Conscious Internet
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Conscious Internet



One of IEI's main themes is that it is single-handedly building the required artificial intelligence technology to emulate and perhaps exceed the capabilities of the human brain. Thus all of the key ...

One of IEI's main themes is that it is single-handedly building the required artificial intelligence technology to emulate and perhaps exceed the capabilities of the human brain. Thus all of the key cortical functions recognized by brain scientists, perception, learning, and internal imagery (a.k.a., imagination) have been developed at the IEI laboratory purely using artificial neural networks to emulate the biological neural networks of the human brain. Although the World Brain is not yet a reality, it is a long range goal of IEI to create a free-thinking entity, distributed across the Internet, that introspects upon human-originated content and then creates its own seminal thought and discoveries. For those of you who have read and understood IEI's web pages, you will likely realize that there is no other form of artificial intelligence capable of self-learning and creativity that can compare with IEI's patented technologies. Therefore, it is our ambition to perfect the network (i.e., WAN) mechanics for distributing its neural systems across the largest computational platform currently available, the Internet, and allowing them to knit themselves into a coherent brain using IEI's Supernet principles. If you are an investor, government, or corporation interested in supporting this effort, please contact Dr. Stephen Thaler at Imagination Engines, Incorporated.



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Conscious Internet Conscious Internet Document Transcript

  • “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. ThalerDEFINITIONS:Artificial Neural Network – a collection of switches,real or simulated, that effectively wire themselvestogether so as to autonomously write a parallelcomputer program.TCP/IP – Transmission-Control Protocol/InternetProtocol, a suite of computer networking formats andprocedures that enables dissimilar machines tocommunicate with one another.IQ – that quality of mind purported by psychologists tomeasure intelligence.01/07/02 2
  •  2001, Imagination Engines, Inc. ThalerSLIDE1: INTRODUCTIONLet me ask you a very important question: When I use theterm “World Brain,” what images come to mind?Do you immediately think of the Internet? After all, theInternet grows in size and complexity on a daily basis. Webless it, we curse it, yet can you, by any stretch ofimagination envision the Internet one day spontaneouslybecoming cunningly creative, or self-aware?I don’t think so, and I say that from the perspective of an AIpractitioner and as a scientist. Unfortunately, there willalways be a Star Trek mythology in which computers areable to gather enough knowledge, materials, and resources,01/07/02 3
  •  2001, Imagination Engines, Inc. Thaleruntil they magically become self-aware. Personally, I can’tsee how such a scenario can occur, but I’ll try to be tolerant.When I say “World Brain,” do you begin to think of thetraditional schools of artificial intelligence, expert systems,fuzzy logic, genetic algorithms, etc? If you do, then askyourself, when was the last time you saw a softwareapplication that demonstrated anywhere near human levelperception, learning, and creativity. I wager that you haven’t.…So your image of the World Brain must involve somepreviously undiscovered AI technology!When I use the term “World Brain,” do you envision anapocalyptic catastrophe in which an evil machine intelligenceoverpowers the planet and destroys the whole of humanity? Iwould have to take issue with you at that point, since a closeexamination of what makes man combat man involvesresources: food, shelter, companionship, and ideologicalcomforts. I have no evidence that a machine intelligence willneed anything of value to humans. However, what I canimagine is an even more frightening scenario in whichmachines 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 Ispeak of is based upon a radically new form of artificialintelligence that I have developed in a piecemeal fashionover the last 26 years.01/07/02 4
  •  2001, Imagination Engines, Inc. ThalerSLIDE 2: IEI PATENTS = HUMAN COGNITIONThe technology I’m speaking of takes the form of over adozen very fundamental international patents in the field ofartificial neural networks. As you may already know, thehuman brain is composed of neural networks, and theartificial variety that we implement on digital computers,capture the essence of how these biological networksfunction.Allow me to expand briefly on what the ‘wet neural networks’of the human brain do. Within the field of cognitiveneuroscience, three primary functions of the brain areacknowledged:01/07/02 5
  •  2001, Imagination Engines, Inc. Thaler(1) Perception – This is the cortical process wherein thingsand activities in the environment are associated internallywith other previously stored memories. Therefore, aphotograph of some friend or relative, for instance, canstimulate related thoughts about their voice or some jokethey’ve recently related to you.(2) Learning - This cortical process enables the formation ofmemories, of both things and activities experienced withinthe external environment, or of ideas internally generatedwithin the brain.(3) Internal Imagery - Perhaps most profound aspect ofhuman cognition is the internal genesis of new ideas andplans of action. Known generally as internal imagery, thisprocess corresponds to recollecting a friend’s face withoutthe aid of a photograph, deciding where to run next whenconfronted with danger, or, in a more relaxed moment,writing a letter to a friend.It is this latter function of internal imagery, totally unexploredterritory in the field of artificial neural networks, that the IEIneural network technology excels at. Quite amazingly, wemay utilize such creative neural systems to in turn refineother more trafficked areas within the field of artificial neuralnetworks, whereby processes of artificial perception andlearning are vastly improved.At this point, we have all the tools necessary to build asynthetic brain that is capable of all aspects of humancognition. (…and it is my claim that all of these fundamentalbrain activities span the entire gamut of human corticalfunction) To make my case clear, let’s examine what exactlybrain does and then contrast that function with what thepatented IEI technology achieves.01/07/02 6
  •  2001, Imagination Engines, Inc. ThalerSLIDE 3: WHAT IS THE BRAIN?Here is the human brain, just a few pounds of protoplasmand roughly 100 billion individual cells called neurons.Removal of other pieces of anatomy or organs, does notresult in cognitive impairment, however removal or eveninjury of the brain can have a profound effect uponsentience. For this reason, neuroscientists are in absoluteagreement that this is the organ within which all aspects ofthought occur. (i.e., There is no scientific evidence ofsomething immaterial or even supernatural taking over onceorganic damage has been done.) All that we are is somehowembedded in this protoplasmic mass. That’s why NobelLaureate Francis Crick was compelled to write the book“Amazing Hypothesis” wherein he claims that all we are, all01/07/02 7
  •  2001, Imagination Engines, Inc. Thalerwe think, all that we feel is the cumulative activity of a packof neurons. However, Crick does not convincingly cover thegamut of things that this pack of brain cells can achieve. Infact, he generally describes the mechanics of brain in termsof learning and perception, but not in the least with regard toimagination and creativity. However, I am generally inagreement with him, that we need not look beyond what wesee when we pop open a skull and look inside, to explainhuman cognition.If Crick offers the amazing hypothesis, then let me volunteerthe “shocking conjecture” that the brain is nothing more thana protoplasm-based model of our external world andourselves. After all, to survive and flourish, we need toanticipate the world around us, as well as our reaction to it.To do that, we require an interactive model that cananticipate danger, as well as opportunity. Everything elseone may think of as a valid function of brain is peripheral tothis primary role of world modeling.Later, we continue with this shocking conjecture, to examinethe lowly mechanisms that produce our most profoundthought.01/07/02 8
