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Computational Framework
Abstract
In the early fabrication of fibers (mid-1960s to early 1970s), impurities by absorption was the major reason for high attenuation of the optical signal. An extremely high level of purity against some elements is necessary. For example, two parts per billion (ppb) of cobalt can induce a 10 dB/km loss; 20 ppb of nickel, iron or chromium, or 50 ppb of copper, or even 100 ppb of manganese or vanadium can each induce 10 dB/km loss through the fiber. In 1970, the quality control of the fiber manufacture process was poor enough to induce a 20 dB/km fiber. In 1972, the loss was reduced to about 4 dB/km and fiber for communication became scientifically and economically feasible for longer distance trunk applications.
... Elements of knowledge (shortened as kels 1 to represent knowledge el nowledge can be a computational entity that can be processed as numerical, logical, and/or informational entities in computers and networks [1,2]. The command languages and processing architectures for knowledge systems become progressively more intricate, elaborate, and structured [see Chapters 5,6, and 7 in Reference 2]. ...
... The role of the two individuals is reversible and the centerline of symmetry runs horizontally through the computational model. Further elaboration of this diagram results in a more comprehensive computer model presented in Figure 2. [7,2]. Mathematical models of human interactions are presented by Roman, et al, [8,9] and Pen [10] present the symmetric interactive processes. ...
... It was conceived and presented [6] as far back as 1993 and 1994. Knowledge science as a scientific discipline was presented in 2006 [11] and further expounded in 2009 [2] based on the theory of knowledge. The convergence of knowledge science, computational programming, and its machine implementation as they can be implemented in the Science of Medicine was presented in 2013 [12]. ...
In this paper, three virtual but dimensioned entities are used to contain knowledge; while it is by itself abstract. Knowledge resides in knowledge-banks of computers and the Internet. More importantly, knowledge resides in all living species. The main emphasis is on the human species that construct their personalized knowledge structures and banks that they deploy to resolve their personal Needs. Such needs drive behavior and adaptive. Both these human characteristics are alive and get influence by human interaction. These dimensions have human (thought), scientific and physical (energy and time) orientations. It becomes feasible to build a hyperspace for knowledge and confine it in the three dimensions of thought (anchored in the personality of an individual), energy, and time (both anchored in both physiological and physical spaces). We present the personality aspects based on the human needs that drive the human being (a noun object, n) to perform actions (one or more verb functions v) in intelligent steps (convolutions (⁎) between n’s and v’s ) to gratify the needs. Needs are inherent in human personality to maintain life.
... Knowledge science methodology that could be implemented by novel computer architectures was conceived and presented [4] as far back as 1993 and 1994. Knowledge science as a scientific discipline was presented in 2006 [5] and further expounded in 2009 [6] based on the theory of knowledge [7]. The convergence of knowledge science, computational programming and its machine implementation as they can be implemented in the Science of Medicine was presented in 2013 [8]. ...
... The attributes and bondage of the KCOs are altered by the knowledge systems much as the numerical values and their dependencies are altered by the CPUs and programs of computers. The concepts from references [5 and 6] are explored and expanded further in Reference [7] and correlated to the basic human needs that offer the raw motivational energy to drive the actions that manipulate the KCOs. These altered stated of KCOs gratify the human needs that prompted the action. ...
... Segment Two uses a different set specialized of skills for STEPS IV through VIII (Figure 4) (vi) This rather intricate process (encompassing STEPS IV, and V) involves the convolution (that includes judgment on the part of the doctor) to generate kels (7,(1)(2)(3)(4)(1)(2)(3)(4)(5) from the derived kels (5,(1)(2)(3)(4) and the doctors (treatment) skill sets kels (6,(1)(2)(3)(4)(5). (Note that these skill sets are different from those used in Figure 3 and can depend on different aspects of the doctors training and background. ...
In first half this paper, we identify, classify and formalize the interactive steps between a patient and a human doctor as a platform for the robotic doctor to take over the human functions of the real doctor and be able to perform the necessary steps to train a robotic doctor. The initial interactive steps between patients and human doctors have successfully stylized and reduced to computer-based artificial modules as far back as 1976 and 1984 by Shortcliffe, et al, [1]. In this paper, we have greatly enhanced and expounded these early rudimentary AI concepts to nine basic steps elaborated in the second section in the body of the paper. In the second half of this paper, we generalize the generalized doctor-patient interactive seven step methodology to the solution of need-based problems of individuals, social entities, corporations, governments and nations. The number of steps and the operating systems and the generic interactive modules need customizations for the specific environments and cultural settings. However, these computer systems are expected to solve such generic problems and provide one or more solutions with one or more specific reason for each deduced procedural step based on practical constraints such as minimization of cost, probability of success, highest benefit to cost ratio, etc. Such an approach reduces time to solve the problem and the number of iteration by human beings to solve large open ended social cultural and environmental problems. The machines are expected to have access to Internet knowledge bases, be able to perform matrix manipulation function to correlate the numerous "objects" with their "functions" in order to generate a realistic and practical solution to social and cultural problems.
