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Artificial Intelligence & Cosmic Consciousness

May 07, 2015




This conference volume paper spells out the fundamental mechanism behind consciousness in lay terms and examines the implications upon our cosmology, beliefs, and social institutions.

  • 1.Thalamocortical Algorithms in Space! TheBuilding of Conscious Machines and theLessons Thereofby Stephen L. Thaler, PhDExecutive SummaryThe escalating tension between religious dogmas is not only a sig-nificant source of discord and conflict in the world, but also a major dis-traction from more worthy human efforts in areas such as life extension,the equitable distribution of wealth, and more accurate systems of justice.A radically new form of synthetic intelligence not only forms the one andonly basis of truly brilliant and conscious machines that can address a hostof globally critical issues, but also, by its very nature, sheds light upon theage-old questions that have contributed to the growing spiritual schismthat more than ever impedes human progress and survival. Ironically, theway such conscious machines influence our future is not through vasttechnological achievements, but by what their attainment teaches us aboutourselves.About the Author Stephen L. Thaler, PhD, carried out his thesis research in bothn uclear and laser physics at the University of MissouriColumbia. Early inhis career he grew crystalline laser and modulator materials for HughesAircraft and UCLA Engineering. He has worked for Mallinckrodt Nuclearin the area of nuclear chemistry, as well as McDonnell Douglas investigat-ing uclear and laser interactions with solids. He holds over 60 patents andnstatutory patent registrations in diverse areas, ranging from laser warfare,stealth technology, high speed diamond growth, and advanced artificialintelligence. He has also been active in areas related to information warfarewhile employed in the Maryland area. Currently he is President and CEO of Imagination Engines, Inc., as 2010 World Future Society All rights reserved 7910 WoodmontAvenue, Suite 450, Bethesda, Maryland 20814 U.S.A. www.wfs.org

