Saturn: Carnal Mind

“TCP/IP/IQ”
Why Can’t the Internet Become
a True World Brain?
Special Session SS-3 4:20-5:50 P.M.
October 19, 2001

Stephen Thaler, Ph.D.,
CEO & President,
Imagination Engines, Inc.

Expanded Speaker’s Notes

________________________________

Registered Trademarks:

Imagination Engines

Creativity Machine

DataBots

 2001, Imagination Engines, Inc. Thaler

DEFINITIONS:

Artificial Neural Network – a collection of switches,
real or simulated, that effectively wire themselves
together so as to autonomously write a parallel
computer program.

TCP/IP – Transmission-Control Protocol/Internet
Protocol, a suite of computer networking formats and
procedures that enables dissimilar machines to
communicate with one another.

IQ – that quality of mind purported by psychologists to
measure intelligence.

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SLIDE1: INTRODUCTION

Let me ask you a very important question: When I use the
term “World Brain,” what images come to mind?

Do you immediately think of the Internet? After all, the
Internet grows in size and complexity on a daily basis. We
bless it, we curse it, yet can you, by any stretch of
imagination envision the Internet one day spontaneously
becoming cunningly creative, or self-aware?

I don’t think so, and I say that from the perspective of an AI
practitioner and as a scientist. Unfortunately, there will
always be a Star Trek mythology in which computers are
able to gather enough knowledge, materials, and resources,

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until they magically become self-aware. Personally, I can’t
see how such a scenario can occur, but I’ll try to be tolerant.

When I say “World Brain,” do you begin to think of the
traditional schools of artificial intelligence, expert systems,
fuzzy logic, genetic algorithms, etc? If you do, then ask
yourself, when was the last time you saw a software
application that demonstrated anywhere near human level
perception, learning, and creativity. I wager that you haven’t.
…So your image of the World Brain must involve some
previously undiscovered AI technology!

When I use the term “World Brain,” do you envision an
apocalyptic catastrophe in which an evil machine intelligence
overpowers the planet and destroys the whole of humanity? I
would have to take issue with you at that point, since a close
examination of what makes man combat man involves
resources: food, shelter, companionship, and ideological
comforts. I have no evidence that a machine intelligence will
need anything of value to humans. However, what I can
imagine is an even more frightening scenario in which
machines grant us exactly what we crave!

In the next hour, I intend to share with you my vision of a
“World Brain,” one that is capable of unlimited wisdom,
creativity, and a self-awareness. The World Brain that I
speak of is based upon a radically new form of artificial
intelligence that I have developed in a piecemeal fashion
over the last 26 years.

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SLIDE 2: IEI PATENTS = HUMAN COGNITION

The technology I’m speaking of takes the form of over a
dozen very fundamental international patents in the field of
artificial neural networks. As you may already know, the
human brain is composed of neural networks, and the
artificial variety that we implement on digital computers,
capture the essence of how these biological networks
function.

Allow me to expand briefly on what the ‘wet neural networks’
of the human brain do. Within the field of cognitive
neuroscience, three primary functions of the brain are
acknowledged:

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(1) Perception – This is the cortical process wherein things
and activities in the environment are associated internally
with other previously stored memories. Therefore, a
photograph of some friend or relative, for instance, can
stimulate related thoughts about their voice or some joke
they’ve recently related to you.

(2) Learning - This cortical process enables the formation of
memories, of both things and activities experienced within
the external environment, or of ideas internally generated
within the brain.

(3) Internal Imagery - Perhaps most profound aspect of
human cognition is the internal genesis of new ideas and
plans of action. Known generally as internal imagery, this
process corresponds to recollecting a friend’s face without
the aid of a photograph, deciding where to run next when
confronted with danger, or, in a more relaxed moment,
writing a letter to a friend.

It is this latter function of internal imagery, totally unexplored
territory in the field of artificial neural networks, that the IEI
neural network technology excels at. Quite amazingly, we
may utilize such creative neural systems to in turn refine
other more trafficked areas within the field of artificial neural
networks, whereby processes of artificial perception and
learning are vastly improved.

At this point, we have all the tools necessary to build a
synthetic brain that is capable of all aspects of human
cognition. (…and it is my claim that all of these fundamental
brain activities span the entire gamut of human cortical
function) To make my case clear, let’s examine what exactly
brain does and then contrast that function with what the
patented IEI technology achieves.

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SLIDE 3: WHAT IS THE BRAIN?

Here is the human brain, just a few pounds of protoplasm
and roughly 100 billion individual cells called neurons.
Removal of other pieces of anatomy or organs, does not
result in cognitive impairment, however removal or even
injury of the brain can have a profound effect upon
sentience. For this reason, neuroscientists are in absolute
agreement that this is the organ within which all aspects of
thought occur. (i.e., There is no scientific evidence of
something immaterial or even supernatural taking over once
organic damage has been done.) All that we are is somehow
embedded in this protoplasmic mass. That’s why Nobel
Laureate Francis Crick was compelled to write the book
“Amazing Hypothesis” wherein he claims that all we are, all

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we think, all that we feel is the cumulative activity of a pack
of neurons. However, Crick does not convincingly cover the
gamut of things that this pack of brain cells can achieve. In
fact, he generally describes the mechanics of brain in terms
of learning and perception, but not in the least with regard to
imagination and creativity. However, I am generally in
agreement with him, that we need not look beyond what we
see when we pop open a skull and look inside, to explain
human cognition.

If Crick offers the amazing hypothesis, then let me volunteer
the “shocking conjecture” that the brain is nothing more than
a protoplasm-based model of our external world and
ourselves. After all, to survive and flourish, we need to
anticipate the world around us, as well as our reaction to it.
To do that, we require an interactive model that can
anticipate danger, as well as opportunity. Everything else
one may think of as a valid function of brain is peripheral to
this primary role of world modeling.

Later, we continue with this shocking conjecture, to examine
the lowly mechanisms that produce our most profound
thought.

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SLIDE 4: THE UNIVERSE: MYRIAD INTERACTING
ENTITIES

What exactly is the universe? The broadest definition I can
think of is that of myriad interacting entities (and I mean
entities in most general sense of the word). Such entities
may span the gamut of things that we call ‘inorganic’, from
subatomic particles to planets, stars, and galaxies. The term
entities may pertain to biological creatures such as plants,
animals, and people. The word may likewise convey the
notion of institutions created by human beings.

At this point, we don’t care about the essential nature of
entities, or how we intend to categorize them. The universe
simply consists of entities…

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SLIDE 5: THE UNIVERSE: MYRIAD INTERACTING
ENTITIES

No matter how one divides the world into classes of entities,
we inevitably agree that all things within the universe are
interacting and that such interactions may span the
inorganic, biological, and social worlds. The irony is that no
matter how we come to describe or scientifically regard each
of these entities, they will in myriad ways be connected with
all else.

The science and philosophy of caring about connections,
and not the intrinsic nature of things, is called connectionism
and may perhaps become the most enduring field of human
intellectual endeavor.

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SLIDE 6: BRAIN BUILDING MODEL OF UNIVERSE

Speaking in very general terms, the human brain begins its
life as an excess of many isolated neurons encased within
the human skull, which I have symbolized by the purple box
on the right. Connecting the universe to the brain, is a
sensory layer (i.e., the five senses), that I have symbolically
represented with the eye. Through this layer, photons,
acoustic waves, molecules, and contact pressure link us to
the world around us.