... Knowledge science methodology that could be implemented by novel computer architectures was conceived and presented [4] as far back as 1993 and 1994. Knowledge science as a scientific discipline was presented in 2006 [5] and further expounded in 2009 [6] based on the theory of knowledge [7]. The convergence of knowledge science, computational programming and its machine implementation as they can be implemented in the Science of Medicine was presented in 2013 [8]. ...
... The attributes and bondage of the KCOs are altered by the knowledge systems much as the numerical values and their dependencies are altered by the CPUs and programs of computers. The concepts from references [5 and 6] are explored and expanded further in Reference [7] and correlated to the basic human needs that offer the raw motivational energy to drive the actions that manipulate the KCOs. These altered stated of KCOs gratify the human needs that prompted the action. ...
... Segment Two uses a different set specialized of skills for STEPS IV through VIII (Figure 4) (vi) This rather intricate process (encompassing STEPS IV, and V) involves the convolution (that includes judgment on the part of the doctor) to generate kels (7,(1)(2)(3)(4)(1)(2)(3)(4)(5) from the derived kels (5,(1)(2)(3)(4) and the doctors (treatment) skill sets kels (6,(1)(2)(3)(4)(5). (Note that these skill sets are different from those used in Figure 3 and can depend on different aspects of the doctors training and background. ...
In first half this paper, we identify, classify and formalize the interactive steps between a patient and a human doctor as a platform for the robotic doctor to take over the human functions of the real doctor and be able to perform the necessary steps to train a robotic doctor. The initial interactive steps between patients and human doctors have successfully stylized and reduced to computer-based artificial modules as far back as 1976 and 1984 by Shortcliffe, et al, [1]. In this paper, we have greatly enhanced and expounded these early rudimentary AI concepts to nine basic steps elaborated in the second section in the body of the paper. In the second half of this paper, we generalize the generalized doctor-patient interactive seven step methodology to the solution of need-based problems of individuals, social entities, corporations, governments and nations. The number of steps and the operating systems and the generic interactive modules need customizations for the specific environments and cultural settings. However, these computer systems are expected to solve such generic problems and provide one or more solutions with one or more specific reason for each deduced procedural step based on practical constraints such as minimization of cost, probability of success, highest benefit to cost ratio, etc. Such an approach reduces time to solve the problem and the number of iteration by human beings to solve large open ended social cultural and environmental problems. The machines are expected to have access to Internet knowledge bases, be able to perform matrix manipulation function to correlate the numerous "objects" with their "functions" in order to generate a realistic and practical solution to social and cultural problems.
... The methodologies of signal transmission in electrical circuits and media are used to derive the transformation matrix for the social media as information and knowledge traverse such social media. The nature of the matrix is presented in [7]. See boxes at rows C through I in columns 1 and 2 of Figure 1. ...
... Behavioral patterns of social entities and objects become important in the emulation of such (KCO's). Unfortunately, the mathematical tools and procedures are not well documented in the DDS 302 and DDS 304, but some of the behavioral modes can be emulated as programmable computer processes [7]. ...
... In the same vein, an activity sheet of the major knowledge functions (e.g., inventions, innovations, novelty and range of products, etc.) within a corporation provide a snap shot of creativity. If it deviates from a bench mark setting, a knowledge machine (KM) [7] will identify the opportunities for innovation and progress. More than that, the KM can formulate creative convolutions of the past verb function and current noun objects that show promise of desirable changes. ...
... The methodologies of signal transmission in electrical circuits and media are used to derive the transformation matrix for the social media as information and knowledge traverse such social media. The nature of the matrix is presented in [7]. See boxes at rows C through I in columns 1 and 2 of Figure 26 This signal flow in electrical engineering and knowledge flow in social media is explained when two objects n1 and n2 interact in a humanistic machine. ...