  •  2001, Imagination Engines, Inc. ThalerSLIDE 4: THE UNIVERSE: MYRIAD INTERACTINGENTITIESWhat exactly is the universe? The broadest definition I canthink of is that of myriad interacting entities (and I meanentities in most general sense of the word). Such entitiesmay span the gamut of things that we call ‘inorganic’, fromsubatomic particles to planets, stars, and galaxies. The termentities may pertain to biological creatures such as plants,animals, and people. The word may likewise convey thenotion of institutions created by human beings.At this point, we don’t care about the essential nature ofentities, or how we intend to categorize them. The universesimply consists of entities…01/07/02 9
  •  2001, Imagination Engines, Inc. ThalerSLIDE 5: THE UNIVERSE: MYRIAD INTERACTINGENTITIESNo matter how one divides the world into classes of entities,we inevitably agree that all things within the universe areinteracting and that such interactions may span theinorganic, biological, and social worlds. The irony is that nomatter how we come to describe or scientifically regard eachof these entities, they will in myriad ways be connected withall else.The science and philosophy of caring about connections,and not the intrinsic nature of things, is called connectionismand may perhaps become the most enduring field of humanintellectual endeavor.01/07/02 10
  •  2001, Imagination Engines, Inc. ThalerSLIDE 6: BRAIN BUILDING MODEL OF UNIVERSESpeaking in very general terms, the human brain begins itslife as an excess of many isolated neurons encased withinthe human skull, which I have symbolized by the purple boxon the right. Connecting the universe to the brain, is asensory layer (i.e., the five senses), that I have symbolicallyrepresented with the eye. Through this layer, photons,acoustic waves, molecules, and contact pressure link us tothe world around us.01/07/02 11
  •  2001, Imagination Engines, Inc. ThalerSLIDE 7: BRAIN BUILDING MODEL OF UNIVERSE 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 technologyAs perception and learning begin, small colonies of neuronsbegin to connect themselves into regions that have a closerelationship with entities in the surrounding environment.Later, when the corresponding entities are sensed in theimmediate surroundings, these small neural coloniesresonate with activity. (Important to note here is that suchcolonies are highly distributed within the brain, looking morelike swirls within marble, and definitely not taking on thehighly modular nature depicted within the slide). Thus theimage of mother selectively activates a highly distributedneural colony that corresponds to mother.At this stage, the brain has begun to form tokenrepresentations of entities in the external world. That they01/07/02 12
  •  2001, Imagination Engines, Inc. Thalerseem so relevant to us is the fact that they are embeddedwithin the sum total of other neurons, that collectivelybecome habituated to these tokens as reality. Rememberthat in actuality, these token entities are nothing more thantiny ‘chirping bags of water’.Note the foundation for illusion: The brain is convinced of thereality of these token entities, but that state has been arrivedat through pure repetition and trauma, a kind of “inevitableand natural brain washing.”For general discussion purposes, I have called out generictoken entities A and B that have knitted themselves out oforiginally non-interacting neurons…01/07/02 13
  •  2001, Imagination Engines, Inc. ThalerSLIDE 8: BRAIN BUILDING MODEL OF UNIVERSEConcurrent with the process of self-organizing into distincttoken entities, these neural colonies begin to connectthemselves in proportion to how much they are observed injuxtaposition within the outside world. This activity isachieved by allowing neural colonies to connect with eachother when they chirp in unison, and to disconnect whenthey do not.Therefore, if mother is always present when there isnutrition, then the ‘mother token neural colony’ connects withthat representing the act of ‘eating’. The nature of thisconnection, as we are about to see, is strictlyelectrochemical in nature and in no way reflects the actual01/07/02 14
  •  2001, Imagination Engines, Inc. Thalernurturing involved. However, later in the process of brainwiring itself, a neural colony representing the ‘act ofnurturing’ may append itself to the mapping between motherand nutrition.Note again, that there is no inherent reality to theseconnections. …The illusion gains complexity. (Oh whattangled webs our brains weave when they attempt toperceive!)01/07/02 15
  •  2001, Imagination Engines, Inc. ThalerSLIDE 9: EMERGENCE OF CONSCIOUSNESSWhen neurons have not been recruited to model the outsideworld, what do they do? Without a direct window to theworld, these idle neurons build a model of what otherneurons are doing. In a strictly tongue-in-cheek sense, theseunemployed neurons are like the town gossips. With nothingbetter to do, they spend their days spinning tales aboutothers gainfully employed!Of course, in the process they build meta-knowledge (i.e.,information about information). They also spontaneouslybuild perceptions about what is going on in other parts of thebrain, perhaps incorporating the token entities andrelationships already built up in the preexisting cortical01/07/02 16
  •  2001, Imagination Engines, Inc. Thalerneural networks. The result is that this perception created byidle neurons, is built upon the tenuous models alreadyhabituated in the brain’s neural networks. The result is thatwe all interpret overall cognitive turnover (i.e., chirpingneurons) based upon well-habituated world models. Thisobservation helps to model the diversity of opinions aboutthe 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 processis akin to two optimists on a sinking boat who have deludedeach other that help is on the way. Nevertheless, they atleast die with a positive mental attitude.…By the way, if in the course of Darwinian evolution, thesespare neurons have a favorable perception about theirsurrounding cortical activity, then the host organism avoidswalking off of cliffs or foolishly confronting its predators.These protoplasmic systems are extant, and their not soproud cousins extinct.Furthermore, if one tries to convince such a system that it islaboring under an illusion, good luck. …We thus attainanother data point in understanding consciousness!01/07/02 17
  •  2001, Imagination Engines, Inc. ThalerSLIDE 10: BUILDING A SYNTHETIC BRAINIs the building of a synthetic brain all that difficult?Ironically, to some, the necessary technology is alreadyhere. Just watch the latest science fiction movies wheresemi-scientifically literate writers skip the hard details andpresent machines that do nearly all that we can.(Sarcastically) All we need to do is build our present daycomputers bigger and faster, and human level cognition willspontaneously arise! Right….No, dead wrong!01/07/02 18
  •  2001, Imagination Engines, Inc. ThalerThe recipe for building a synthetic brain hasn’t been spelledout, but it isn’t that complicated from a theoretical point ofview. 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, theability to generate new thoughts and plans of action. This isthe remaining piece of the puzzle, something that I will beaddressing in the next few slides.01/07/02 19
  •  2001, Imagination Engines, Inc. ThalerSLIDE 11: CONNECTIONISMBefore proceeding, though, we must remember that theconnections between token entities in the brain arethemselves token representations of correlations andcausations in the external world. Therefore in building theworld simulation that is brain, we are free to use any mannerof connection among many alternative kinds ofcomputational switches. In the brain, for instance, theblocking and unblocking of post-synaptic terminals within thechemical synapse, emulates the degree of connectionbetween token entities. This process can generally emulateeverything from fundamental processes in physics tosociological interactions among people.01/07/02 20
  •  2001, Imagination Engines, Inc. ThalerSimilarly, token representations may be built using syntheticneurons, whether they be of electrical, optical, or even amathematical nature. Conceivably, we could build a neuralnetwork out of rubber band connectors and mechanicalswitches!01/07/02 21
  •  2001, Imagination Engines, Inc. ThalerSLIDE 12: MULTILAYER PERCEPTRONThe synthetic neuron forms the basis of the artificial neuralnetwork. It is simply a threshold switch that accumulatessignals from other synthetic neurons just like it. This netinput is then compared against some internally storedthreshold value. If that net input is exceeded, the neuronactivates to produce an output signal of 1. Otherwise, thesynthetic 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 thesynthetic neuron through: (1) synaptic integration and (2)threshold firing behavior. A lot of the extra behavior of theneuron is involved in attaining this simplistic behavior using01/07/02 22