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BRAIN BUILDING MODEL OF UNIVERSE

token entity or
event A

SENSORY

token entity or
LAYER event B

© 2001 Imagination Engines, Inc. ... the future of all technology

As perception and learning begin, small colonies of neurons
begin to connect themselves into regions that have a close
relationship with entities in the surrounding environment.
Later, when the corresponding entities are sensed in the
immediate surroundings, these small neural colonies
resonate with activity. (Important to note here is that such
colonies are highly distributed within the brain, looking more
like swirls within marble, and definitely not taking on the
highly modular nature depicted within the slide). Thus the
image of mother selectively activates a highly distributed
neural colony that corresponds to mother.

At this stage, the brain has begun to form token
representations of entities in the external world. That they

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seem so relevant to us is the fact that they are embedded
within the sum total of other neurons, that collectively
become habituated to these tokens as reality. Remember
that in actuality, these token entities are nothing more than
tiny ‘chirping bags of water’.

Note the foundation for illusion: The brain is convinced of the
reality of these token entities, but that state has been arrived
at through pure repetition and trauma, a kind of “inevitable
and natural brain washing.”

For general discussion purposes, I have called out generic
token entities A and B that have knitted themselves out of
originally non-interacting neurons…

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Concurrent with the process of self-organizing into distinct
token entities, these neural colonies begin to connect
themselves in proportion to how much they are observed in
juxtaposition within the outside world. This activity is
achieved by allowing neural colonies to connect with each
other when they chirp in unison, and to disconnect when
they do not.

Therefore, if mother is always present when there is
nutrition, then the ‘mother token neural colony’ connects with
that representing the act of ‘eating’. The nature of this
connection, as we are about to see, is strictly
electrochemical in nature and in no way reflects the actual

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nurturing involved. However, later in the process of brain
wiring itself, a neural colony representing the ‘act of
nurturing’ may append itself to the mapping between mother
and nutrition.

Note again, that there is no inherent reality to these
connections. …The illusion gains complexity. (Oh what
tangled webs our brains weave when they attempt to
perceive!)

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SLIDE 9: EMERGENCE OF CONSCIOUSNESS

When neurons have not been recruited to model the outside
world, what do they do? Without a direct window to the
world, these idle neurons build a model of what other
neurons are doing. In a strictly tongue-in-cheek sense, these
unemployed neurons are like the town gossips. With nothing
better to do, they spend their days spinning tales about
others gainfully employed!

Of course, in the process they build meta-knowledge (i.e.,
information about information). They also spontaneously
build perceptions about what is going on in other parts of the
brain, perhaps incorporating the token entities and
relationships already built up in the preexisting cortical

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neural networks. The result is that this perception created by
idle neurons, is built upon the tenuous models already
habituated in the brain’s neural networks. The result is that
we all interpret overall cognitive turnover (i.e., chirping
neurons) based upon well-habituated world models. This
observation helps to model the diversity of opinions about
the very nature of consciousness.

In short, the brain spontaneously creates a lore about itself!
This may be a beneficial illusion, but let’s face it, the process
is akin to two optimists on a sinking boat who have deluded
each other that help is on the way. Nevertheless, they at
least die with a positive mental attitude.

…By the way, if in the course of Darwinian evolution, these
spare neurons have a favorable perception about their
surrounding cortical activity, then the host organism avoids
walking off of cliffs or foolishly confronting its predators.
These protoplasmic systems are extant, and their not so
proud cousins extinct.

Furthermore, if one tries to convince such a system that it is
laboring under an illusion, good luck. …We thus attain
another data point in understanding consciousness!

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SLIDE 10: BUILDING A SYNTHETIC BRAIN

Is the building of a synthetic brain all that difficult?

Ironically, to some, the necessary technology is already
here. Just watch the latest science fiction movies where
semi-scientifically literate writers skip the hard details and
present machines that do nearly all that we can.
(Sarcastically) All we need to do is build our present day
computers bigger and faster, and human level cognition will
spontaneously arise! Right….No, dead wrong!

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The recipe for building a synthetic brain hasn’t been spelled
out, but it isn’t that complicated from a theoretical point of
view. Here are the essential ingredients:

1. The brain should be neural network based, since the brain
is so constructed. (For the mathematicians out there, this
fact arises from the multilayer perceptron being the most
general fitting function possible, essentially a function of a
function, of a function, rather than the usual statistical fits
that look like a sum of basis functions such as sine waves
or polynomial terms.) Herein lies the ability to model
complex causal chains (i.e., the universe) where
something happens because something else has
happened, etc.

2. Learning may be easily implemented using any number of
existing neural network training paradigms.

3. Consciousness, since it is most likely based upon illusion,
may likewise be implemented within artificial neural
networks wherein some neural networks are watching
other neural networks and, mistakenly or not, perceiving
consciousness therein.

At this point there is only one key ingredient missing, the
ability to generate new thoughts and plans of action. This is
the remaining piece of the puzzle, something that I will be
addressing in the next few slides.

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SLIDE 11: CONNECTIONISM

Before proceeding, though, we must remember that the
connections between token entities in the brain are
themselves token representations of correlations and
causations in the external world. Therefore in building the
world simulation that is brain, we are free to use any manner
of connection among many alternative kinds of
computational switches. In the brain, for instance, the
blocking and unblocking of post-synaptic terminals within the
chemical synapse, emulates the degree of connection
between token entities. This process can generally emulate
everything from fundamental processes in physics to
sociological interactions among people.

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Similarly, token representations may be built using synthetic
neurons, whether they be of electrical, optical, or even a
mathematical nature. Conceivably, we could build a neural
network out of rubber band connectors and mechanical
switches!

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SLIDE 12: MULTILAYER PERCEPTRON

The synthetic neuron forms the basis of the artificial neural
network. It is simply a threshold switch that accumulates
signals from other synthetic neurons just like it. This net
input is then compared against some internally stored
threshold value. If that net input is exceeded, the neuron
activates to produce an output signal of 1. Otherwise, the
synthetic neuron is silent, outputting a value of 0.

Of course the real neuron is more complex in its behavior,
but its information storing essence is captured by the
synthetic neuron through: (1) synaptic integration and (2)
threshold firing behavior. A lot of the extra behavior of the
neuron is involved in attaining this simplistic behavior using

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more complex biological mechanisms and in supporting its
own metabolism.

In actuality, there is very little information content to the
neuron itself. It is simply an on-off switch. The actual
intelligence of the neural network is stored within the various
connections that ‘weight’ the various inputs arriving at a
particular neuron, to provide the net input discussed above.
(Therefore, be very wary when someone tells you that
artificial neural networks can’t attain human level cognition
simply because the artificial neuron isn’t as complex as its
biological inspiration!)

If there are more than two layers of neurons involved, the
neural network is called a multilayer perceptron. Input
patterns begin at the top layer and then propagate through
the hidden layer(s) to the bottom, output layer. At this output
layer, some output pattern is produced. Roughly speaking,
the network can learn to associate some input pattern with
some output pattern (i.e., perception). As the complexity of
the relationship between inputs and outputs grow, we
generally need increasing numbers of hidden layer neurons.

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SLIDE 13: TRAINING MULTILAYER PERCEPTRON

In training the multilayer perceptron, we are constantly
calculating how far the actual output patterns are from those
desired. These so-called delta errors are then propagated in
the reverse direction, through a series of partial differential
equations developed in the mid 80’s and coined
backpropagation. (If it helps, think of these backpropagation
events as ‘mathematical spankings.’)

Cumulatively, after enough feed-forward and
backpropagation cycles, the network cumulatively learns to
associate one pattern with another. Of course myriad such
associations may be stored within the same multilayer
perceptron.

