here, a parent passed to offspring a fused copy in one of the two parental gametes, to birth a being with 47 chromosomes. That fusion then passed into the population where the fusion would then on occasion have the fusion in both gametes to make the first 46 chromosome beings. From a man and woman both with 46 (fusion in both gametes) could only come 46 chromosome offspring, us.


[1] G. Gaulin, Demonstrating the Self-Assembly of the Cell Membrane, NSTA -The Science teacher, 10/1/2007
Prior version, open access:

[3] G. Gaulin, Intelligence Generator/Detector.
7 sensors (Stall, Full, Forward, See/Smell Food, Towards Food, Spin Towards, Angle) plus 4 motor bits as "feedback" so memory (brain) knows what the motors are doing.
If you do not have a Visual Basic compiler then this version is the same as Planet SourceCode but with additional comment in IntelligenceGenerator5.FRM file (the "source code") to help non-programmers and Windows runnable program. Properties (right click) of the IntelligenceGenerator5.EXE program that should match what you received are; Modified: November 14, 2008, 8:24:42 AM Size: 108 KB (110,592 bytes)

[4] Intelligence Generator computer model was adapted from the book (robot made virtual): Heiserman, D. L., How to Build Your Own Self-Programming Robot, Blue Ridge Summit, PA, TAB Books, Inc., 1979

[6] Guenter Albrecht-Buehler, Robert Laughlin Rea, Cell Intelligence (webpages)

[7] Harvard, Inner Life, animation.
Also higher resolutions and videos:

[8] Molecular Nanobiointelligence Computers, National Cancer Center, June 21, 2005, Byoung-Tak Zhang, Center for Bioinformation Technology (CBIT) & Biointelligence Laboratory, School of Computer Science and Engineering, Seoul National University

[9] Synthesizing cellular intelligence and artificial intelligence for bioprocesses, P.R. Patnaik, Institute of Microbial Technology, Sector 39-A, Chandigarh-160 036, India

[10] G. Kreth, J. Finsterle, J. von Hase, M. Cremer and C. Cremer
Radial Arrangement of Chromosome Territories in Human Cell Nuclei: A Computer Model Approach Based on Gene Density Indicates a Probabilistic Global Positioning Code
Biophys. J. 2004 86: 2803-2812

[11] X.V. Zhang, S.P. Ellery, C.M. Friend, H.D. Holland, F.M. Michel, M.A.A. Schoonen, and S.T. Martin, "Photodriven Reduction and Oxidation Reactions on Colloidal Semiconductor Particles: Implications for Prebiotic Synthesis," Journal of Photochemistry and Photobiology A: Chemistry, 2006, 185, 301-311.

[12] FROM: Driving Parts of Krebs Cycle in Reverse through Mineral Photochemistry
Xiang V. Zhang and, Scot T. Martin
Journal of the American Chemical Society 2006 128 (50), 16032-16033

[13] Clays May Have Aided Formation of Primordial Cells

[14] Decision-Making Circuitry of Blood Stem Cells Mapped

[15] Kyte J, Doolittle RF, Hydropathy index, Wikipedia, May 1982

[16] JH Koeslag, What is Life, Physiology website