2. well as the founder of the non-profit In Its Image, Inc. Both of these orga-nizations are built around his foundational U.S. and international patentsthat teach the use of noise stimulated artificial neural networks and self-forming synthetic brain pathways to carry out autonomous discovery, in-vention, and improvisational control. The former company is dedicated tocommercial and military applications of this radically new form of artifi-cial intelligence. The latter is committed to exploring the philosophicaland spiritual repercussions thereof. 3. Thalamocortical AlgorithmsIn Space!The Building of Conscious Machines and the Lessons ThereofStephen L. Thaler, PhDIntroductionMany have suggested that somehow machine intelligence isabout to become superhuman. Common to such thinking is that, asmachines become progressively faster and more complex, the under-lying artificial intelligence (AI) will spontaneously become self-awareand conscious, thereafter becoming either our savior or bane.There are many flaws in such speculation, most of which I wontbegin to touch upon here, but the foremost misconception is thatmainstream AI will form the foundation of such godlike systems.Those falling prey to such a fallacy are sorely disappointed when com-puter scientists admit that relatively slow human beings actually gen-erate such AI in the first place and that once laboriously created, suchalgorithms have limited ability to produce results outside their orig-inal programming. Although such systems may be more logical andcomputationally swifter than humans, they cannot claim creative in-tuition, self-awareness, or anticipatory fear of their own demise, theStephen L. Thaler, PhD, is president and CEO of Imagination Engines Inc. andfounder of the nonprofit In Its Image Inc. E-mail sthaler@imagination-engines.com. 4. 410 Strategies and Technologies for a Sustainable Futurehallmarks of human consciousness. Ironically, nature has already shown us how to build consciousmachine intelligencethe brainbut the primary obstacle to creat-ing it is not a technical, but a philosophical barrier. After all, whenneuroscientists peer into the brain, they see only two cognition-gen-erating structures: neurons, essentially on-off switches, and their syn-aptic interconnections. Then introspection, even within this scientif-ically disciplined culture, leads to nagging doubts that such relativelysimple physical mechanisms can lead to sublime thoughts and hu-man feelings. However, toggling their focus back to a more objectivemode, they observe only physical (i.e., electromagnetic, acoustic, andpressure) inputs to the brain through sensory channels, clusters ofneurons internal to the brain responding to these patterns, and sim-ilar internal neuronal activity taking place even in the complete ab-sence of such external stimuli. Surrendering to such inner tension,some scientists ultimately declare the riddle of consciousness unsolv-able (Chalmers 1995), while others undergo an intrepid philosophi-cal conversion, altogether abandoning subjective introspection anddrawing upon a palette of just neurons and connections to paint aself-consistent and demystified picture of cognition. I myself turned toward the latter reductionist theory of mindmore than 30 years ago. Part of that personal transition was drivenby my growing revolt against those feeling that the nature of con-sciousness is beyond the grasp of science. The remaining contribut-ing factor to this new outlook was my growing interest in the newlyemerging field of artificial neural networks. In the following, I willmention just enough about the latter motivation, neural networks, toserve as mental scaffolding for the uninitiated reader to better relishthe concept of mind emerging from my own private rebellion. There-after, it is a personal matter as to whether similar doubts about thehuman brains mystical and unexplainable self-perception are allowedto persist. 5. Thaler: Thalamocortical Algorithms in Space!411Artificial Neural NetworksTraditional artificial neural networks (ANNs) emulate the fun-damental mechanism by which the brain perceives, learns, and formsmemories. The major paradigm shift ANNs bring to the world of ma-chine intelligence is a newfound independence from human beings,whose traditional role in AI has been to laboriously embed theirthoughts within highly glorified scripts called computer programs. Insharp contrast, synthetic neural nets require only exemplary vecto-rial inputs (i.e., the human senses) and exemplary vectorial outputs(i.e., resultant human thoughts and actions). Given that there is someunderlying and intrinsic relationship between these complex inputand output spaces, ANNs interconnect their simple onoff switchesto capture memories of, and relationships between, things and activ-ities within these two respective data environments. In effect, intel-ligence automatically grows within numerical connection strengthsbetween these very unintelligent switches called neurons, without anyhuman assistance.But ANNs, in and of themselves, contribute only necessary, butnot the sufficient capabilities to attain brainlike, cognitive, and con-scious function in machines. Essentially, the world models absorbedby these systems must in some way be altered or set into motion toproduce ideas that depart from such rote knowledge. Furthermore,these cybernetic creations must possess all of the sublime and pro-found thoughts that minds typically have of themselves, the very qual-ities required of machine intelligence in order to truly qualify as con-scious.To better appreciate how such conscious machines can be built,and their impact upon the future, consider how two fundamental ar-tificial neural network components, called perceptrons and imagitrons,may be simply and elegantly combined into what has been patentedas, and arguably is, the first conscious machine intelligence, the Cre-ativity Machine paradigm (Thaler 1997A). I first discuss the older 6. 412 Strategies and Technologies for a Sustainable Futureand more established principle, the perceptron.Perceptrons In effect, ANNs are pattern associators, cumulatively learninghow to generate an output vector, or association, when presented withsome raw sensory input vector. Studied as early as 1943 (Rosenblatt1958), such systems, called perceptrons, were first recruited by com-putational psychologists to describe how the brain forms opinionsabout the world. The most salient feature of these researchers mes-sage was that the subjective opinion formation process going on withinthe brain is simply the learned mapping between the physical effectof raw sensory input arriving from the environment and associatedmemories (Figure 1). If for instance, the flavor of chocolate is pleas-ant, the stimulation pattern of the four basic taste bud groupssweet,sour, salty, and bitteris automatically associated with patterns onetypically considers pleasant, the taste of something else that is agree-able, or for that matter any and all enjoyable memories. Similarly, if one is not a fan of this sweet, then the pattern association isFigure 1: Perceptronswith less savory experience.Perceptrons are neural network In the brain, the process ismodules that map raw sensoryinput patterns to associated more complicated, in that opin-patterns known as memories. In ion formation is not the result ofessence, the output patternrepresents an opinion about thea single monolithic perceptron,input pattern originating in the but a vast collection of individ-environment. In neurobiology, theassociated pattern is often a string ual neural nets that produce notof perceptron-based associations,a single association, but a wholeas depicted below in Figure 2. chain of them (Figure 2). Thus to one who relishes the taste of chocolate, a sequence of pleas- ant thoughts emerge, typically terminating, like a snake swal- lowing itself, until such loops 7. Thaler: Thalamocortical Algorithms in Space!413are preempted by newly arriving and distractive environmental in-put patterns, or reformed into newer topologies through the trigger-ing of specialized cells conn