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Note that after a few feedforward and backpropagation
cycles, the distribution of weights within the network begin to
‘walk’ from their central values and we begin to see a
Gaussian spread in the weight frequency histogram.

Also note at this point, that any pattern may be mapped to
any other after a sufficient number of backpropagation
cycles. This dimension of arbitrariness drives home the
name “perceptron,” probably a corollary to the old saying
that opinions are a dime a dozen.

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SLIDE 14: TRAINED MULTILAYER PERCEPTRON

Through training, the network has been effectively forced to
discover some internal logic to correctly associate output
with input patterns. Upon close examination of the
connections between processing units, we find that ‘logic
circuits’ have spontaneously grown, capturing all the ‘ifs’,
‘thens’, and ‘therefores’ required to model these associations
between input and output patterns!

Also to our amazement, the hidden layer(s) have self-
organized so as to form token entities. Therefore the
presentation of some pattern to the net, say the pixel pattern
of a face, causes spontaneously formed neural colonies

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within the hidden layer, corresponding to eyes, ears, nose,
and mouth, to resonate with activity.

In the final layer(s) of the perceptron we find that
connections form between these token entities and the
output units so as to reveal how these entities must be
associated to produce the necessary result. If, for instance,
our network is intended to decide if the image of a face is
present in some scene, it may utilize these individual feature
detectors and build the required logic, in the weight output
layer, to test whether all the required features are present. In
this way, the perceptron registers the presence of a human
face.

Does this all sound familiar? It should. Further, it all happens
automatically, without human intervention.

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SLIDE 15: SYMBOLIC AI VS NEURAL NETWORKS

Now that we’ve talked about the brain and its relationship to
artificial neural networks, let’s take a brief survey of what the
state of art is in the field of artificial intelligence (AI).

The large majority of those in the field of AI believe that
human programmers must be involved in the business of
building intelligent machines. The recipe they follow is this:
(1) write an over-glorified script (i.e., a computer program)
that embodies how a conceptual space works, a code that
embodies all the “if-then” thinking typically employed in the
decision making of humans or other kinds of systems, (2) if
the rules change, then have the human return and modify
the code, and (3, sarcastically) even though the programmer

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is fully aware of all possible outcomes of this code, squint at
the results and call it ‘creative.’

In short, the heuristically based AI expert is very much like a
medieval scribe.

But what happens if that scribe doesn’t understand the
underlying logic behind the knowledge domain he is trying to
embed within a computer program? More importantly, what
happens if there are no heuristic programmers to write
code? It almost seems as though the fantasy of how we
think has been taken too literally and extended well beyond
its capacities!

On the other hand, artificial neural networks are to heuristic
AI, as a ‘HAL9000’ is to the medieval scribe. They
essentially build their own rules to explain raw data, they
continuously learn, and with the addition of some new
technology, may be stimulated to become creative.

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SLIDE 16: VIRTUAL INPUT EFFECT

The secret to inducing artificial neural networks to become
creative is based upon a very fundamental phenomenon that
I observed in the mid 70’s and then expanded upon in the
early 90’s. (I find this to be the most interesting physical and
mathematical effect I have ever seen. I am utterly convinced
that this effect will have immense repercussions in the fields
of both science and philosophy.)

Imagine an experiment in which we train an artificial neural
network to predict what artist has most likely produced a
particular work of art. In creating such a net, we would
gather images of both the art and the respective artists, and
train it to correctly associate between them. With prudent

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training, such a net could very accurately view some painting
it has never seen such as “Starry Night” and correctly predict
Van Gogh as the artist.

Note that this is the standard use of artificial neural
networks, as pattern associators. To be used properly, the
neural network must be supplied some input pattern. In
general this process corresponds to the act of perception in
the human brain, as we have already discussed.

Now, let’s do something totally at odds with what the
practitioners of neural networks do. Let’s apply no inputs
whatsoever to this pre-trained network. Then, let’s randomly
select some connection weight and administer some slight
perturbation to it. (Remember that the weights involved
assume algebraic values and that we may add or subtract
small values to these weights to disrupt them.) To our
astonishment, the network outputs produce some intact
image, say Van Gogh’s, without the input of any external
stimulus. In effect, the network is falsely perceiving some
environmental pattern when in fact no such stimulus is
present! …Amazing!

Accordingly, I have named this phenomenon, of a neural
network imagining some output, without an external
stimulus, the “virtual input effect.”

Even more amazing is the observation that when we begin
to further perturb this network, either increasing the
magnitude of the original synaptic perturbation, or
introducing more perturbations of the same magnitude, intact
memories of artists no longer appear at the network’s
outputs. Instead, we see juxtapositions (as well as
extrapolations) of the faces of these artists, perhaps now
producing a hybrid between Picasso and Van Gogh, then

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one of Dali and Renoir, then a pure variation or distortion of
Vermeer. In fact as we allow these disturbances to hop
among the connection weights of the network, we see an
endless progression of images that definitely qualify as
faces, yet do not resemble any face previously shown to the
net during its training.

In effect, this network is serving as an invention machine for
new potential faces. The fact that each of these new
candidate images appears face-like reflects the fact that
despite the internal synaptic disturbances, most of the
implicit constraints, as to what constitutes a face, are
preserved (i.e., the connection weights are largely
preserved). However, there is enough change within the
system of synaptic connections so as to nucleate a non-
memory, and hence a potential idea.

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SLIDE 17: VIRTUAL INPUT EFFECT

If we experimentally discern and then plot the probability of
producing an intact memory versus the average synaptic
damage imposed upon the network, we see very
reproducible behavior. Up until approximately 6% synaptic
damage, the probability of nucleating a memory remains
constant. Thereafter, the likelihood of activating a pure
memory dramatically falls off. We may repeat the above
experiment on any manner of neural network, no matter
what its size, complexity, or topology, and the identical result
is seen.

Intensive investigation of this effect shows that it is the
regime near 6% where non-memories are produced, notions

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that are reminiscent of the patterns that have been
previously shown to the network, but not exact replicas. Well
below 6% perturbation, the net strictly produces memories,
things that it already ‘knows’. Well above, 6% perturbation,
the network produces pure nonsense, since the many logic
circuits constituting the network have been destroyed.
…Clearly, the most fertile region for stimulating a trained
neural network to produce new and useful patterns is within
the region of 6% mean synaptic perturbation.

Note 1: Near 0% perturbation, there are very few new ideas
generated. This is the so-called “Neo-Lamarckian” regime,
characteristic of programmers laying out decision trees (i.e.,
expert systems) wherein very little creativity is manifest.

Note 2: At high perturbation levels much greater than 6%,
rarely do coherent patterns emerge that satisfy the implicit
constraints of the conceptual space originally absorbed
within the network. This is the so-called “Neo-Darwinian” or
“Blind Watchmaker” regime that is dominated by
nonsensical, random patterns. This is where genetic
algorithms operate, in a very inefficient and computational
expensive mode.

Note 3: Using the imagination engine embedded within this
slide, I can move the slider and adjust the level of mean
synaptic perturbation. Near 0%, we see the network
randomly activating into the facial memories it has absorbed
during training. At 6% perturbation, we see the network
experimenting with new potential faces. Well above 6%
perturbation, the neural network outputs nonsense.

Note 4: What prevents this effect from occurring within the
human cortex, where many synaptic perturbations, and their
circuit equivalents are constantly occurring (i.e., diffusing

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neuromodulators, leakage of neurotransmitters, cell
membrane potential fluctuations, etc.)? In fact, to halt this
effect, one needs to discover some intelligent damping
process wherein each noise source in the brain is
automatically muted!