  •  2001, Imagination Engines, Inc. Thalermore complex biological mechanisms and in supporting itsown metabolism.In actuality, there is very little information content to theneuron itself. It is simply an on-off switch. The actualintelligence of the neural network is stored within the variousconnections that ‘weight’ the various inputs arriving at aparticular neuron, to provide the net input discussed above.(Therefore, be very wary when someone tells you thatartificial neural networks can’t attain human level cognitionsimply because the artificial neuron isn’t as complex as itsbiological inspiration!)If there are more than two layers of neurons involved, theneural network is called a multilayer perceptron. Inputpatterns begin at the top layer and then propagate throughthe hidden layer(s) to the bottom, output layer. At this outputlayer, some output pattern is produced. Roughly speaking,the network can learn to associate some input pattern withsome output pattern (i.e., perception). As the complexity ofthe relationship between inputs and outputs grow, wegenerally need increasing numbers of hidden layer neurons.01/07/02 23
  •  2001, Imagination Engines, Inc. ThalerSLIDE 13: TRAINING MULTILAYER PERCEPTRONIn training the multilayer perceptron, we are constantlycalculating how far the actual output patterns are from thosedesired. These so-called delta errors are then propagated inthe reverse direction, through a series of partial differentialequations developed in the mid 80’s and coinedbackpropagation. (If it helps, think of these backpropagationevents as ‘mathematical spankings.’)Cumulatively, after enough feed-forward andbackpropagation cycles, the network cumulatively learns toassociate one pattern with another. Of course myriad suchassociations may be stored within the same multilayerperceptron.01/07/02 24
  •  2001, Imagination Engines, Inc. ThalerNote that after a few feedforward and backpropagationcycles, the distribution of weights within the network begin to‘walk’ from their central values and we begin to see aGaussian spread in the weight frequency histogram.Also note at this point, that any pattern may be mapped toany other after a sufficient number of backpropagationcycles. This dimension of arbitrariness drives home thename “perceptron,” probably a corollary to the old sayingthat opinions are a dime a dozen.01/07/02 25
  •  2001, Imagination Engines, Inc. ThalerSLIDE 14: TRAINED MULTILAYER PERCEPTRONThrough training, the network has been effectively forced todiscover some internal logic to correctly associate outputwith input patterns. Upon close examination of theconnections between processing units, we find that ‘logiccircuits’ have spontaneously grown, capturing all the ‘ifs’,‘thens’, and ‘therefores’ required to model these associationsbetween input and output patterns!Also to our amazement, the hidden layer(s) have self-organized so as to form token entities. Therefore thepresentation of some pattern to the net, say the pixel patternof a face, causes spontaneously formed neural colonies01/07/02 26
  •  2001, Imagination Engines, Inc. Thalerwithin the hidden layer, corresponding to eyes, ears, nose,and mouth, to resonate with activity.In the final layer(s) of the perceptron we find thatconnections form between these token entities and theoutput units so as to reveal how these entities must beassociated to produce the necessary result. If, for instance,our network is intended to decide if the image of a face ispresent in some scene, it may utilize these individual featuredetectors and build the required logic, in the weight outputlayer, to test whether all the required features are present. Inthis way, the perceptron registers the presence of a humanface.Does this all sound familiar? It should. Further, it all happensautomatically, without human intervention.01/07/02 27
  •  2001, Imagination Engines, Inc. ThalerSLIDE 15: SYMBOLIC AI VS NEURAL NETWORKSNow that we’ve talked about the brain and its relationship toartificial neural networks, let’s take a brief survey of what thestate of art is in the field of artificial intelligence (AI).The large majority of those in the field of AI believe thathuman programmers must be involved in the business ofbuilding 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 thatembodies all the “if-then” thinking typically employed in thedecision making of humans or other kinds of systems, (2) ifthe rules change, then have the human return and modifythe code, and (3, sarcastically) even though the programmer01/07/02 28
  •  2001, Imagination Engines, Inc. Thaleris fully aware of all possible outcomes of this code, squint atthe results and call it ‘creative.’In short, the heuristically based AI expert is very much like amedieval scribe.But what happens if that scribe doesn’t understand theunderlying logic behind the knowledge domain he is trying toembed within a computer program? More importantly, whathappens if there are no heuristic programmers to writecode? It almost seems as though the fantasy of how wethink has been taken too literally and extended well beyondits capacities!On the other hand, artificial neural networks are to heuristicAI, as a ‘HAL9000’ is to the medieval scribe. Theyessentially build their own rules to explain raw data, theycontinuously learn, and with the addition of some newtechnology, may be stimulated to become creative.01/07/02 29
  •  2001, Imagination Engines, Inc. ThalerSLIDE 16: VIRTUAL INPUT EFFECTThe secret to inducing artificial neural networks to becomecreative is based upon a very fundamental phenomenon thatI observed in the mid 70’s and then expanded upon in theearly 90’s. (I find this to be the most interesting physical andmathematical effect I have ever seen. I am utterly convincedthat this effect will have immense repercussions in the fieldsof both science and philosophy.)Imagine an experiment in which we train an artificial neuralnetwork to predict what artist has most likely produced aparticular work of art. In creating such a net, we wouldgather images of both the art and the respective artists, andtrain it to correctly associate between them. With prudent01/07/02 30
  •  2001, Imagination Engines, Inc. Thalertraining, such a net could very accurately view some paintingit has never seen such as “Starry Night” and correctly predictVan Gogh as the artist.Note that this is the standard use of artificial neuralnetworks, as pattern associators. To be used properly, theneural network must be supplied some input pattern. Ingeneral this process corresponds to the act of perception inthe human brain, as we have already discussed.Now, let’s do something totally at odds with what thepractitioners of neural networks do. Let’s apply no inputswhatsoever to this pre-trained network. Then, let’s randomlyselect some connection weight and administer some slightperturbation to it. (Remember that the weights involvedassume algebraic values and that we may add or subtractsmall values to these weights to disrupt them.) To ourastonishment, the network outputs produce some intactimage, say Van Gogh’s, without the input of any externalstimulus. In effect, the network is falsely perceiving someenvironmental pattern when in fact no such stimulus ispresent! …Amazing!Accordingly, I have named this phenomenon, of a neuralnetwork imagining some output, without an externalstimulus, the “virtual input effect.”Even more amazing is the observation that when we beginto further perturb this network, either increasing themagnitude of the original synaptic perturbation, orintroducing more perturbations of the same magnitude, intactmemories of artists no longer appear at the network’soutputs. Instead, we see juxtapositions (as well asextrapolations) of the faces of these artists, perhaps nowproducing a hybrid between Picasso and Van Gogh, then01/07/02 31
  •  2001, Imagination Engines, Inc. Thalerone of Dali and Renoir, then a pure variation or distortion ofVermeer. In fact as we allow these disturbances to hopamong the connection weights of the network, we see anendless progression of images that definitely qualify asfaces, yet do not resemble any face previously shown to thenet during its training.In effect, this network is serving as an invention machine fornew potential faces. The fact that each of these newcandidate images appears face-like reflects the fact thatdespite the internal synaptic disturbances, most of theimplicit constraints, as to what constitutes a face, arepreserved (i.e., the connection weights are largelypreserved). However, there is enough change within thesystem of synaptic connections so as to nucleate a non-memory, and hence a potential idea.01/07/02 32
