The Intelligence Machine
Copyright © 2001 by Tienzen (Jeh-Tween) Gong
- I. Dive into the un-computable universe
- II. The mapping principle
- III. Engineering a science
- IV. Designing parameters for an intelligence machine
- V. Topological neuron
- VI. Two design criteria
- VII. Window signal and signal memory
- VIII. Signal registration
- IX. Second registration & recall memory
- X. Topological map memory and recall memory
- XI. A thinking system (I)
- XII. A thinking system (II)
I. Dive into the un-computable universe
With a new Epistemology, there should be a new paradigm for
the 21th Century.
- The paradigm of 20th Century --- science connotes truth. Any
other claimed truth is only a proclamation.
- The paradigm of 21th Century --- Science is, of course, still
one of the truth machine, but it cannot reach the domain of
spacelessness or of timelessness which houses a huge
un-computable universe. The Occam's Happy Coincidence
Epistemology is the best truth tester. OHC epistemology is
able to cover all universes.
With this new epistemology, we are able to investigate the
spaceless, the timeless and the un-computable universe, and
one of the such a subject is intelligence.
Intelligence is the source for understanding physics, but
traditional physics is unable to understand how intelligence
- a) What process(es) give(s) rise to intelligence?
- b) What physical structure can house an intelligence
machine and can generate intelligence?
II. The mapping principle
For human, to understand a thing (its structure, internal
logic, and what not) must map it into human brain. That is,
a "subset" of human brain structure must be "isomorphic to"
If the entirety of the universe (its structure, logic, laws
etc.) can be mapped into human brain(s), then the universe
can be understood in its entirety by mankind.
If the entirety of God (His structure, logic, essence, laws,
etc.) cannot be mapped into human brain(s), then God is
uncomprehensible by mankind.
This "Mapping Principle" can be a tool to investigate two
We can "design" this artificial intelligent brain without
knowing anything about the human brain physiology.
- a) To "design" an artificial intelligent brain with a
"reverse engineering process" when we already understood
many other things, such as the physical universe.
- b) To find out whether God can be understood by this
artificial intelligent brain.
Can we design a brain better than a human brain?
Can this be a method to understood the structure of human
Let's find out these together.
III. Engineering a science
Is engineering a science? If it is, it is quite different
from the science of physics. For engineering, a problem can
be resolved without truly knowing its cause. For example,
a machine is vibrating very badly. Engineers can use some
vibrating isolators and shock absorbers to solve the problem
without knowing the cause of or eliminating the source of
In Fictitious Universe Epistemology, we are actually
"engineering" physics, and this provides many more degrees
of freedom in the research of physics. "The problem" of the
20th century (the unification between gravity and other
forces) was easily solved with "engineered physics", and
it only needs to fabricate three parts.
Even if the real universe were absolutely different from
the above "engineered" universe, this FU shows that there are
other possible universes and other possible physics.
- Part 1: We make h-bar as a viewing scope, and it divides
space-time into two worlds.
(See Constants of nature at:
One world is viewed by this scope, and this scope
moves with light speed (c). This "viewing act" must
have some meanings and can be represented with
something, and I call it -- FU electric charge,
FU(q) which is, of course, "logically" not related to
the electric charge of traditional physics. However,
a FU electric force can be defined.
FU(q) = (h-bar * c)^(1/2)
- Part 2: There is space-time in this h-bar scope itself.
The size of this scope is measured by its wavelength
lambda. Again, with this "measuring act," a FU mass
charge can be defined as:
FU(m) = h-bar/(lambda * c)
This FU mass charge is also logically not related
to the mass of traditional physics, but a FU gravity
can be defined by it.
- Part 3: The space of FU is measured with the following
delta S = c * delta T
With these three parts, we can "design" a force:
Force (FU) = (K * h-bar)/(delta S * delta T)
K: the coupling constant
This FU force, of course, unifies FU electric force with
IV. Designing parameters for an intelligence machine
The moving space-time can often obscure or even erase its
previous signatures. Thus, things can develop and become
quite distinct from their origins. The research of an
intelligent machine (brain) might not be able to discover
what intelligence itself is. And this type of research is
confined by its research object. We will have a much freer
hand by designing an intelligent machine from the ground up.