... In the same vein, an activity sheet of the major knowledge functions (e.g., inventions, innovations, novelty and range of products, etc.) within a corporation provide a snap shot of creativity. If it deviates from a bench mark setting, a knowledge machine (KM) [7] will identify the opportunities for innovation and progress. More than that, the KM can formulate creative convolutions of the past verb function and current noun objects that show promise of desirable changes. ...
In this chapter, we propose that knowledge can be initially designed like any scientific object such a rudimentary automobile, airplane, or spacecraft. The premise is based on the theme that a specific body of knowledge rests on the embedded noun objects and the structural relation between these key-groups of knowledge centric objects (KCOs). The events, interactions, and forces in the society alter such KCOs and their structural relationships. The design of knowledge deploys a very pragmatic approach that knowledge based on these key objects, their interrelationships, and their interactions can be processed by knowledge machines. To this extent, we follow pragmatism (see Rows 5 and 6 in Table 12.1B) proposed by John Dewey and Nathan Crick (see Section 12.2.6), but we also propose that knowledge undergoes dynamic changes in the society, the minds of human beings, and the knowledge structures stored in the memories and knowledge banks of the machine and the Internet. Structures of knowledge can be altered in the knowledge processing units of knowledge machines much like data structures are altered in the central processor units of traditional computers.
... The methodologies of signal transmission in electrical circuits and media are used to derive the transformation matrix for the social media as information and knowledge traverse such social media. The nature of the matrix is presented in [7]. See boxes at rows C through I in columns 1 and 2 of Figure 26 This signal flow in electrical engineering and knowledge flow in social media is explained when two objects n1 and n2 interact in a humanistic machine. ...
... In the same vein, an activity sheet of the major knowledge functions (e.g., inventions, innovations, novelty and range of products, etc.) within a corporation provide a snap shot of creativity. If it deviates from a bench mark setting, a knowledge machine (KM) [7] will identify the opportunities for innovation and progress. More than that, the KM can formulate creative convolutions of the past verb function and current noun objects that show promise of desirable changes. ...
In this chapter, we propose that knowledge can be reduced to its elementary (elemental) size. Each element consisting of quantized noun object(s), their quantized verb function(s), and the incremental type(s) the convolutions that bind such noun objects and verb functions. Even though knowledge may not be quantized as finely and as definitively as matter can be quantized in physics, these elements of knowledge form building block for larger and practical bodies of knowledge. These elements of knowledge (kels) exhibit statistical properties and their dynamics are based on the properties of kels, their origin, their environment, the media, and their recipients. Further, we define the elementary particles as a kuantum of knowledge, even though a kuantum is not a quantum in the traditional sense.
... take over the role of traditional computer systems. Number crunching at pico-second rate is substituted by deliberate considerations of "Knowledge Centric Objects" [5] or KCOs that have intellectual, emotional, financial, and utilitarian linkages and should be weighed and considered. Whereas the computers provide enormous data and information processing power, the Internet provides enormous knowledge and social power. ...
... At the start of the next transaction, the cycle continues with some memory of the prior interaction. This repetitive cycle can be symbolized in the time sequence as (a, b, c, d) cycle needing discrete intervals to for each of the micro steps a, b, c, d, to continue the 5 These instructions are typical for hardwired CPUs. The instructions set for micro-programmed CPUs are quite different and controlled by the micro-programs in the Control memory. ...
When knowledge processing becomes abundant as data processing, the truisms of knowledge will become as frequent as the philosophic wisdom of Plato or Aristotle. We begin this chapter by examining the scientific basis for social machines since order; logic and rationality constitute the basis of science. We extend the knowledge domain of sciences into the wisdom domain of philosophers and derive machines that can process knowledge into axioms based on truisms of the social scientists and philosophers. The Internet society is now bonded by wireless and fiber optic media and moves through the air-waves and fiber. The earlier observations and the deductions were based in a slower society bonded in human minds. The current society lives in an age where computer scans are automated at microsecond speeds and transmitted at gigabits per second to vast intellectual space of refined scientific communities. The impact on human lives is significant. It almost seems as if the human mind is losing its grip on the flow of information and knowledge but not on wisdom and hopefully not on ethics. Truisms and long-term observations affirm the foundations of axioms of wisdom and some of the truisms last long. In this chapter, we propose five such truisms derived from observations but tailored to the peta-flop speeds of modern machines and are generic enough to programmable in personal space of the users of PCs and Androids in the Information age.
... Based on the science of organizing, arranging and architecting of knowledge, a fundamental theory of knowledge is constituted. 15 The general theory of knowledge is based the truism that any action that is associated with an object changes its status. It is also because actions cause change. ...