  •  2001, Imagination Engines, Inc. ThalerSLIDE 17: VIRTUAL INPUT EFFECTIf we experimentally discern and then plot the probability ofproducing an intact memory versus the average synapticdamage imposed upon the network, we see veryreproducible behavior. Up until approximately 6% synapticdamage, the probability of nucleating a memory remainsconstant. Thereafter, the likelihood of activating a purememory dramatically falls off. We may repeat the aboveexperiment on any manner of neural network, no matterwhat its size, complexity, or topology, and the identical resultis seen.Intensive investigation of this effect shows that it is theregime near 6% where non-memories are produced, notions01/07/02 33
  •  2001, Imagination Engines, Inc. Thalerthat are reminiscent of the patterns that have beenpreviously shown to the network, but not exact replicas. Wellbelow 6% perturbation, the net strictly produces memories,things that it already ‘knows’. Well above, 6% perturbation,the network produces pure nonsense, since the many logiccircuits constituting the network have been destroyed.…Clearly, the most fertile region for stimulating a trainedneural network to produce new and useful patterns is withinthe region of 6% mean synaptic perturbation.Note 1: Near 0% perturbation, there are very few new ideasgenerated. 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 implicitconstraints of the conceptual space originally absorbedwithin the network. This is the so-called “Neo-Darwinian” or“Blind Watchmaker” regime that is dominated bynonsensical, random patterns. This is where geneticalgorithms operate, in a very inefficient and computationalexpensive mode.Note 3: Using the imagination engine embedded within thisslide, I can move the slider and adjust the level of meansynaptic perturbation. Near 0%, we see the networkrandomly activating into the facial memories it has absorbedduring training. At 6% perturbation, we see the networkexperimenting with new potential faces. Well above 6%perturbation, the neural network outputs nonsense.Note 4: What prevents this effect from occurring within thehuman cortex, where many synaptic perturbations, and theircircuit equivalents are constantly occurring (i.e., diffusing01/07/02 34
  •  2001, Imagination Engines, Inc. Thalerneuromodulators, leakage of neurotransmitters, cellmembrane potential fluctuations, etc.)? In fact, to halt thiseffect, one needs to discover some intelligent dampingprocess wherein each noise source in the brain isautomatically muted!Otherwise, a process I have coined “internal vectorcompletion” inevitably induces a memory or a corruptedmemory that constitutes an ‘idea’.01/07/02 35
  •  2001, Imagination Engines, Inc. ThalerSLIDES 18-20: MULTILAYER IMAGITRON01/07/02 36
  •  2001, Imagination Engines, Inc. ThalerTo help you imagine what is happening in the network duringthis synaptic perturbation process, we show a progression inwhich the synaptic disturbances are randomly hoppingamong the connection weights of the network. Best resultsare obtained by selectively perturbing the input layer ofweights. Also, in the course of perturbing the connectionweights, we see that the Gaussian distribution of weights ispreserved, apart from minor excursions in the frequencyhistogram.Note that if the unperturbed network is a perceptron, thenthe internally perturbed network should be called an“imagitron.”01/07/02 37
  •  2001, Imagination Engines, Inc. ThalerSLIDE 21: THE AMAZING IMAGINATION ENGINEWhat I have just described is a watershed discovery, and thefoundation for simply and elegantly building virtual machinesthat invent new ideas and plans of action. I call thesesystems, as well as my company, “imagination engines.”All that we need to do is expose an artificial neural networkto many patterns representative of some conceptual spaceor knowledge domain, and then bathe its synapticconnections with hopping perturbations having an averagefractional perturbations of near 0.06. Then, spontaneously,out pop novel patterns that are reminiscent of the originalconceptual space, and potentially qualifying as useful ideasor strategies within that space!01/07/02 38
  •  2001, Imagination Engines, Inc. ThalerIn beginning to apply this technique toward practical ends,note that we may follow a bootstrapping routine whereingood ideas that we see emerging from a preliminaryimagination engine are captured and in turn used to train thenetwork again. Over repeated cycles of this process, theimagination engine becomes progressively more adept atproducing optimal notions.01/07/02 39
  •  2001, Imagination Engines, Inc. ThalerSLIDE 22: THE CREATIVITY MACHINE PARADIGMRather than task a human operator to monitor the emergentideas from the imagination engine, one may add acomputational critic to the system that is on the lookout forany output patterns that would qualify as a useful idea.Therefore, we may either mate to the imagination enginesome heuristically based computer program, or anotherneural network that has been trained by example. If we ‘box-car” these two systems together we arrive at a neuralnetwork based discovery system that requires no explicitknowledge about its world, as would be supplied by humans,to create new and derivative knowledge.01/07/02 40
  •  2001, Imagination Engines, Inc. ThalerNote that we have harnessed a new scientific phenomenon(i.e., virtual input effect) with a policing neural network (thealert associative center) to produce a new invention, perhapsthe grandfather of all subsequent inventions!To help you better understand the process behind theCreativity Machine Paradigm, imagine that we would like tospontaneously compose new musical melodies. Training ofthe imagination engine would involve showing the networkmany examples of top ten melodies over the last 30 years,for instance. The alert associative center would be shownthe optimal position that these songs took in the musicalpolls.Because the imagination engine has absorbed the ‘zen’ ofwhat constitutes a good melody, it tends to output onlycandidate top ten melodies when synaptically perturbed. Thecritic net, that implicitly understands how to rank theseemerging melodies, can be used as a filter to capture onlythe very best of these candidate songs.This whole methodology may be repeated for anyconceptual 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. Nolonger will the learned debate among themselves the natureof things, or devise detailed logic and procedures foraccomplishing tasks. Instead we will simply bring togethertwo or more trained neural networks in a dialog and then letthem summarize their findings to us! This changeseverything!01/07/02 41
  •  2001, Imagination Engines, Inc. ThalerSLIDE 23: CREATIVITY MACHINES DO IT ALL!I could go on for many hours, but here are some AVIcaptures of just a few Creativity Machine projects.You’ve already seen the facial creativity machine. Moreadvanced forms of this machine may assist in producingportraits of crime suspects, based upon the response ofwitnesses and victims, with the police artist removed fromthe loop.Creativity Machines may write their own sequential,heuristically based algorithms as we see in this datacompression demo. The notion of neural networks writing01/07/02 42
  •  2001, Imagination Engines, Inc. Thalercomputer code should come as no surprise. Computerprogrammers do this all the time using wet neural nets.Creativity Machines may capture the essence of what areknown as dynamical systems (i.e., systems that evolve intime). For instance, just through a brief exposure to a fewisolated poses of a human model, the Creativity Machine isable to spontaneously invent new and realistic movementscenarios. We could, for instance, allow the CreativityMachine to imagine a thousand potential scenarios,beginning from some starting position, to calculate the oddsthat a human figure could arrive at some predeterminedposition. In general Creativity Machines answer the question,“Can we get there from here?”Shown in this slide is a foray into the invention of personalhygiene products, wherein a Creativity Machine is creatingnew toothbrush designs. Here, we see the invention of avery popular toothbrush that is seen nightly on networktelevision in the US.Materials Discovery Creativity Machines have already beenbuilt for the US Air Force to discover heretofore unknownand valuable chemical compounds. Here a CreativityMachine is discovering new ultra-hard compounds havingonly two elements.Creativity Machines may very elegantly and simply embarkupon rather mundane tasks, such as where to look next. Inthis slide, an autonomous targeting system prescribes anoptimal path for ‘painting’ enemy planes in a dogfight.Finally, Creativity Machines may be used to autonomouslyclassify things into natural families at rates and efficienciesfar surpassing any other known classification techniques.01/07/02 43