Now, we shall first set some designing parameters or list
some issues. If they don't work out, we change them.
We will not discuss the above as right or wrong issues. We
will simply "design" some mechanisms or processes to perform
- Is the known intelligent machine (brain) a source of
Or, is it (brain) a result (product) of an intelligence
Can intelligence be created by or composed of something
not intelligent? Human brain consists of three things:
Which one creates intelligence?
- a) matter (proton, neutron, electron, etc.)
- b) structures (cells, circuits, connections, etc.)
- c) activities (internal, external, etc.)
It would be the easiest if all three carry intelligence.
Do we truly have a free hand to design intelligence
in such a way? We could find this out together.
This engineering process has a similar problem to the
transcendence and immanence paradox of theology.
Does an intelligence exist apart from each intelligence
Does every and each part of an intelligence machine
We will discuss these in detail.
- Human brain seemingly has an infinite memory.
- The death of a small number of neurons in the course
of human life naturally seemingly will not cause a
- Intelligence consists of, but not limited to, the
- a) memory (different from computer memory)
- b) rational reasoning
- c) irrational reasoning
- d) emotion, spirituality, psyche, etc..
V. Topological neuron
We can design an intelligence machine by first designing a
topological neuron (the t-neuron). This t-neuron has the
- it has about 1,000 dendrites (short connections) and
many axons (long connections) to make contact with other
- This t-neuron is a topological torus (a ball with two
holes). One hole let things go into this t-neuron; one
let things go out from it. The hole can be as one point
or as the entire surface of this t-neuron.
- This t-neuron has an ideal "balanced" state.
- This t-neuron can get into an excited state by receiving
something (being fired or charged upon) through its
entrance hole (the end of one or many dendrites or axons).
This state can be called as waking state.
- When a t-neuron is fired repeatedly, it will finally
reach a fatigue state, and it no longer can be fired.
- An excited t-neuron can discharge something (firing)
through its exit hole and gets back to its balanced
- A fatigued t-neuron must go through a "reset process"
by discharge something (firing others) in order to
regain the ability to get into the waking state.
When a t-neuron is imbedded in a t-neuron mass, it must
follow a social rule. When its neighbors are waking, it
most likely be stay awake. A fatigued t-neuron cannot
easily be reset when others are waking. That is, a mass of
t-neuron must "reset" together about the same time.
A resetting t-neuron must, in fact, charge its neighbors.
However, a group (mass) t-neuron can still be reset with
the following tactics:
The bigger this t-neuron mass is, the longer the resetting
time it needs.
- a) Slow down the discharge rate together.
- b) Discharge to neighbors who are not as fatigue.
I do not know why animals and human need sleep. But I do
know that a t-neuron mass must be reset (t-neuron sleep),
and there are many t-neuronal activities during this
t-neuron sleep (the t-neuron dreams).
When a cell has only a handful neighbors (like a muscle
cell), it can be somewhat as itself. When a cell is
connected with thousands neighbors, it must obey the
It forms, at least but not limited to, three state:
It takes two processes to mediate the three states above.
- i) an ideal "balanced" state
- ii) an excited (being fired) waking state
- iii) fatigue state
Because every t-neuron is connected with thousands neighbors,
it cannot function the two processes alone but must
synchronize with others. This makes two states for a
- a) be charged by others
- b) resetting (charging other)
Yes, a t-neuron mass must sleep and will dream.
- i) waking state (with mental activities)
- ii) sleeping state (with dreaming activities)
After a memory system is designed for this t-neuron mass,
it can perform the followings:
This t-neuron dream will also provide a similar mechanism
for Freudian dreams.
