Ali, here is the latest version of the theory I'm working on. Maybe I should send it to him? I could sure use the money!
Theory of Intelligent Design (Revision 4.3 - preliminary)
Gary S. Gaulin, 2008
The theory of intelligent design holds that certain features of the universe and of living things are best explained by an intelligent cause[1] where multicellular intelligence is emergent from cellular intelligence which is emergent from molecular intelligence which is emergent from nonrandom atomic behavior which is emergent from nonrandom subatomic behavior which is emergent from a source currently unknown to science that must always be present for living things to exist. From energy itself, comes increasingly complex behavior that molecularly self-assemble into learned and instinctual memory based intelligence that responds to environment by attempting to control it for its own needs that vary with design.
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. At any stage through the assembly cycle a molecule of proper fit may be drawn to where it belongs by molecular forces into a nearby self-assembly reaction. At least part of the 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 producing cells. But intelligence could be forming in the existing cycle available for molecular intelligence to exploit. We can here predict self-assembly of a precellular starter mechanism that genome is a product of, instead of genome first being present to produce this intelligence.
The "intelligent" component is not in any way powering the Krebs Cycle that would exist regardless of molecular intelligence being present or not to control it. A rudimentary intelligence may instead be challenged to keep up with its production rate.
Molecular Intelligence (life) so profoundly changes the usual features of the universe that we can tell it is present from outside its solar system by there being an Oxygen concentration dangerously close to explosion that blows its atmosphere into outer space.[16] The culprit of firestorms and other cataclysm is detectable in part of the blue-green light spectrum not reflecting back from the surface consumed to power the Krebs Cycle that consumes CO2 gas giving off flammable to explosive O2 (Oxygen atoms pair up, a diatomic molecule) gas.
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 that uses TV and Radio 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.
Intelligence can build entities much like we do together to build cities, either inside or outside of cells. Molecular intelligence achieves the complexity of the cell. 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 to ice in cold like insects and other cold blooded living things of simpler design. On our level of 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 responds to environment through continual replication of a genetic memory where output actions are stored as on or off genes that catalyze production of protein cellular organelles that self-assemble from a range of molecules mass produced by the Krebs Cycle into increasingly complex molecular designs. 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 responds to environment through sensory molecules that address genetic switches (epigenetic) that can change during the lifetime of the genome but coding itself does not change. Vernalization stores seasonal climate information to know proper time to begin regrowth or bloom. All together these mechanisms that help a cell adapt without a change in the genome coding is the cellular intelligence. What changes code is at the molecular intelligence level, that does not require genes to be switched and when they are there is another level of functionality, it's a more moment to moment cellular intelligence. There may be a light sensitive brain-like mechanism involving centrioles that would certainly represent cellular intelligence. Observing microscopic single celled hay infusion protozoa show instinctive "behavior" inherent to design but is not in itself memory driven intelligence.
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 living things this 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 sensed.
The basic mechanism that produces the phenomena of intelligence can be modeled with a simple loop. 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 two control bits are connected to memory, it inherently self-organizes all inputs and outputs.
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.
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.
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.
LoopStart:
Call RunMotors
If Stall=0 then Conf(Addr)=Conf(Addr)+1 Else Conf(Addr)=Conf(Addr)-1
Addr = LMF + (LMR*2) + (RMF*4) + (RMR*8) + (Stall*16)
If Conf(Addr)<1 Then Call RandomGuess: Conf(Addr)=1
Goto LoopStart
This model is analogous to finger muscle control that through training become 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, 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 visual as in typing, 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 as in a brain, or genes that are located in a unique functional location in a chromosome that is in a unique chromosome territory inside the nucleus of the cell. 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 an organized recoding at some million times the normal rate to find a way to destroy an invader.
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 who or what is consumed.
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 strong need for knowledge is also why this theory exists.
Asexual reproduction (except for accidental mutation) makes perfect clones. The only time it tries something new is when a freak accident does its genome some good. It's "learning rate" would here be extremely slow.
Adding sexual-type crossover exchange will cause the intelligence to try new things likely to 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. Sexual reproduction 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.
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.
In the beginning before there was life molecular forces (bonding, polar) would self-assemble cell membranes (vesicles) and crystals like snowflakes then molecular intelligence made possible more complex tubulin based crystal designs including ATP synthase and flagellum motors. This complexity is achievable because of the way even a simple molecular intelligence inherently responds with a successful response and when challenged with an unknown can take a good guess what will work then remember the response. In sexual reproduction there is a crossover exchange of large amounts of genetic information that would appear to be random chaos but half the time produces viable offspring that are not perfect clones of the parents as there would always be without this crossover. After that genes are often copied or further moved around in an organized way that may seem like random jumping from place to place but it is analogous to learning where something belongs by trying different things to see what happens.
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 its 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.
REFERENCES
[1] Discovery Institute, Questions about Intelligent Design, What is the theory of intelligent design?
http://www.discovery.org/csc/topQuestions.php
[2] G. Gaulin, Intelligence Generator/Detector computer model, download.
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.
http://www.planet-source-code.com/vb...71381&lngWId=1
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. 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)
http://sites.google.com/site/intelli...Generator5.zip
[5] G. Gaulin, Demonstrating the Self-Assembly of the Cell Membrane, NSTA -The Science teacher, 10/1/2007
http://www.nsta.org/store/product_de...st07_074_07_72
Prior version, open access:
http://www.lessonplanspage.com/Scien...periment68.htm
[6] Guenter Albrecht-Buehler, Robert Laughlin Rea, Cell Intelligence (webpages)
http://www.basic.northwestern.edu/g-...r/cellint0.htm
[7] Harvard, Inner Life, animation.
http://multimedia.mcb.harvard.edu/an...erlife_lo.html
Also higher resolutions and videos:
http://multimedia.mcb.harvard.edu/media.html
[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
http://bi.snu.ac.kr/Courses/4ai06f/NCC2005.pdf
[9] Synthesizing cellular intelligence and artificial intelligence for bioprocesses, P.R. Patnaik, Institute of Microbial Technology, Sector 39-A, Chandigarh-160 036, India
http://www.aseanbiotechnology.info/A...t/21018478.pdf
[10] 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
[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.
http://www.seas.harvard.edu/environm...Z_JPP_2007.pdf
[12] X.V. Zhang and S.T. Martin, "Driving Krebs Cycle in Reverse through Mineral Photochemistry," Journal of the American Chemical Society, 2006, 128, 16032.
http://pubs.acs.org/cgi-bin/sample.c.../ja066103k.pdf
[13] Clays May Have Aided Formation of Primordial Cells
http://www.hhmi.org/news/szostak3.html
[14] Decision-Making Circuitry of Blood Stem Cells Mapped
http://www.hhmi.org/news/singh20060825.html
http://www.hhmi.org/research/investigators/singh.html
[15] Kyte J, Doolittle RF, Hydropathy index, Wikipedia, May 1982
http://en.wikipedia.org/wiki/Hydropathy_index
[16] JH Koeslag, What is Life, Physiology website
http://academic.sun.ac.za/med_physbi.../dept/life.htm
[17] Annalee Newitz, Princeton Scientists Discover Proteins that Control Evolution, io9, 11/11/2008
http://io9.com/5083673/princeton-sci...trol-evolution
[18] K. MacPherson, Evolution's new wrinkle: Proteins with cruise control provide new perspective, News At Princeton, 11/10/2008
http://www.princeton.edu/main/news/a...ion=topstories
http://scitation.aip.org/getabs/serv...cvips&gifs=yes