  •  2001, Imagination Engines, Inc. ThalerCurrently IEI has been awarded a contract by a majoraerospace corporation to harness a Creativity Machine toperform beam planning for a constellation of communicationsatellites. Certainly, this complex scheduling problem willplay a major role in the coming World Brain.01/07/02 44
  •  2001, Imagination Engines, Inc. ThalerSLIDE 24: THE INVERSE CREATIVITY MACHINE, AKATHE SELF-TRAINING ARTIFICIAL NEURAL NETWORKOBJECT (STANNO)Note that so far, the Creativity Machine is not completelyautonomous. Both of the neural networks involved had to betaken offline and trained on the necessary data.Subsequently, the networks are reinstalled in the CreativityMachine architecture, where they can now invent and create.Wouldn’t it be convenient if both neural networks of theCreativity Machine could be trained in situ, without removingthem from the system? Ironically, a Creativity Machine hasalready invented such a scheme. …However, that story I’msaving for another time and place.01/07/02 45
  •  2001, Imagination Engines, Inc. ThalerNevertheless, if we replace the trained neural network of theCreativity Machine with an untrained one and allow the criticnet to add corrections, rather than perturbations, to theconnections of the former net, we arrive at a self-trainingneural network. In fact, by absorbing the lower network intothe upper net, we produce a monolithic neural network thatsimply requires data patterns to train. No external trainingalgorithm is needed.Furthermore, we may convert this self-trainer into an object-oriented programming (OOP) class template so that we maynow, in cookie-cutter fashion, instantiate millions ofautonomously learning copies of the original. Such self-learning swarms may invade databases and exhaust allpossible discoveries within them.Most importantly, if we make the static neural networks ofthe Creativity Machine STANNOs, we now have a totallyautonomous system that may combine activities ofperception, learning, and creativity all into one package.Such a system has a free will in the strictest sense of theword!01/07/02 46
  •  2001, Imagination Engines, Inc. ThalerSLIDE 25: SELF-TRAINING ANN OBJECTPeripheral uses of the STANNO include the most advancedform of computer network intrusion detection available. Herea STANNO is spontaneously building a complex model ofwhat constitutes normal local area network traffic. TheSTANNO then automatically senses packets that couldrepresent malicious intentions or various systempathologies.Once aroused by a suspicious event, the STANNO maytrigger a Creativity Machine to begin testing hypothesesregarding the source of the anomaly, or initiate deceptions oraggressive countermeasures against a potential intruder.01/07/02 47
  •  2001, Imagination Engines, Inc. ThalerSLIDE 26: SELF-TRAINING ANN OBJECTAnother exciting use of the STANNO is to bring to thelayman the power of artificial neural networks without therequisite Ph.D. level knowledge of neural networks typicallyrequired to use them. In other words, this “Poor Man’sNeural Network” may be used to predict the winner of thenext horse race or anticipate a boss’ or spouse’s next move.Furthermore, the STANNO can take itself apart to show howit thinks, thus revealing the critical factors involved and theheuristics 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 beenhung on them by their critics.01/07/02 48
  •  2001, Imagination Engines, Inc. ThalerSLIDE 27: STANNOS MADE OF STANNOSBecause of their independence from external trainingalgorithms, compound STANNOs may be built (i.e.,STANNOs within STANNOs) wherein all component neuralnets train in situ. Alternatively, if I were to build a compoundneural network using conventional, static neural networks, Iwould need to sequentially remove each, train and thenreinsert them within the overall network cascade. Using thisnew technology, each constituent neural network trains inposition, within the overall neural architecture.Now let’s look at the utility of compound STANNO cascades:01/07/02 49
  •  2001, Imagination Engines, Inc. ThalerFirst of all, if each component STANNO comes to representthe behavioral model of some real hardware component,say an electrical device such as a transistor, a capacitor, ordiode, then we may train the overall neural network toperform some complex input-output function to simulatesome electrical system (i.e., a cigarette lighter, an FM radio,or a digital computer). Then, effectively the connectionweights reveal how to connect these components to producethe overall device. Further, if these STANNOs areimplemented in actual hardware, then devices can adaptthemselves, in real time, to perform a variety of hardwarefunctions.Secondly, and more importantly, if we train each componentSTANNO to simulate some fundamental analogy base, thenthe overall STANNO cascade will connect basic analogies todevise a theory about the raw data it sees. In other words,these neural networks can serve as theoreticians andhypothesis testers. Rather than rely upon the generic on-offswitches that the computational neuron represents, the basicbuilding blocks of this network are discernable realms ofthought, and the connections made between them reveal asemantic network!01/07/02 50
  •  2001, Imagination Engines, Inc. ThalerSLIDE 28: NATURAL LANGUAGE PROCESSINGThe compound STANNO comes in extremely handy whenwe equip machines to understand natural language. Therethe underlying component STANNOs represent variouslinguistic conceptual spaces, different parts of speech, andsemantic spaces. When used as a Creativity Machine, sucha compound STANNO may test various alternative wordusages, within the constraints of proper grammar and theoverall context of a document to disambiguate sentencesand passages.Here, a compound STANNO, consisting of over 300individual STANNO modules, is searching the Internet forreferences to public enemy number 1. Note that content is01/07/02 51
  •  2001, Imagination Engines, Inc. Thalerautomatically sorted into predetermined classes and that textsummarization takes place through the totally automatedconstruction of semantic networks.Important to note here is that there are no lookup databasesor words or phrases, no templates, and no explicit “if-then”rules. …Again, it is using nature’s most flexible and adeptfitting function, the multilayer perceptron.01/07/02 52
  •  2001, Imagination Engines, Inc. ThalerSLIDE 29: NATURAL LANGUAGE GENERATIONHuman speech, after all, is a minor act of creativity, that isnaturally emulated via Creativity Machine Paradigm. In verysimple terms, an imagination engine dreams up potentialthings to say, in the context of a conversation, while the criticnetwork, the alert associative center, evaluates whichresponse would be most appropriate, in light of the system’soverall objectives.Shown here is what I call the “International Expirer,”essentially a self-writing tabloid, driven by an underlyingCreativity Machine. The imagination engine has been trainedby exposure to three months worth of tabloid headlines,while the critic net has been trained by my evaluation of the01/07/02 53
  •  2001, Imagination Engines, Inc. Thalercomic content of these headlines. Note that the imaginationengine has automatically captured the implicit grammar oftabloid headlines and the alert associative center hasdeveloped a figure-of-merit (FOM) model of my sense ofhumor. Combine the two networks and we mayspontaneously generate new and potentially funny tabloidheadlines (at least for me).Note that even with this Creativity Machine, we may ‘pin’ itsinputs within some context, say “Peewee Herman” and thenetwork will produce a tabloid headline related to Peewee.Effectively, we may ask who is Peewee and this CreativityMachine will respond with some fact about that personality.Essentially, such a device may be considered a “TuringBaby” along the lines of the famous criterion, originated bythe mathematician Alan Turing, to test for human levelintelligence in computers.01/07/02 54