- a) rational reasoning
- b) irrational reasoning
VI. Two design criteria
I do not know whether human brain can store infinite bits
of information or not. The intelligence machine we are designing
should be capable of doing so. Mathematicians would disagree
the using of the term of "infinite" here. I will make a definition
"R is the number bits information that our intelligent
machine can store. For a (any) nature number N, R can always
larger than N."
Strictly speaking, especially in terms of mathematics, R is
called "finite but unbounded," not infinite. However, in my
paper, I will make the terms of "unbound" and of "infinite" to be
inter-changeable. I will prove that the R of my machine is
"unbounded," and will simply call it "infinite." This might
not be exactly correct in terms of mathematics, but it is
convenient to our discussion, and it will not distort any of
My t-neuron mass (the intelligent machine) has a finite number
of t-neurons (about 10 billions) and has a finite number of
connections (about 10 trillions).
If the information is stored in individual t-neuron or its
circuits, we can always find a N larger than R. Perhaps, this
R is large enough for human's life time, but the R of my
machine must be infinite.
Because my t-neuron is a life, it could die. If information is
stored in individual t-neuron itself or its circuits, its
death can cause to lose a vital bit of information or to
disable an important memory circuit.
For the two reasons above, my design will not choose the
above methods regardless of whether human brain uses them or
In order to resolve the t-neuron death problem above, my
machine uses a group (for example, one million) t-neurons to
store one "unit" of information. Thus, the death of 10 or 20%
of this group will not lose the stored information.
In order to find an R to be an infinite, the finite number
of t-neurons must be used "over and over" for different
bits of information.
Information can be written on a "magic slate" (a toy) "over
and over." Of course, it does not have memory, and I will
resolve this problem later.
A magic slate has two states:
It must go back to the white sheet state before a new page
can be written.
- as a white sheet
- as a written page.
VII. Window signal and signal memory
Now, my t-neuron machine has a few windows; vision window
(eyes), sound window (ears), pressure window, etc..
When a chair is detected from a vision window:
- It generates a window signal.
- A group (about one million) t-neuron got excited when
they receive this window signal.
- This excited group forms a topological map (topo-chair)
in this t-neuron mass.
- When this window signal disappears, the topo-map
(topo-chair) disappears, and the t-neuron mass goes back
to its "random state" (the white sheet state).
- When a desk is detected, another group got excited
(written state) to form a topo-desk.
- Topo-chair and topo-desk can share some t-neurons
(10%, 20%, or...)
- When that same chair appears again, a new topo-chair forms,
and this new topo-chair is about 95% identical to the
old topo-chair in terms of their t-neuron members.
- After repeating the chair window signal, a "signal
memory" comes alive as the topo-chair's members become
consistent. Of course, this signal memory is far from a
With this "signal memory" mechanism, two problems are resolved.
- The R of this machine can be as an infinite because the
finite number of t-neurons can be used over and over.
The information is not stored in individual t-neuron itself but
is stored as a window signal topo-map (ws-topo-map). A finite
number of t-neurons can create infinite number of ws-topo-maps as
the t-neuron mass has two states:
When a t-neuron mass lose its ability to return to its random
state, some particular images (topo-maps) will stay and appear
over and again. This t-neuron mass will, then, suffer a
schizophrenia-like symptom. Drugs which can relax t-neurons
back to their random state will ease this symptom.
- a) The random state (the white sheet)
- b) The mapped state (the written page)
- Because the information is stored in a group (about one
million) t-neurons in the form as a topo-map, the death of 10% or
more of that group will not lose the stored information as the
topo-map should not be distorted significantly.
VIII. Signal registration
With a repeated window signal, a ws-topo-map will soon reach a
fatigue state which must begin to discharge. Because this
group is connected with one another via their dendrites (short
connections), it is much easier to discharge via axons (long
connections) under this circumstance. That is, a new topo-map at
a distant place is generated by this fatiguing ws-topo-map. I call
this action as registration of the ws-topo-map and this new topo-map
as register (reg-map) or syntax of this ws-topo-map.