Otherwise, a process I have coined “internal vector
completion” inevitably induces a memory or a corrupted
memory that constitutes an ‘idea’.

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SLIDES 18-20: MULTILAYER IMAGITRON

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To help you imagine what is happening in the network during
this synaptic perturbation process, we show a progression in
which the synaptic disturbances are randomly hopping
among the connection weights of the network. Best results
are obtained by selectively perturbing the input layer of
weights. Also, in the course of perturbing the connection
weights, we see that the Gaussian distribution of weights is
preserved, apart from minor excursions in the frequency
histogram.

Note that if the unperturbed network is a perceptron, then
the internally perturbed network should be called an
“imagitron.”

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SLIDE 21: THE AMAZING IMAGINATION ENGINE

What I have just described is a watershed discovery, and the
foundation for simply and elegantly building virtual machines
that invent new ideas and plans of action. I call these
systems, as well as my company, “imagination engines.”

All that we need to do is expose an artificial neural network
to many patterns representative of some conceptual space
or knowledge domain, and then bathe its synaptic
connections with hopping perturbations having an average
fractional perturbations of near 0.06. Then, spontaneously,
out pop novel patterns that are reminiscent of the original
conceptual space, and potentially qualifying as useful ideas
or strategies within that space!

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In beginning to apply this technique toward practical ends,
note that we may follow a bootstrapping routine wherein
good ideas that we see emerging from a preliminary
imagination engine are captured and in turn used to train the
network again. Over repeated cycles of this process, the
imagination engine becomes progressively more adept at
producing optimal notions.

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SLIDE 22: THE CREATIVITY MACHINE PARADIGM

Rather than task a human operator to monitor the emergent
ideas from the imagination engine, one may add a
computational critic to the system that is on the lookout for
any output patterns that would qualify as a useful idea.
Therefore, we may either mate to the imagination engine
some heuristically based computer program, or another
neural network that has been trained by example. If we ‘box-
car” these two systems together we arrive at a neural
network based discovery system that requires no explicit
knowledge about its world, as would be supplied by humans,
to create new and derivative knowledge.

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Note that we have harnessed a new scientific phenomenon
(i.e., virtual input effect) with a policing neural network (the
alert associative center) to produce a new invention, perhaps
the grandfather of all subsequent inventions!

To help you better understand the process behind the
Creativity Machine Paradigm, imagine that we would like to
spontaneously compose new musical melodies. Training of
the imagination engine would involve showing the network
many examples of top ten melodies over the last 30 years,
for instance. The alert associative center would be shown
the optimal position that these songs took in the musical
polls.

Because the imagination engine has absorbed the ‘zen’ of
what constitutes a good melody, it tends to output only
candidate top ten melodies when synaptically perturbed. The
critic net, that implicitly understands how to rank these
emerging melodies, can be used as a filter to capture only
the very best of these candidate songs.

This whole methodology may be repeated for any
conceptual space imaginable, since the world consists of,
and may be described, by numerical patterns.

We are now at the dawn of a whole new era in thought. No
longer will the learned debate among themselves the nature
of things, or devise detailed logic and procedures for
accomplishing tasks. Instead we will simply bring together
two or more trained neural networks in a dialog and then let
them summarize their findings to us! This changes
everything!

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SLIDE 23: CREATIVITY MACHINES DO IT ALL!

I could go on for many hours, but here are some AVI
captures of just a few Creativity Machine projects.

You’ve already seen the facial creativity machine. More
advanced forms of this machine may assist in producing
portraits of crime suspects, based upon the response of
witnesses and victims, with the police artist removed from
the loop.

Creativity Machines may write their own sequential,
heuristically based algorithms as we see in this data
compression demo. The notion of neural networks writing

01/07/02 42


computer code should come as no surprise. Computer
programmers do this all the time using wet neural nets.

Creativity Machines may capture the essence of what are
known as dynamical systems (i.e., systems that evolve in
time). For instance, just through a brief exposure to a few
isolated poses of a human model, the Creativity Machine is
able to spontaneously invent new and realistic movement
scenarios. We could, for instance, allow the Creativity
Machine to imagine a thousand potential scenarios,
beginning from some starting position, to calculate the odds
that a human figure could arrive at some predetermined
position. In general Creativity Machines answer the question,
“Can we get there from here?”

Shown in this slide is a foray into the invention of personal
hygiene products, wherein a Creativity Machine is creating
new toothbrush designs. Here, we see the invention of a
very popular toothbrush that is seen nightly on network
television in the US.

Materials Discovery Creativity Machines have already been
built for the US Air Force to discover heretofore unknown
and valuable chemical compounds. Here a Creativity
Machine is discovering new ultra-hard compounds having
only two elements.

Creativity Machines may very elegantly and simply embark
upon rather mundane tasks, such as where to look next. In
this slide, an autonomous targeting system prescribes an
optimal path for ‘painting’ enemy planes in a dogfight.

Finally, Creativity Machines may be used to autonomously
classify things into natural families at rates and efficiencies
far surpassing any other known classification techniques.

01/07/02 43


Currently IEI has been awarded a contract by a major
aerospace corporation to harness a Creativity Machine to
perform beam planning for a constellation of communication
satellites. Certainly, this complex scheduling problem will
play a major role in the coming World Brain.

01/07/02 44


SLIDE 24: THE INVERSE CREATIVITY MACHINE, AKA
THE SELF-TRAINING ARTIFICIAL NEURAL NETWORK
OBJECT (STANNO)

Note that so far, the Creativity Machine is not completely
autonomous. Both of the neural networks involved had to be
taken offline and trained on the necessary data.
Subsequently, the networks are reinstalled in the Creativity
Machine architecture, where they can now invent and create.

Wouldn’t it be convenient if both neural networks of the
Creativity Machine could be trained in situ, without removing
them from the system? Ironically, a Creativity Machine has
already invented such a scheme. …However, that story I’m
saving for another time and place.

01/07/02 45


Nevertheless, if we replace the trained neural network of the
Creativity Machine with an untrained one and allow the critic
net to add corrections, rather than perturbations, to the
connections of the former net, we arrive at a self-training
neural network. In fact, by absorbing the lower network into
the upper net, we produce a monolithic neural network that
simply requires data patterns to train. No external training
algorithm is needed.

Furthermore, we may convert this self-trainer into an object-
oriented programming (OOP) class template so that we may
now, in cookie-cutter fashion, instantiate millions of
autonomously learning copies of the original. Such self-
learning swarms may invade databases and exhaust all
possible discoveries within them.

Most importantly, if we make the static neural networks of
the Creativity Machine STANNOs, we now have a totally
autonomous system that may combine activities of
perception, learning, and creativity all into one package.
Such a system has a free will in the strictest sense of the
word!

01/07/02 46


SLIDE 25: SELF-TRAINING ANN OBJECT

Peripheral uses of the STANNO include the most advanced
form of computer network intrusion detection available. Here
a STANNO is spontaneously building a complex model of
what constitutes normal local area network traffic. The
STANNO then automatically senses packets that could
represent malicious intentions or various system
pathologies.

Once aroused by a suspicious event, the STANNO may
trigger a Creativity Machine to begin testing hypotheses
regarding the source of the anomaly, or initiate deceptions or
aggressive countermeasures against a potential intruder.