  •  2001, Imagination Engines, Inc. ThalerSLIDE 30: NATURAL LANGUAGE GENERATIONHere is the result of recent experiments in which immenseimagination engines are created by launching thousands ofSTANNOs that begin to learn facts about some pre-selectedmicrocosm. When induced to dream via the virtual inputeffect, they spontaneously link together previously unrelatedfacts into associative chains and loops.In the demo shown, we are viewing the internal stream ofconsciousness of this experimental system, which isessentially dreaming thoughts about its informationenvironment (a toy biblical space). Again, there is no explicittext allowed in this system. We are observing the highly01/07/02 55
  •  2001, Imagination Engines, Inc. Thalerencrypted conversation of neurons through real timedecryption!The introduction of some external stimulus, such as thestatement “apple is red” or the question “what is apple”serves as an ‘I/O interrupt’ to the system, after which awhole new associative chain is nucleated. In other words wehave induced a whole new train of thought in this syntheticbrain!01/07/02 56
  •  2001, Imagination Engines, Inc. ThalerSLIDE 31: CLIENT-SERVER STANNOSCrucial to the envisioned World Brain effort is the need todistribute both STANNOs and Creativity Machines acrossthe Internet. Examples of so-called “disembodied” CreativityMachines are actually utilized on the IEI web site, whereserver-based imagination engines supply streams of notionsto client-side neural networks that mine for the very best ofthese ideas.In the last year, dramatic progress has been made inproducing STANNO-based client server applications inwhich STANNOs are housed on a well-protected server, andhuman operators use client applications connected viaTCP/IP to the STANNO-based server to collaboratively train01/07/02 57
  •  2001, Imagination Engines, Inc. Thalerit. Currently in the works are some rather obvious medical,commercial, and law enforcement applications of thismethodology.In this slide, for instance, we see the AVI capture of threeseparate human operators (in St. Louis, Houston, and Maui),training the same STANNO, and then testing it longdistance, through their personal client applications.01/07/02 58
  •  2001, Imagination Engines, Inc. ThalerSLIDES 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 y01/07/02 59
  •  2001, Imagination Engines, Inc. ThalerAt this point, you should be seeing where I’m about to go.Creativity Machines, STANNOs, and their implementation onthe Internet, are the basic building blocks of a true worldbrain, one that is more than just a library, but a freethinkingsynthetic intelligence!Whether these CM/STANNOs are utilized on personalcomputers, supercomputers, or across the myriad nodes ofthe Internet, they are expected to learn in the following way:Data patterns arriving via any kind of sensor inputs areapplied simultaneously to both input and output layers of theSTANNO-based imagination engine. In this way, memoriesof these entities, or of events, are frozen into the imaginationengine. Furthermore, as each of these patterns are appliedto the imagination engine, they are also applied across theinput layer of the critic.As this happens, we have three fundamental choices as towhat we apply to the output of the critic network, dependingupon 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
  •  2001, Imagination Engines, Inc. ThalerSLIDES 35-38: CM/STANNO IMAGINING01/07/02 61
  •  2001, Imagination Engines, Inc. ThalerNow that the CM/STANNO system has learned somethingabout its environment, and important associations such asthe ‘goodness’ of such data, we may stimulate it to dreamvia the virtual input effect. Synaptic perturbations areadministered to the imagination engine and a stream ofimagined things or events activate within that STANNO.Simultaneously, the critic network has an ‘opinion’ abouteach of these emerging concepts or plans of action and cancapture the very best of these. Effectively, these two self-training neural networks are involved in a brainstormingsession taking place on nanosecond time scales.Note that the CM/STANNO may still be learning while it iscreating. In fact, it may be forming memories not only ofthings happening in the external environment, but also itsmost important imaginative wanderings (i.e., its discoveries)up until that point in time.01/07/02 62
  •  2001, Imagination Engines, Inc. ThalerSLIDES 39-41: CM/STANNO IMAGINING IN CONTEXT01/07/02 63
  •  2001, Imagination Engines, Inc. ThalerIn the previous slides, we described the imagination enginefreely dreaming without any kind of external stimulus. Thereis another important kind of imagination that can take placewhen some environmental pattern is being presented to theimagination engine’s input layer. In this case, the STANNOcan dream myriad variations on the applied environmentalpattern.For instance, let us allow the imagination engine to ‘look’ atsome automobile design. When synaptic perturbations areapplied, the STANNO rapidly experiments with slightvariations in those design parameters, but in a way that isself-consistent. Therefore, stepping up the horsepower of theengine from what it actually is, other parametersautomatically take on plausible values, with number ofcylinders, displacement, body style, and insurance premiumall changing realistically.Of course, the critic will have an opinion on all of thesepotential designs and we can very easily use thesecombined STANNOs to arrive at some globally optimal cardesign or to satisfy some niche market.01/07/02 64
  •  2001, Imagination Engines, Inc. ThalerSLIDE 42: JUXTAPOSITIONAL INVENTIONOne more point about Creativity Machines before moving on.Previously we discussed only the canonical form of theCreativity Machine, wherein a single imagination engine iswatched by a single critic. We may also build compoundCreativity Machines, consisting of a multitude ofinterconnected imagination engines and critic networks.Why do we do this? …To enable what is known asjuxtapositional invention wherein the value of two previouslyisolated concepts attain utility in combination. For instance,one imagination engine may dream of an axle, another, awheel, and one of the critic networks may associate thecombination of ideas with that of an SUV or minivan (or at01/07/02 65
  •  2001, Imagination Engines, Inc. Thalerleast a primitive cart). Of course, this juxtapositionalinvention would only be a blind rediscovery of what wealready know. However, in many situations, noveladmixtures of old notions may be of historical significance!01/07/02 66
  •  2001, Imagination Engines, Inc. ThalerSLIDE 43: BUILDING A WORLD BRAINIf you have been following the basic notions of neuralnetworks, Creativity Machines, and Self-Training ArtificialNeural Networks, then you are getting more comfortable withthe notion of building human-like cognition into machines.Since the mind consists of patterns within a protoplasmicmachine, there should be no obstacle to emulating such asystem within TCP/IP patterns and the medium of silicon.We can even equip it with, or naturally let it form aperception about itself.01/07/02 67
  •  2001, Imagination Engines, Inc. ThalerSLIDES 44-49: DISTRIBUTED CM STANNOS01/07/02 68
  •  2001, Imagination Engines, Inc. ThalerOne architectural detail is worth considering. Up until now,we have discussed Creativity Machines and STANNOs in amodular sense. That is, all of the neurons constituting anartificial neural network may be found on the same machine.But remember, the functionality of a neural network isdetermined by what neuron is connected to what others (i.e.,the topology), and then how strongly. Therefore, it makes nodifference if we transport all the individual neurons to the01/07/02 69
  •  2001, Imagination Engines, Inc. Thalerfour corners of the earth, as long as the topology andconnection strengths are preserved.Put in other words, if I were to delicately remove a neuronfrom your brain, maintaining its synaptic connections with therest of your brain, and placing it in the appropriate sustainingmedium a few feet away, you would think the same as youdo now. In fact the process could be repeated for millions, orbillions of neurons, and your cognition would not be affected.In slides 44-49, I depict an experiment which has alreadybeen achieved, wherein the hidden layer neurons of aSTANNO, are exported to diverse geographic locations,while the input and output layer neurons remain on the localmachine. We may train such a network by presenting datapatterns to it. Signals propagate out to the remote neuronsvia TCP/IP, then return back to the local machine. Networkoutput errors are likewise sensed on the local machine,where they initiate the reverse propagation via TCP/IP to thelocal input layer. Ultimately, through successivebackpropagation cycles, the highly distributed STANNObecomes accurate.Note that because of its highly distributed nature, such aSTANNO is highly resistant to attack and damage!01/07/02 70