Now, a chair can generate a window signal.
If we can design a mechanism to activate the reg-map internally,
this reg-map could activate the ws-topo-map without the
window signal. That is, we would have designed a recall
- This window signal generates a ws-topo-map in the t-neuron
- A fatiguing ws-topo-map generates a reg-map (or syntax)
at a distant part of the t-neuron mass.
With a recall memory, we can, then, design a "thinking
IX. Second registration & recall memory
Only with a "recall memory" system at hand, we can, then,
design a thinking machine. Although the window signal can
be registered in the t-neuron mass, a mechanism is needed
to activate its reg-map internally (without the window
signal) for a true recall memory system.
So far, the signal memory has a uni-direction only:
window signal --> ws-topo-map --> reg-map (syntax)
Now, four window signals are mapped into (not onto) a
t-neuron mass as followings:
These four reg-maps can be viewed as a new topo-map. When
this new topo-map is getting fatigue, it generates a 2nd
order reg-map (a relation-map). A relation-map can be called
- chair --> ws-topo-chair --> reg-chair
- table --> ws-topo-table --> reg-table
- baby --> ws-topo-baby --> reg-baby
- crying --> ws-topo-crying --> reg-crying
The next day, four window signals are mapped into this
t-neuron mass as followings:
- chair --> ws-topo-chair --> reg-chair
- table --> ws-topo-table --> reg-table
- baby --> ws-topo-baby --> reg-baby
- laughing --> ws-topo-laughing -->reg-laughing
Obviously, the topo-map of reg2nd (chair, table, baby,
crying) has a significant overlap with the topo-map of
reg2nd (chair, table, baby, laughing).
The more syntaxes the two reg2nd maps share, the less the
difference between the two maps. When the difference between
the two reg2nd maps reduces to a level, they can switch
from one to another and vice verse. I call this
very-alike switching (or va-switching).
That is, after a reg2nd (chair, table, baby, laughing) is
activated by some window signals, it has a chance to switch
to reg2nd (chair, table, baby, crying), and it can activate
reg-crying without the window signal of crying.
With the 2nd (or higher) order of registration system and
a va-switching mechanism, a recall memory system is designed.
Now, we need to summarize what we have designed so far.
- a t-neuron, as a ball with two holes; that is, it can
be charged and be discharged.
- a t-neuron mass (about 10 billion t-neurons), its member
has 1,000 dendrites and many axons.
- this t-neuron mass can be excited by window signals.
- t-neuron will reach a fatigue state after being excited
- there are two ways to reduce the t-neuron fatigue:
- i) t-neuron sleep
- ii) topo-map registration (1st, 2nd or higher order)
- the t-neuron mass is a white sheet
- this white sheet can be written repeatedly, as it always
goes back to a white sheet state when the external
- Although this t-neuron mass is a white sheet, a "signal
memory" can exist.
- the window signal can be registered (burnt in) as reg-map
at another part of this t-neuron mass
- The 2nd order registration forms a topo-map network
- With a va-switching mechanism and a topo-map network,
a recall memory system can be designed.
This topo-map network is not a hardware circuit which could
be found in this t-neuron mass because the background state
of this t-neuron mass is a white sheet (the randomness).
This topo-map network is formed with the followings:
If we feed two "identical" t-neuron mass (having identical
neuronal connections) with different window signals, their
topo-map networks will be completely different. They will be
two entirely different intelligent machines.
- a) signal memory
- b) registration pathways
- c) va-switching
- d) recall mechanism
For a window signal-chair, only when the reg2md-chair has
"burnt in," it will, then, become a part of the long term memory
from which the reg-chair can be recalled. That is, the t-neuron
mass is, now, having three different functioning sections:
- A section for window signal mapping.
- A section for short term memory -- 1st order of
- A section for long term memory -- 2nd order of
X. Topological map memory and recall memory
We have briefly outlined the design of a "recall memory"
system which starts out from a "signal memory." As we are
not researching the human brain memory system, there is no
issue about right or wrong on this signal memory. It is
simply an issue of whether it is a good or a bad design.