An insight into the HW, SW and Medical-Ware of Medical Computer Systems that are gradually creeping into the Medical Domain as Computer Aided Analysis and Design, Computer in Manufacture, and finally Robotics have crept in the Engineering Domain. The paper outlines the HW and AI aspects necessary to bridge the gap between Medical Arts to Medical Science.
... The social entities (like human elements, doctors, patients, staff, bacteria, viruses, etc.) and other knowledge centric objects (KCOs [3]) are alive and their profiles change constantly changing. Physical, social, cultural, and environment processes around such objects are constantly occurring. ...
In this chapter, we propose that knowledge bears a remarkable parallelism with the elements in nature. The numerous elements in nature correspond to branches of knowledge. The elements of knowledge play the role of atoms in chemistry that combine with other atoms to make compounds. The laws of chemistry are well adhered in forming stable compounds as the linguistic laws that make up sentences. Chains of compounds make up the organic compounds and polymers that have a useful life as words and sentences that make up “bodies of knowledge” that have finite spans of life. Knowledge science is more recent than chemistry or physical chemistry. Knowledge evolves in the minds of creative human beings and by the ceaseless exploration of machines that explore all combination of noun-objects (ns), verb functions (vs), and their convolutions (*s) that constitutes a kel. The elements in nature have already evolved (as far as we know) and have filled all the possible discrete shell orbits by the electrons, even though some of the rarest elements are highly unstable. In their structure elements and compounds conserve energy. For the exploration of knowledge, still in its infancy, the exploration of knowledge space is as wide as that of the universe itself. Variation of the atomic structures of the elements, that they may assume in other galaxies challenges the imagination that lies at the outer limits of knowledge.
... Noise in social settings is as inevitable as the electrical noise in circuits or as the noise in networks or microwave communications and detailed in [3]. Electromagnetic noises are not as disruptive as those in electrical storms in galactic space or the sunspot activities. ...
Devices, computers, and networks are prime movers in the knowledge era as the internal combustion engines, petroleum products, and roadways were the prime movers of the automobile era. The knowledge worker would be deflated without machines and Internet as much as the human body would perish without food and water. More recently, the hand-held devices and microwave communications have become intrinsically interdependent in providing significant and timely data and information to knowledge workers.
In this chapter, we take bold step and propose the unthinkable: The genesis of a Customizable Mind-Machine. Thought that stems from the mind is deeply seated in a biological framework of neurons. The biological origin lies in the marvel of evolution over the eons and refined ever so fast, faster than in the prior centuries. Three (a, b, and c), triadic objects are ceaselessly at work. At a personal level (a) mind, knowledge, and machines have been intertwined like inspiration, words, and language since the dawn of the human evolution and more recently, (b) technology, manufacturing, and economics have formed a hub of progress, (c) wealth, global marketing, and insatiable needs of humans and civilization. These triadic cycles of nine essential objects of human existence are spinning quicker and quicker every year. The Internet offers the mind no choice but to leap and soar over history and over the globe. Alternatively, human mind can sink deeper and deeper into ignorance and oblivion. More recently, the Artificial Intelligence at work in the Internet had challenged the natural intelligence at the cognizance level in the mind to find its way to breakthroughs and innovations.
Evolution of Knowledge Science: Myth to Medicine: Intelligent Internet-Based Humanist Machines explains how to design and build the next generation of intelligent machines that solve social and environmental problems in a systematic, coherent, and optimal fashion. The book brings together principles from computer and communication sciences, electrical engineering, mathematics, physics, social sciences, and more to describe computer systems that deal with knowledge, its representation, and how to deal with knowledge centric objects. Readers will learn new tools and techniques to measure, enhance, and optimize artificial intelligence strategies for efficiently searching through vast knowledge bases, as well as how to ensure the security of information in open, easily accessible, and fast digital networks.
Author Syed Ahamed joins the basic concepts from various disciplines to describe a robust and coherent knowledge sciences discipline that provides readers with tools, units, and measures to evaluate the flow of knowledge during course work or their research. He offers a unique academic and industrial perspective of the concurrent dynamic changes in computer and communication industries based upon his research. The author has experience both in industry and in teaching graduate level telecommunications and network architecture courses, particularly those dealing with applications of networks in education.
Readership
Graduates, researchers, or professionals in computer science and communication science, especially in knowledge representation, networking and related areas, including those related to education programs
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