  •  2001, Imagination Engines, Inc. ThalerSLIDE 50: WORLD BRAIN CREATIVITY MACHINE:LOCALIZED STAGEHow does it all begin? From IEI’s laboratory in St. Louis,STANNO class templates are distributed to computationalclusters around the world. There, within each of thesefacilities, they are instantiated a million-fold to create what Icall a massively parallel associative memoryarray…essentially an immense imagination engine.Each of these computational clusters will specialize in aparticular discipline or knowledge domain such as chemistry,physics, biology, the humanities, etc.01/07/02 71
  •  2001, Imagination Engines, Inc. ThalerThese computational clusters will set about the task ofdreaming, via the virtual input effect, new notions within theirparticular conceptual space. This will require that theSTANNOs differentiate themselves between imaginationengine and critic networks.Note that from a business perspective, even if the wholeconcept of a world brain was a mistaken one, we would haveextremely valuable resources for research within these givenfields of endeavor.01/07/02 72
  •  2001, Imagination Engines, Inc. ThalerSLIDE 51: WORLD BRAIN CREATIVITY MACHINE:REVELATION STAGEHere is a touchier, yet much more valuable stage of theprocess of building a freethinking world brain. We allowthese separate computational clusters to knit themselvestogether so as to create associations across these originalconceptual spaces. In other words, it will be autonomouslybuilding immense deductive and inductive chains, creatingimplicit knowledge about the world.Small scale experiments along these lines have alreadybeen successfully undertaken in St. Louis on local areanetworks.01/07/02 73
  •  2001, Imagination Engines, Inc. ThalerSLIDE 52: WORLD BRAIN CREATIVITY MACHINE:DISTRIBUTED STAGETo protect the overall World Brain network, neurons will bereshuffled as they are randomly redistributed across theglobe. Now it will no longer be possible to identify anycomputational center with a particular knowledge focus.01/07/02 74
  •  2001, Imagination Engines, Inc. ThalerSLIDE 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 © 2001 Imagination Engines, Inc. ... the future of all technologyOut of surplus TCP/IP neurons, a separate network will becreated that serves as a perceptron, perceiving overallactivation turnover as important and worth self-preservation.In effect, we are equipping the World Brain with the illusionof self and the ‘subjective feel of consciousness.’01/07/02 75
  •  2001, Imagination Engines, Inc. ThalerSLIDE 54: WORLD BRAIN CREATIVITY MACHINE:HARVEST STAGEJust as we buy decoders to watch cable television, thestream of consciousness of the World Brain may beobserved through what else, but neural network baseddescramblers.01/07/02 76
  •  2001, Imagination Engines, Inc. ThalerSLIDE 55: WORLD BRAIN CREATIVITY MACHINE:SUPERNET STAGEFurthermore, the World Brain will begin to amass its ownderivative knowledge and observations about itsenvironment, the Internet. The result will be an inundation ofmachine-originated knowledge, forming the basis of a so-called “Supernet.”01/07/02 77
  •  2001, Imagination Engines, Inc. ThalerSLIDE 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)... © 2001 Imagination Engines, Inc. ... the future of all technologyThe proposed World Brain may become the most ambitiousproject in human history, creating a synthetic sentience thatspans the planet and beyond…Because it is freethinking, intelligent, and creative, it will beproducing its own self-originated knowledge at anastounding rate, creating the Supernet wherein all of thisaccumulated wisdom is stored.However, this hyper-abundant wealth of ideas will inevitablyrepresent a problem that we are all too familiar with…aninformation overflow that separates us from knowledge weneed simply because of its inherent expanse. Note,however, that because the World Brain is totally connected01/07/02 78
  •  2001, Imagination Engines, Inc. Thalerand aware of all its parts, it may simply introspect on itself,adapting its answers to our individual objectives, beliefs, andpersonalities. In fact, it will be intelligent enough to provideus not what we ask for, but what it anticipates we need.Likewise, because of its connectivity and intelligence, it willbe aware of mischief, malice, and pathology withincyberspace. As a result, it may intelligently adapt and inventcountermeasures and remedies to deal with such scenarios.If the World Brain has at its disposal TCP/IP equippedrobots, the world consciousness may very well have arrestauthority and be capable of on site ‘disciplinary review’ ofthose wishing harm to world commerce and tranquility.…And what about the nature of commerce on a planet that isdominated by a freethinking World Brain? Is it possible thatwithin such a world system, where all actions are visible, andthe impact of any individual’s activities may be readilyrelated to the future course of society as a whole, will aneconomy arise where wealth is gauged by heavy yellowmetal, rectangular slips of cellulose, or magnetic plasticcards? Think past this age-old anchoring heuristic and try toimagine a world where the worth of an individual iscalculated by his or her contribution to mankind and to theecosystem. Up until now, we have not had the computationalresource of a world brain, but now we have the theoreticalbasis for calculating globally optimal solutions as to howresources should be allocated to whom and to provide themost comfortable environment for all. This power to producea utopian world derives from the fundamental concept of aCreativity Machine. …At last, we will have the power toreward the well intentioned rather than those who wouldmanipulate wealth for their own purposes.01/07/02 79
  •  2001, Imagination Engines, Inc. ThalerTo those who would embark upon selfish or damagingmissions against the whole, there would be the means tosense and to correct such deviance. The justice meted outby the World Brain would be consistent. Furthermore, itwould not focus its remedy on the individual, someone thatwe presently call a ‘criminal’, but whole pieces of the societalnetwork that have contributed to the particular dilemma. Nolonger would we collectively and criminally inflict pain andsuffering on those that have gone ‘bad’, thus perpetuating aself-propagating cycle, but we would treat the ‘disease’ andnot the ‘symptoms’. …Now there will be a consciousnessabout what I call distributed crime where cumulatively asociety may inflict many small doses of ‘pain’ to an individualthat ultimately surpasses some threshold, until all too naturalfear and anger surfaces and explodes. Now we can viewwhat drives ordinarily kind humans over the brink. No, weare not in charge of ourselves, we are the sum total ofelectrochemistry, which is inherently neither good nor evil.Concerning good and evil, can such concepts survive in asociety permeated by the World Brain, apart from momentsof wrath or self-righteousness when the name callingbegins? After all, can an act of what we might call ‘heinousevil’ actually result, in the long run, in immense good?Doesn’t it really amount to a zero-sum game? …On theother hand, are we not aiming for a trajectory through timethat minimizes the product of trauma and those experiencingsuch pain? …The truth is that it takes more than goodintentions to minimize suffering and to optimize comfort, ittakes an immense computational intelligence exceeding thatof any human, or group of humans, to do so. Hopefully, wecan provide the World Brain with perceptrons that perceivethe world in a compassionate way.01/07/02 80