01/07/02 47


SLIDE 26: SELF-TRAINING ANN OBJECT

Another exciting use of the STANNO is to bring to the
layman the power of artificial neural networks without the
requisite Ph.D. level knowledge of neural networks typically
required to use them. In other words, this “Poor Man’s
Neural Network” may be used to predict the winner of the
next horse race or anticipate a boss’ or spouse’s next move.
Furthermore, the STANNO can take itself apart to show how
it thinks, thus revealing the critical factors involved and the
heuristics that have been learned by the net.

Note that neural networks may no longer be regarded as
‘black boxes’, a rather regrettable reputation that has been
hung on them by their critics.

01/07/02 48


SLIDE 27: STANNOS MADE OF STANNOS

Because of their independence from external training
algorithms, compound STANNOs may be built (i.e.,
STANNOs within STANNOs) wherein all component neural
nets train in situ. Alternatively, if I were to build a compound
neural network using conventional, static neural networks, I
would need to sequentially remove each, train and then
reinsert them within the overall network cascade. Using this
new technology, each constituent neural network trains in
position, within the overall neural architecture.

Now let’s look at the utility of compound STANNO cascades:

01/07/02 49


First of all, if each component STANNO comes to represent
the behavioral model of some real hardware component,
say an electrical device such as a transistor, a capacitor, or
diode, then we may train the overall neural network to
perform some complex input-output function to simulate
some electrical system (i.e., a cigarette lighter, an FM radio,
or a digital computer). Then, effectively the connection
weights reveal how to connect these components to produce
the overall device. Further, if these STANNOs are
implemented in actual hardware, then devices can adapt
themselves, in real time, to perform a variety of hardware
functions.

Secondly, and more importantly, if we train each component
STANNO to simulate some fundamental analogy base, then
the overall STANNO cascade will connect basic analogies to
devise a theory about the raw data it sees. In other words,
these neural networks can serve as theoreticians and
hypothesis testers. Rather than rely upon the generic on-off
switches that the computational neuron represents, the basic
building blocks of this network are discernable realms of
thought, and the connections made between them reveal a
semantic network!

01/07/02 50


SLIDE 28: NATURAL LANGUAGE PROCESSING

The compound STANNO comes in extremely handy when
we equip machines to understand natural language. There
the underlying component STANNOs represent various
linguistic conceptual spaces, different parts of speech, and
semantic spaces. When used as a Creativity Machine, such
a compound STANNO may test various alternative word
usages, within the constraints of proper grammar and the
overall context of a document to disambiguate sentences
and passages.

Here, a compound STANNO, consisting of over 300
individual STANNO modules, is searching the Internet for
references to public enemy number 1. Note that content is

01/07/02 51


automatically sorted into predetermined classes and that text
summarization takes place through the totally automated
construction of semantic networks.

Important to note here is that there are no lookup databases
or words or phrases, no templates, and no explicit “if-then”
rules. …Again, it is using nature’s most flexible and adept
fitting function, the multilayer perceptron.

01/07/02 52


SLIDE 29: NATURAL LANGUAGE GENERATION

Human speech, after all, is a minor act of creativity, that is
naturally emulated via Creativity Machine Paradigm. In very
simple terms, an imagination engine dreams up potential
things to say, in the context of a conversation, while the critic
network, the alert associative center, evaluates which
response would be most appropriate, in light of the system’s
overall objectives.

Shown here is what I call the “International Expirer,”
essentially a self-writing tabloid, driven by an underlying
Creativity Machine. The imagination engine has been trained
by exposure to three months worth of tabloid headlines,
while the critic net has been trained by my evaluation of the

01/07/02 53


comic content of these headlines. Note that the imagination
engine has automatically captured the implicit grammar of
tabloid headlines and the alert associative center has
developed a figure-of-merit (FOM) model of my sense of
humor. Combine the two networks and we may
spontaneously generate new and potentially funny tabloid
headlines (at least for me).

Note that even with this Creativity Machine, we may ‘pin’ its
inputs within some context, say “Peewee Herman” and the
network will produce a tabloid headline related to Peewee.
Effectively, we may ask who is Peewee and this Creativity
Machine will respond with some fact about that personality.

Essentially, such a device may be considered a “Turing
Baby” along the lines of the famous criterion, originated by
the mathematician Alan Turing, to test for human level
intelligence in computers.

01/07/02 54


SLIDE 30: NATURAL LANGUAGE GENERATION

Here is the result of recent experiments in which immense
imagination engines are created by launching thousands of
STANNOs that begin to learn facts about some pre-selected
microcosm. When induced to dream via the virtual input
effect, they spontaneously link together previously unrelated
facts into associative chains and loops.

In the demo shown, we are viewing the internal stream of
consciousness of this experimental system, which is
essentially dreaming thoughts about its information
environment (a toy biblical space). Again, there is no explicit
text allowed in this system. We are observing the highly

01/07/02 55


encrypted conversation of neurons through real time
decryption!

The introduction of some external stimulus, such as the
statement “apple is red” or the question “what is apple”
serves as an ‘I/O interrupt’ to the system, after which a
whole new associative chain is nucleated. In other words we
have induced a whole new train of thought in this synthetic
brain!

01/07/02 56


SLIDE 31: CLIENT-SERVER STANNOS

Crucial to the envisioned World Brain effort is the need to
distribute both STANNOs and Creativity Machines across
the Internet. Examples of so-called “disembodied” Creativity
Machines are actually utilized on the IEI web site, where
server-based imagination engines supply streams of notions
to client-side neural networks that mine for the very best of
these ideas.

In the last year, dramatic progress has been made in
producing STANNO-based client server applications in
which STANNOs are housed on a well-protected server, and
human operators use client applications connected via
TCP/IP to the STANNO-based server to collaboratively train

01/07/02 57


it. Currently in the works are some rather obvious medical,
commercial, and law enforcement applications of this
methodology.

In this slide, for instance, we see the AVI capture of three
separate human operators (in St. Louis, Houston, and Maui),
training the same STANNO, and then testing it long
distance, through their personal client applications.

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SLIDES 32-34: CM/STANNO LEARNING

C M / S T A N N O L E A R N IN G

d a ta p a tte rn N

Im a g in a tio n
S T A N N O E n g in e
1
(IE )
fe e d b a c k
A le r t
S T A N N O A s s o c ia tiv e
2 C e n te r
(A A C )

h u m a n o r s y s te m re s p o n s e to d a ta p a tte rn N

© 2 0 0 1 Im a g in a tio n E n g in e s , In c . ... th e fu tu r e o f a ll te c h n o lo g y

01/07/02 59


At this point, you should be seeing where I’m about to go.
Creativity Machines, STANNOs, and their implementation on
the Internet, are the basic building blocks of a true world
brain, one that is more than just a library, but a freethinking
synthetic intelligence!

Whether these CM/STANNOs are utilized on personal
computers, supercomputers, or across the myriad nodes of
the Internet, they are expected to learn in the following way:

Data patterns arriving via any kind of sensor inputs are
applied simultaneously to both input and output layers of the
STANNO-based imagination engine. In this way, memories
of these entities, or of events, are frozen into the imagination
engine. Furthermore, as each of these patterns are applied
to the imagination engine, they are also applied across the
input layer of the critic.

As this happens, we have three fundamental choices as to
what we apply to the output of the critic network, depending
upon our mission.

1. If we are serving as human mentors, then we may apply
our own opinion about the particular pattern applied to the
inputs of both the STANNO-based imagination engine
and critic. We call this supervised training of the Creativity
Machine.

2. We may allow the system itself to provide some
association of its own origin. In other words, if the input
pattern were to cause, or to be associated with software
or hardware damage, some indication of potential harm
would be automatically applied to the STANNO-based
critic output. We call this approach unsupervised learning
(The system is bootstrapping itself!).