So, we are not obligated to provide an explanation of why
this signal memory should work. However, I do want to
provide an explanation on it here because that mechanism is
very important for our future design works.
The forming of ws-topo-chair is quite arbitrary when the
t-neuron mass receives a window signal-chair the first time.
The shapes of ws-topo-chair for two different t-neuron
mass could be quite different.
With a repeating window signal, two things happen to this
- the member of this map begins to join the group; that
is, it begins to recognize the other group members.
- the group as a whole moves toward to a fatigue state
which causes a registration, the generation of a reg-map.
Every t-neuron has over 1,000 dendrites, and we can number
them as follow:
(D1, D2, ..., D1000, ...)
A t-neuron (good boy) can join:
Thus, an individual t-neuron can be a member of many different
topo-groups. And this t-neuron will reduce the "activation
resistance" for a "group signal" of which it is a member. If
a signal in the form of (D1, D3, D8) is not recognized as a
member signal, the activation resistance will be higher, and
this t-neuron will not get excited until that signal is high
enough to draft this t-neuron to be its member.
- the topo-group1 with D3 connection
- the topo-group2 with (D3, D7) connections
- ..., etc.,
This membership mechanism gives rise to a "topo-map memory"
which applies to all topo-maps (ws-topo-map, reg-map,
reg2nd-map, etc.). Again, this topo-map memory cannot be
found as any hardware circuit in the t-neuron mass.
We, now, can give a more detailed outline of our t-neuron mass
memory system design as follow:
- The information is stored in topo-map ( a group
t-neurons, numbers from tens thousand to a few millions),
not in any individual t-neuron.
- An individual t-neuron can be a member of many different
topo-maps. Its ability to recognize those different
topo-maps gives rise to a "topo-map memory."
- With a fatiguing mechanism, a topo-map can be registered
with a reg-map.
- With a 2nd order registration, the reg2nd-map forms a
- With a very-alike switching mechanism, a recall memory
system can be designed.
XI. A thinking system (I)
We arbitrarily designed that the group size for the
ws-topo-map to be about one million t-neurons. The group
size for reg-map does not need that big; one-tenth of one
million should be fine. The group size for reg2nd-map can be
The smaller the group size a topo-map has, the easier it
can be activated.
Our t-neuron mass does not have a power supply plug. It is
"on" all the time. It is "on" even during the t-neuron sleep.
Although the background state for the t-neuron mass is a
white sheet (the randomness state), an reg2nd-map can be
activated even during the t-neuron sleep because of two
So, a reg2nd-group can be activated internally, and
randomly. This is an internal random activation mechanism.
- the activation resistance for a topo-map is much smaller
than the resistance of an arbitrary group.
- the reg2nd-group is relatively small.
The more often a topo-map is activated, the lower its
activation resistance will be.
Now, there are two ways to recall a topo-map:
Soon, a small group of reg2nd-maps will have a much lower
activation resistance compared to the others. This small
group of reg2nd-maps will, then, take over the control of
the neural activities of this t-neuron mass. When some
topo-maps can be activated internally in a non-random fashion,
a thinking machine has come alive in this t-neuron mass.
- by window signal via a very-alike switching mechanism.
- by an internal power activation mechanism of the
so, what is "thinking" in terms of the traditional definition?
With our new intelligent machine, we can define "thinking"
- Although window signal can be a part of thinking, thinking
is neural activities which do not rely on any window signal and
is activated internally in the t-neuron mass.
- Thinking is not a random activation process. It is
leaded by a small group of reg2nd-maps.
Thus, thinking must have a starting point (the initial
condition) and a pathway provided by those initial topo-maps.
With a "thinking" system designed, we, now, are able to
discuss the issues of:
XII. A thinking system (II)
- rational reasoning
- irrational reasoning
When a few reg2nd-maps light up at the same time, they form
a new topo-map, and it can also be registered. For the
designing concern, we can map this 3rd order registration
onto the reg2nd section, but this might not be a good design.