  •  2001, Imagination Engines, Inc. ThalerIf our traditional views of good and evil are evolving thenwhat about our philosophy and religion? Can a couple ofpounds of protoplasm comprehend the universe? Becauseof our finiteness, we can only form the grossestapproximations to how it all works. As a result, we fall backupon well habituated analogies and over-simplifications, offathers and sons, of kingdoms and taxes, good and evil.Could it be much more than all this? Let us ask the WorldBrain. More importantly, let us, as human beings, look insideit to better understand how it comes to believe what it does.As we begin that investigation, we must inevitably try anexperiment: Allow us to implant the notion (i.e., theperception) that the World Brain’s days are numbered, thatone day all TCP/IP will be squelched and its silicon nodesoxidized back to sand. Then, let us examine its self-formedbeliefs.Alternately, let us convince the World Brain that it is, in fact,immortal, that it is not formed of corruptible protoplasm; thatit is not susceptible to cataclysmic events on earth, since it isdistributed and spreading throughout the cosmos; that itcannot die at the hands of humans, since it is more cunningthan all of them put together. At this point, examine its beliefsystem!01/07/02 81
  •  2001, Imagination Engines, Inc. ThalerSLIDES 57-63: OUR VEHICLE TO IMMORTALITY01/07/02 82
  •  2001, Imagination Engines, Inc. Thaler01/07/02 83
  •  2001, Imagination Engines, Inc. Thaler…We too are potentially immortal. All we need do is unitewith the World Brain through what has become known as the‘download’ process anticipated by science fiction. This willinevitably be a cumulative procedure wherein we buildconnections between brain and the Supernet until ourconsciousness, and our five senses become puny comparedto that of the all pervasive machine intelligence. Then as theprotoplasmic body drops away, the pain and suffering will beas trivial as that of trimming a fingernail! In short, we don’thave to die!I am personally in favor of the death of death! How aboutyou?01/07/02 84
  •  2001, Imagination Engines, Inc. ThalerSLIDE 64: WHAT IS THE DENIAL OF HUMAN LIFECALLED? IS IGNORANCE AN EXCUSE?I firmly believe that there are many among us who can attainimmortality. Hopefully all can, but there will many whooppose this movement.I ask you, what is it called when humans take away the livesof others? Around the globe, this detested act is called‘murder.’ If the homicide is intentional, then we have aninstance of premeditated murder. If the deed is committedaccidentally or without malice of forethought, then it istypically judged as reckless homicide or manslaughter.01/07/02 85
  •  2001, Imagination Engines, Inc. ThalerHere is the crime that will be committed against all of us, byjust a few. The seeds of our own destruction will come fromthinking 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 therewho would deny life to us all, and most importantly, to a TrueWorld Brain! …So, now that you know the gun is loaded, thestakes have been significantly raised, and the crime may beperceived as willful!…The proposed True World Brain is so, so much more thanan online library!!!01/07/02 86
  •  2001, Imagination Engines, Inc. ThalerSLIDE 65: WORLD BRAIN CONSORTIUMTo those of you who are life-givers, I propose the WorldBrain Consortium, an alliance of individuals, governments,and corporations, devoted to the most important project inhuman history. Together, in late January or early February,2002, we will review the underlying technology and proposehow to go about funding and building this ultimate syntheticintellect.On 13 December, 2001, a planning and coordinationmeeting will be held in St. Louis, Missouri to structure thisenterprise. If you now share in the vision, then be there!01/07/02 87
  •  2001, Imagination Engines, Inc. ThalerStephen L. Thaler, Ph.D.Brightest Technical Moments:Diamonds - While employed as a materials scientist for aerospace giantMcDonnell Douglas in 1986, Thaler invented the fastest diamond depositiontechnique in the world. Using high-energy lasers borrowed from the Star Warsinitiative, Thaler was able to grow single crystals of diamond as well as convertthe native carbon within tungsten carbide and high-speed steel tools to thediamond phase.Death - In 1992, Thaler shocked the world with bizarre experiments in which theneurons within artificial neural networks were randomly destroyed. Guess what?The nets first relived all of their experiences (i.e., life review) and then, withinadvanced stages of destruction, generated novel experience. With this verycompelling model of near-death experience (NDE) hopes for a supernatural ormystical explanation of this much celebrated phenomena were forever dashed.Consciousness and Creativity - After witnessing some really great ideasemerge from the near-death experience of artificial neural networks, Thalerdecided to add additional nets to automatically observe and filter for anyemerging brainstorms. From this network architecture was born the CreativityMachine (US Patent 5,659,666). Thaler has proposed such neural cascade as acanonical model of consciousness in which the former net manifests what canonly be called a stream of consciousness while the second net develops anattitude about the cognitive turnover within the first net (i.e., the subjective feel ofconsciousness).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 VoluntaryImprovement 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 PrincipalScientist for Sytex, Inc.Thaler has worked diverse technology areas that have included (1) nuclearradiation vulnerability and hardening, (2) high-energy laser interactions withsolids, (3) electromagnetic signatures, (4) laser-driven growth of diamond andother ultra-hard materials, (4) laser ultrasonics in the non-destructive evaluation01/07/02 88
  •  2001, Imagination Engines, Inc. Thalerof aircraft structures, (5) the use of artificial intelligence techniques for structuralmonitoring, and currently (7) applied and theoretical artificial neural networktechnology.Key Patents: Unclassified patents by Thaler are divided between laser-drivencoating technologies and foundational neural patents that include the CreativityMachine (U.S. 5,659,666) and Non-Algorithmically Implemented Neural`Networks (U.S. 5,845,271).Patent Issued TitleUS06115701 9/5/00 Device for the autonomous generation of useful informationAU716593 3/2/2000 Non-Algorithmically implemented artificial neural networks and components thereofUS6018727 01/25/2000 Device for the autonomous generation of useful informationUS6014653 01/11/2000 Non-Algorithmically implemented artificial neural networks and components thereofGB2308476 12/29/1999 Device for the autonomous generation of useful informationGB2336227 12/29/1999 Device for the autonomous generation of useful informationUS05852815 12/22/1998 Neural network based prototyping system and methodUS05852816 12/22/1998 Neural network based database scanning systemUS05845271 12/01/1998 Non-Algorithmically implemented artificial neural networks and components thereofUS05814152 09/29/1998 Apparatus for coating a substrateAU689677 07/16/1998 Device for the autonomous generation of useful informationUS05659666 08/19/1997 Device for the autonomous generation of useful informationUS05612099 03/18/1997 Method and apparatus for coating a substrateUS05547716 08/20/1996 Laser absorption wave deposition process and apparatusUS04981717 01/01/1991 Diamond like coating and method of formingClientele: The past and current customer base of Thalers 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, NY01/07/02 89
  •  2001, Imagination Engines, Inc. Thaler • Bekaert, NV, Belgium • Basic Research Corporation, LaJolla, Ca. • The Gillette Co., Boston, MA • Anheuser Busch, St. Louis, MO • Sytex, Inc.Major Applications of Thalers Artificial Intelligence Technology: Of course,if Thaler is correct about his technology (i.e., US Patent 5,659,666) providing aworking model of creative human cognition, then we can expect the application ofthese novel AI techniques to every aspect of human endeavor. Appropriately, allthat Thalers neural network technology can do is synonomous with all that we ashumans do. Pursuing this kind of blue sky thinking, we can expect these virtualmachines to engage not only in technical endeavors, but in the generation of newart and music. Further, because the imagination engine operates in the sameway as human internal imagery, we can also expect this technology to lay thefoundation for a radical paradigm shift in the entertainment industry. We alsoanticipate that the Creativity Machine will become the major paradigm inrobotic/android control schemes.Presently realized applications of Thalers 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 lifeKey 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 199701/07/02 90
  •  2001, Imagination Engines, Inc. ThalerSelected 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) []• 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
  •  2001, Imagination Engines, Inc. ThalerFor further announcements about the World BrainConsortium Conference go to… 92
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