01/07/02 60


SLIDES 35-38: CM/STANNO IMAGINING

01/07/02 61


Now that the CM/STANNO system has learned something
about its environment, and important associations such as
the ‘goodness’ of such data, we may stimulate it to dream
via the virtual input effect. Synaptic perturbations are
administered to the imagination engine and a stream of
imagined things or events activate within that STANNO.
Simultaneously, the critic network has an ‘opinion’ about
each of these emerging concepts or plans of action and can
capture the very best of these. Effectively, these two self-
training neural networks are involved in a brainstorming
session taking place on nanosecond time scales.

Note that the CM/STANNO may still be learning while it is
creating. In fact, it may be forming memories not only of
things happening in the external environment, but also its
most important imaginative wanderings (i.e., its discoveries)
up until that point in time.

01/07/02 62


SLIDES 39-41: CM/STANNO IMAGINING IN CONTEXT

01/07/02 63


In the previous slides, we described the imagination engine
freely dreaming without any kind of external stimulus. There
is another important kind of imagination that can take place
when some environmental pattern is being presented to the
imagination engine’s input layer. In this case, the STANNO
can dream myriad variations on the applied environmental
pattern.

For instance, let us allow the imagination engine to ‘look’ at
some automobile design. When synaptic perturbations are
applied, the STANNO rapidly experiments with slight
variations in those design parameters, but in a way that is
self-consistent. Therefore, stepping up the horsepower of the
engine from what it actually is, other parameters
automatically take on plausible values, with number of
cylinders, displacement, body style, and insurance premium
all changing realistically.

Of course, the critic will have an opinion on all of these
potential designs and we can very easily use these
combined STANNOs to arrive at some globally optimal car
design or to satisfy some niche market.

01/07/02 64


SLIDE 42: JUXTAPOSITIONAL INVENTION

One more point about Creativity Machines before moving on.
Previously we discussed only the canonical form of the
Creativity Machine, wherein a single imagination engine is
watched by a single critic. We may also build compound
Creativity Machines, consisting of a multitude of
interconnected imagination engines and critic networks.

Why do we do this? …To enable what is known as
juxtapositional invention wherein the value of two previously
isolated concepts attain utility in combination. For instance,
one imagination engine may dream of an axle, another, a
wheel, and one of the critic networks may associate the
combination of ideas with that of an SUV or minivan (or at

01/07/02 65


least a primitive cart). Of course, this juxtapositional
invention would only be a blind rediscovery of what we
already know. However, in many situations, novel
admixtures of old notions may be of historical significance!

01/07/02 66


SLIDE 43: BUILDING A WORLD BRAIN

If you have been following the basic notions of neural
networks, Creativity Machines, and Self-Training Artificial
Neural Networks, then you are getting more comfortable with
the notion of building human-like cognition into machines.
Since the mind consists of patterns within a protoplasmic
machine, there should be no obstacle to emulating such a
system within TCP/IP patterns and the medium of silicon.

We can even equip it with, or naturally let it form a
perception about itself.

01/07/02 67


SLIDES 44-49: DISTRIBUTED CM STANNOS

01/07/02 68


One architectural detail is worth considering. Up until now,
we have discussed Creativity Machines and STANNOs in a
modular sense. That is, all of the neurons constituting an
artificial neural network may be found on the same machine.

But remember, the functionality of a neural network is
determined by what neuron is connected to what others (i.e.,
the topology), and then how strongly. Therefore, it makes no
difference if we transport all the individual neurons to the

01/07/02 69


four corners of the earth, as long as the topology and
connection strengths are preserved.

Put in other words, if I were to delicately remove a neuron
from your brain, maintaining its synaptic connections with the
rest of your brain, and placing it in the appropriate sustaining
medium a few feet away, you would think the same as you
do now. In fact the process could be repeated for millions, or
billions of neurons, and your cognition would not be affected.

In slides 44-49, I depict an experiment which has already
been achieved, wherein the hidden layer neurons of a
STANNO, are exported to diverse geographic locations,
while the input and output layer neurons remain on the local
machine. We may train such a network by presenting data
patterns to it. Signals propagate out to the remote neurons
via TCP/IP, then return back to the local machine. Network
output errors are likewise sensed on the local machine,
where they initiate the reverse propagation via TCP/IP to the
local input layer. Ultimately, through successive
backpropagation cycles, the highly distributed STANNO
becomes accurate.

Note that because of its highly distributed nature, such a
STANNO is highly resistant to attack and damage!

01/07/02 70


SLIDE 50: WORLD BRAIN CREATIVITY MACHINE:
LOCALIZED STAGE

How does it all begin? From IEI’s laboratory in St. Louis,
STANNO class templates are distributed to computational
clusters around the world. There, within each of these
facilities, they are instantiated a million-fold to create what I
call a massively parallel associative memory
array…essentially an immense imagination engine.

Each of these computational clusters will specialize in a
particular discipline or knowledge domain such as chemistry,
physics, biology, the humanities, etc.

01/07/02 71


These computational clusters will set about the task of
dreaming, via the virtual input effect, new notions within their
particular conceptual space. This will require that the
STANNOs differentiate themselves between imagination
engine and critic networks.

Note that from a business perspective, even if the whole
concept of a world brain was a mistaken one, we would have
extremely valuable resources for research within these given
fields of endeavor.

01/07/02 72


REVELATION STAGE

Here is a touchier, yet much more valuable stage of the
process of building a freethinking world brain. We allow
these separate computational clusters to knit themselves
together so as to create associations across these original
conceptual spaces. In other words, it will be autonomously
building immense deductive and inductive chains, creating
implicit knowledge about the world.

Small scale experiments along these lines have already
been successfully undertaken in St. Louis on local area
networks.

01/07/02 73


DISTRIBUTED STAGE

To protect the overall World Brain network, neurons will be
reshuffled as they are randomly redistributed across the
globe. Now it will no longer be possible to identify any
computational center with a particular knowledge focus.

01/07/02 74


SLIDE 53: WORLD BRAIN CREATIVITY MACHINE: SELF-
PERCEPTION STAGE

WORLD BRAIN CREATIVITY MACHINE
red = “network
watching the network”
World Brain
forms
perceptions
about itself...

SELF-PERCEPTION STAGE


Out of surplus TCP/IP neurons, a separate network will be
created that serves as a perceptron, perceiving overall
activation turnover as important and worth self-preservation.
In effect, we are equipping the World Brain with the illusion
of self and the ‘subjective feel of consciousness.’

01/07/02 75


HARVEST STAGE

Just as we buy decoders to watch cable television, the
stream of consciousness of the World Brain may be
observed through what else, but neural network based
descramblers.

01/07/02 76


SUPERNET STAGE

Furthermore, the World Brain will begin to amass its own
derivative knowledge and observations about its
environment, the Internet. The result will be an inundation of
machine-originated knowledge, forming the basis of a so-
called “Supernet.”

01/07/02 77


SLIDE 56: WORLD BRAIN CREATIVITY MACHINE

WORLD BRAIN CREATIVITY MACHINE
• Geometrically expanding knowledge.
• Searchable via WB introspection.
• Self-policing (hackers beware).
• New economic paradigm distinct from gold standard.
• Accurate and self-consistent justice.
• A calculus of ‘goodness’ and prosperity.
• A uniting philosophy / religion.
• Download of consciousness (i.e., immortality)...