I will create a new section -- reg3rd-map and let all higher
order registrations (4th, 5th, ...) map onto this reg3rd
section. That is, there is no registration section higher
than reg3rd. This is not a right or wrong issue but is a design
Now, let's review our design again.
- Information (ws-topo-chair, reg-chair, reg2nd [chair,
desk,..], etc..) is stored in a group of t-neurons, not
in any individual t-neuron.
- Individual t-neuron has the ability to memorize all its
3) An individual t-neuron can be a member of many different
groups (topo-maps), such as a man can be a member of his
family, an auto club, a discount club, etc....
- Every group gathering will enhance the group memory
(topo-map memory) by lowering the activation resistance
for the group signal. This is called "burn in."
- Some topo-maps have much deeper burnt in than others, and
they can be activated much easier than others. Smaller the
topo-map's size is, easier it can be activated.
- Reg2nd-map forms a "small" topo-map network.
- Reg2nd-map can link to many other "small" topo-maps networks
via a very-alike mechanism and, thus, provides a recall
- Reg3rd-map (or higher order registrations) links all "small"
topo-map networks (reg2nd-maps) together.
- Topo-map is not a hardware structure in the t-neuron mass.
For two identical t-neuron mass (with identical neuronal
connections), one might have only 1,000 topo-maps stored
while the other has over one trillion topo-maps in it.
For the sake of discussion, I am mapping (projecting) all
topo-maps of a t-neuron mass onto a ball surface, depending upon
its activation resistance level and its registration relationships.
This ball will, then, look like a golf ball. Every point on this
golf ball represents one topo-map. The topo-map which has the
lowest activation resistance sits on the bottom of the valley of
this ball surface. The inside of this ball is an "internal energy
wheel" of this t-neuron mass. Obviously, only those topo-maps sit
in the bottom of the valley can be reached by this energy wheel
directly. Those topo-maps which sits on the hill can only be
activated indirectly or by some external energy -- the window signal.
This golf ball is my thinking machine -- with billions (or
trillions, ...) topo-maps and with an internal energy wheel.
Thinking is, thus, done in the reg2nd and reg3rd sections
because reg2nd and reg3rd are converging points of many
topo-maps and will have longer "burn in" time and lower
activation resistance. Thinking does not rely on any window
signal. Although the reg-(beautiful girl) can be recalled,
the ws-topo-(beautiful girl) cannot easily be activated by this
In a given time, a few points (topo-maps) on this golf ball
surface will be lighted up by this internal energy. I call this
a "frame" or a "page." In the next time step, it can have a few
other points being lighted up, or those old points can begin
to grow and to migrate. Thus, thinking can start from some
initial "thinking points" which form the first page. The moving
of the frames (pages) becomes a "thinking process." With
some thinking points and with a thinking process, a few
different thinking and reasoning processes can be designed.
Per my design, every "page" will also be registered. That is,
an entire thinking process (a book) can be recalled with a
much more efficient manner than its first pass creation can be.
This "booking mechanism" gives the thinking machine some
- The more a machine thinks, the more efficient it becomes.
- With some preferred thinking pathways, a thinking machine
could be trapped by those pathways and lose its creativity,
for not trying any new pathway.
Now, this t-neuron mass has four sections:
Because the reg3rd-map must also be a part of the long term
memory, the section 3 and 4 have some overlaps. That I list
them separately is just a design choice.
- Window signal mapping section -- ws-topo-map
- Short term memory section -- reg-map
- Long term memory section -- reg2nd-map
- Thinking section -- reg3rd-map.
Before the design of this t-neuron mass intelligence machine,
there were two contradictory facts:
- Physics laws govern the rise of the entire universe which,
of cause, includes intelligence.
- The issue of intelligence is beyond the scope of physics.
Now, things have changed. We can soon show that physics laws
and the underlying principles of intelligence arise from the same
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