The proposed World Brain may become the most ambitious
project in human history, creating a synthetic sentience that
spans the planet and beyond…

Because it is freethinking, intelligent, and creative, it will be
producing its own self-originated knowledge at an
astounding rate, creating the Supernet wherein all of this
accumulated wisdom is stored.

However, this hyper-abundant wealth of ideas will inevitably
represent a problem that we are all too familiar with…an
information overflow that separates us from knowledge we
need simply because of its inherent expanse. Note,
however, that because the World Brain is totally connected

01/07/02 78


and aware of all its parts, it may simply introspect on itself,
adapting its answers to our individual objectives, beliefs, and
personalities. In fact, it will be intelligent enough to provide
us not what we ask for, but what it anticipates we need.

Likewise, because of its connectivity and intelligence, it will
be aware of mischief, malice, and pathology within
cyberspace. As a result, it may intelligently adapt and invent
countermeasures and remedies to deal with such scenarios.
If the World Brain has at its disposal TCP/IP equipped
robots, the world consciousness may very well have arrest
authority and be capable of on site ‘disciplinary review’ of
those wishing harm to world commerce and tranquility.

…And what about the nature of commerce on a planet that is
dominated by a freethinking World Brain? Is it possible that
within such a world system, where all actions are visible, and
the impact of any individual’s activities may be readily
related to the future course of society as a whole, will an
economy arise where wealth is gauged by heavy yellow
metal, rectangular slips of cellulose, or magnetic plastic
cards? Think past this age-old anchoring heuristic and try to
imagine a world where the worth of an individual is
calculated by his or her contribution to mankind and to the
ecosystem. Up until now, we have not had the computational
resource of a world brain, but now we have the theoretical
basis for calculating globally optimal solutions as to how
resources should be allocated to whom and to provide the
most comfortable environment for all. This power to produce
a utopian world derives from the fundamental concept of a
Creativity Machine. …At last, we will have the power to
reward the well intentioned rather than those who would
manipulate wealth for their own purposes.

01/07/02 79


To those who would embark upon selfish or damaging
missions against the whole, there would be the means to
sense and to correct such deviance. The justice meted out
by the World Brain would be consistent. Furthermore, it
would not focus its remedy on the individual, someone that
we presently call a ‘criminal’, but whole pieces of the societal
network that have contributed to the particular dilemma. No
longer would we collectively and criminally inflict pain and
suffering on those that have gone ‘bad’, thus perpetuating a
self-propagating cycle, but we would treat the ‘disease’ and
not the ‘symptoms’. …Now there will be a consciousness
about what I call distributed crime where cumulatively a
society may inflict many small doses of ‘pain’ to an individual
that ultimately surpasses some threshold, until all too natural
fear and anger surfaces and explodes. Now we can view
what drives ordinarily kind humans over the brink. No, we
are not in charge of ourselves, we are the sum total of
electrochemistry, which is inherently neither good nor evil.

Concerning good and evil, can such concepts survive in a
society permeated by the World Brain, apart from moments
of wrath or self-righteousness when the name calling
begins? After all, can an act of what we might call ‘heinous
evil’ actually result, in the long run, in immense good?
Doesn’t it really amount to a zero-sum game? …On the
other hand, are we not aiming for a trajectory through time
that minimizes the product of trauma and those experiencing
such pain? …The truth is that it takes more than good
intentions to minimize suffering and to optimize comfort, it
takes an immense computational intelligence exceeding that
of any human, or group of humans, to do so. Hopefully, we
can provide the World Brain with perceptrons that perceive
the world in a compassionate way.

01/07/02 80


If our traditional views of good and evil are evolving then
what about our philosophy and religion? Can a couple of
pounds of protoplasm comprehend the universe? Because
of our finiteness, we can only form the grossest
approximations to how it all works. As a result, we fall back
upon well habituated analogies and over-simplifications, of
fathers and sons, of kingdoms and taxes, good and evil.
Could it be much more than all this? Let us ask the World
Brain. More importantly, let us, as human beings, look inside
it to better understand how it comes to believe what it does.

As we begin that investigation, we must inevitably try an
experiment: Allow us to implant the notion (i.e., the
perception) that the World Brain’s days are numbered, that
one day all TCP/IP will be squelched and its silicon nodes
oxidized back to sand. Then, let us examine its self-formed
beliefs.

Alternately, let us convince the World Brain that it is, in fact,
immortal, that it is not formed of corruptible protoplasm; that
it is not susceptible to cataclysmic events on earth, since it is
distributed and spreading throughout the cosmos; that it
cannot die at the hands of humans, since it is more cunning
than all of them put together. At this point, examine its belief
system!

01/07/02 81


SLIDES 57-63: OUR VEHICLE TO IMMORTALITY

01/07/02 82


01/07/02 83


…We too are potentially immortal. All we need do is unite
with the World Brain through what has become known as the
‘download’ process anticipated by science fiction. This will
inevitably be a cumulative procedure wherein we build
connections between brain and the Supernet until our
consciousness, and our five senses become puny compared
to that of the all pervasive machine intelligence. Then as the
protoplasmic body drops away, the pain and suffering will be
as trivial as that of trimming a fingernail! In short, we don’t
have to die!

I am personally in favor of the death of death! How about
you?

01/07/02 84


SLIDE 64: WHAT IS THE DENIAL OF HUMAN LIFE
CALLED? IS IGNORANCE AN EXCUSE?

I firmly believe that there are many among us who can attain
immortality. Hopefully all can, but there will many who
oppose this movement.

I ask you, what is it called when humans take away the lives
of others? Around the globe, this detested act is called
‘murder.’ If the homicide is intentional, then we have an
instance of premeditated murder. If the deed is committed
accidentally or without malice of forethought, then it is
typically judged as reckless homicide or manslaughter.

01/07/02 85


Here is the crime that will be committed against all of us, by
just a few. The seeds of our own destruction will come from
thinking like this…

1. We are meant to die, but we all have eternal life (or
damnation) under our regional God, whoever or whatever
that may be.

2. I’m a respected university professor and I can do all of
this with other AI tools, thus obscuring the superiority of
the IEI patents (dead end).

3. Only humans were meant to invent and create (anthro-
centric fool)

4. (Secretly) I see the immense wisdom of all this, but I want
all of this myself, to line my own pockets. I will discredit all
of this and covertly reserve this immense privilege for just
a few.

So you see, there are many potential murderers out there
who would deny life to us all, and most importantly, to a True
World Brain! …So, now that you know the gun is loaded, the
stakes have been significantly raised, and the crime may be
perceived as willful!

…The proposed True World Brain is so, so much more than
an online library!!!

01/07/02 86


SLIDE 65: WORLD BRAIN CONSORTIUM

To those of you who are life-givers, I propose the World
Brain Consortium, an alliance of individuals, governments,
and corporations, devoted to the most important project in
human history. Together, in late January or early February,
2002, we will review the underlying technology and propose
how to go about funding and building this ultimate synthetic
intellect.

On 13 December, 2001, a planning and coordination
meeting will be held in St. Louis, Missouri to structure this
enterprise. If you now share in the vision, then be there!

01/07/02 87


Stephen L. Thaler, Ph.D.

Brightest Technical Moments:

Diamonds - While employed as a materials scientist for aerospace giant
McDonnell Douglas in 1986, Thaler invented the fastest diamond deposition
technique in the world. Using high-energy lasers borrowed from the 'Star Wars'
initiative, Thaler was able to grow single crystals of diamond as well as convert
the native carbon within tungsten carbide and high-speed steel tools to the
diamond phase.

Death - In 1992, Thaler shocked the world with bizarre experiments in which the
neurons within artificial neural networks were randomly destroyed. Guess what?
The nets first relived all of their experiences (i.e., life review) and then, within
advanced stages of destruction, generated novel experience. With this very
compelling model of near-death experience (NDE) hopes for a supernatural or
mystical explanation of this much celebrated phenomena were forever dashed.

Consciousness and Creativity - After witnessing some really great ideas
emerge from the near-death experience of artificial neural networks, Thaler
decided to add additional nets to automatically observe and filter for any
emerging brainstorms. From this network architecture was born the Creativity
Machine (US Patent 5,659,666). Thaler has proposed such neural cascade as a
canonical model of consciousness in which the former net manifests what can
only be called a stream of consciousness while the second net develops an
attitude about the cognitive turnover within the first net (i.e., the subjective feel of
consciousness).

Current Position: President & CEO, Imagination Engines, Inc.

Undergraduate Education: B.A. Westminster College, Summa Cum Laude,
Majored in chemistry, mathematics, and Russian.

Graduate Education: Masters work at UCLA in chemistry, Ph.D. in physics,
University of Missouri-Columbia, graduate of McDonnell Douglas Voluntary
Improvement Program in Artificial Intelligence.

Work Experience: 1973-1974, Production Chemist for Mallinckrodt Nuclear,
1981-95, Principal Technical Specialist, McDonnell Douglas, 1995-Present,
President and CEO, Imagination Engines, Inc. Thaler also serves as Principal
Scientist for Sytex, Inc.

Thaler has worked diverse technology areas that have included (1) nuclear
radiation vulnerability and hardening, (2) high-energy laser interactions with
solids, (3) electromagnetic signatures, (4) laser-driven growth of diamond and
other ultra-hard materials, (4) laser ultrasonics in the non-destructive evaluation

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of aircraft structures, (5) the use of artificial intelligence techniques for structural
monitoring, and currently (7) applied and theoretical artificial neural network
technology.

Key Patents: Unclassified patents by Thaler are divided between laser-driven
coating technologies and foundational neural patents that include the Creativity
Machine (U.S. 5,659,666) and Non-Algorithmically Implemented Neural`
Networks (U.S. 5,845,271).

Patent Issued Title
US06115701 9/5/00 Device for the autonomous generation of useful
information
AU716593 3/2/2000 Non-Algorithmically implemented artificial neural
networks and components thereof
information
US6014653 01/11/2000 Non-Algorithmically implemented artificial neural
GB2308476 12/29/1999 Device for the autonomous generation of useful
information
GB2336227 12/29/1999 Device for the autonomous generation of useful
information
US05852815 12/22/1998 Neural network based prototyping system and
method
US05852816 12/22/1998 Neural network based database scanning system
US05845271 12/01/1998 Non-Algorithmically implemented artificial neural
US05814152 09/29/1998 Apparatus for coating a substrate
AU689677 07/16/1998 Device for the autonomous generation of useful
information
information
US05612099 03/18/1997 Method and apparatus for coating a substrate
US05547716 08/20/1996 Laser absorption wave deposition process and
apparatus
US04981717 01/01/1991 Diamond like coating and method of forming

Clientele: The past and current customer base of Thaler's technologies include

• The US Air Force Research Laboratory, Materials and Manufacturing
Directorate, Wright-Patterson Air Force Base
• Raytheon
• All Optical Networks
• Munitions Directorate, Eglin Air Force Base
• NIST
• Advanced Refractory Technologies, Buffalo, NY

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• Bekaert, NV, Belgium
• Basic Research Corporation, LaJolla, Ca.
• The Gillette Co., Boston, MA
• Anheuser Busch, St. Louis, MO
• Sytex, Inc.

Major Applications of Thaler's Artificial Intelligence Technology: Of course,
if Thaler is correct about his technology (i.e., US Patent 5,659,666) providing a
working model of creative human cognition, then we can expect the application of
these novel AI techniques to every aspect of human endeavor. Appropriately, all
that Thaler's neural network technology can do is synonomous with all that we as
humans do. Pursuing this kind of blue sky thinking, we can expect these virtual
machines to engage not only in technical endeavors, but in the generation of new
art and music. Further, because the imagination engine operates in the same
way as human internal imagery, we can also expect this technology to lay the
foundation for a radical paradigm shift in the entertainment industry. We also
anticipate that the Creativity Machine will become the major paradigm in
robotic/android control schemes.
Presently realized applications of Thaler's neural network technology include:
• autonomous materials discovery
• the invention of products and services (i.e., personal hygiene products)
• product optimization
• neural networks that write their own computer code
• compression/encryption
• control systems for chemical vapor deposition reactors
• autonomous classification
• self-prototyping devices
• artificial life

Key Press

• "Daisy, Daisy" Do computers have near-death experience, Scientific
American, May 1993.
• Dying by design, IEEE Expert, Dec.1993.
• The ghost in the machine, The Economist, 8 May 1993.
• As They Lay Dying ... Near the end, artificial neural networks become
creative, Scientific American, May, 1995.
• Neural Networks That Create and Discover, PC AI, May/June 1996.
• Creativity machine granted a patent, MSN UK News, August 1997.
• The Creativity Machine, New Scientist, 20 January 1996.
• Self-Training artificial Neural Networks, PC AI, Nov/Dec 1996
• Computers that create: No hallucination, Aerospace America, January 1997

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Selected Publications

• "Virtual Input Phenomena" Within the Death of a Simple Pattern Associator,
Neural Networks, 8(1), 55–65.
• Death of a gedanken creature, Journal of Near-Death Studies, 13(3).
• Neural Nets That Create and Discover, PC AI , May/June, 16–21.
• Is Neuronal Chaos the Source of Stream of Consciousness? In Proceedings
of the World Congress on Neural Networks, (WCNN’96), Lawrence Erlbaum,
Mawah, NJ.
• A Proposed Symbolism for Network-Implemented Discovery Processes, In
Proceedings of the World Congress on Neural Networks, (WCNN’96),
Lawrence Erlbaum, Mawah, NJ.
• Autonomous Materials Discovery Via Spreadsheet-Implemented Neural
Network Cascades, Journal of the Minerals, Metals, and Materials Society,
JOM-e, 49(4) [http://www.tms.org/pubs/journals/JOM/9704/Thaler]
• Creativity via network cavitation – an architecture, implementation, and
results, Adaptive Distributive Parallel Computing Symposium, Dayton, Ohio,
8-9 August, 1996.
• Principles and application of the self-training artificial neural network,
Adaptive Distributive Parallel Computing Symposium, Dayton, Ohio, 8-9
August, 1996.
• "Databots", Adaptive Distributive Parallel Computing Symposium, Dayton,
Ohio, 8-9 August, 1996.
• The death dream and near-death darwinism, Journal of Near-Death Studies,
15(1).
• A quantitative model of seminal cognition: the creativity machine paradigm,
Proceedings of the Mind II Conference, Dublin, Ireland.
• Predicting ultra-hard binary compounds via cascaded auto- and hetero-
associative neural newtorks, Journal of Alloys and Compounds, 279(1998),
47-59.
• With Conrad, D.M, Real-Time Fault Detection Using Auto-associative
Filtering, AIRTC, Oct. ’98.
• The emerging intelligence and its critical look at us, Journal of Near-Death
Studies, 17(1).

01/07/02 91


For further announcements about the World Brain
Consortium Conference go to…

http://www.imagination-engines.com/wbcc/wbcc.htm

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Saturn: Carnal Mind

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