From Information Revolution to Intelligence Revolution: Big Data Science vs. Intelligence Science Yingxu Wang President, International Institute of Cognitive Informatics and Cognitive Computing (ICIC) Director, Laboratory for Cognitive Informatics and Cognitive Computing Dept. of Electrical and Computer Engineering, Schulich School of Engineering The University of Calgary 2500 University Drive, NW, Calgary, Alberta, Canada T2N 1N4 Email:
[email protected] advances in abstract intelligence and intelligence science investigated in cognitive informatics and cognitive computing are well positioned at the center of intelligence revolution. A wide range of applications of cognitive computers have been developing in ICIC [http://www.ucalgary.ca/icic/] such as, inter alia, cognitive computers, cognitive robots, cognitive learning engines, cognitive Internet, cognitive agents, cognitive search engines, cognitive translators, cognitive control systems, cognitive communications systems, and cognitive automobiles.
ABSTRACT The hierarchy of human knowledge is categorized at the levels of data, information, knowledge, and intelligence. For instance, given an AND-gate with 1,000-input pins, it may be described very much differently at various levels of perceptions in the knowledge hierarchy. At the data level on the bottom, it represents a 21,000 state space, known as ‘big data’ in recent terms, which appears to be a big issue in engineering. However, at the information level, it just represents 1,000 bit information that is equivalent to the numbers of inputs. Further, at the knowledge level, it expresses only two rules that if all inputs are one, the output is one; and if any input is zero, the output is zero. Ultimately, at the intelligence level, it is simply an instance of the logical model of an AND-gate with arbitrary inputs. This problem reveals that human intelligence and wisdom are an extremely efficient and a fast convergent induction mechanism for knowledge and wisdom elicitation and abstraction where data are merely factual materials and arbitrary instances in the almost infinite state space of the real world.
Keywords: Cognitive informatics, cognitive computing, abstract intelligence, denotational mathematics, cognitive computers, cognitive robots, knowledge processors, inference engines, learning engines, computational intelligence ABOUT THE KEYNOTE SPEAKER Yingxu Wang is professor of cognitive informatics and software science, President of International Institute of Cognitive Informatics and Cognitive Computing (ICIC, www.ucalgary.ca/icic/), Director of Laboratory for Cognitive Informatics and Cognitive Computing, and Laboratory for Denotational Mathematics and Software Science at the University of Calgary. He is a Fellow of WIF (UK), a Fellow of ICIC, a P.Eng of Canada, and a Senior Member of IEEE and ACM. He received a PhD in Computer Science from the Nottingham Trent University, UK, and a BSc in Electrical Engineering from Shanghai Tiedao University. He has industrial experience since 1972 and has been a full professor since 1994. He was a visiting professor on sabbatical leaves at Oxford University (1995), Stanford University (2008), University of California, Berkeley (2008), and MIT (2012), respectively. He is the founder and steering committee chair of the annual IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC). He is founding Editor-in-Chief of International Journal of Cognitive Informatics and Natural
Although data and information processing have been relatively well studied, the nature, theories, and suitable mathematics underpinning knowledge and intelligence are yet to be systematically studied in cognitive informatics and cognitive computing. This will leads to a new era of human intelligence revolution following the industrial, computational, and information revolutions. This is also in accordance with the driving force of the hierarchical human needs from lowlevel material requirements to high-level ones such as knowledge, wisdom, and intelligence. The trend to the emerging intelligent revolution is to meet the ultimate human needs. The basic approach to intelligent revolution is to invent and embody cognitive computers, cognitive robots, and cognitive systems that extend human memory capacity, learning ability, wisdom, and creativity. Via intelligence revolution, an interconnected cognitive intelligent Internet will enable ordinary people to access highly intelligent systems created based on the latest development of human knowledge and wisdom. Highly professional systems may help people to solve typical everyday problems. Towards these objectives, the latest Proc. 2014 IEEE 13th Int’l Conf. on Cognitive Informatics & Cognitive Computing (ICCI*CC’14) S. Patel, Y. Wang, W. Kinsner, D. Patel, G. Fariello, & L.A. Zadeh (Eds.) 978-1-4799-6081-1/14/$31.00 ©2014 IEEE
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founding Editor-in-Chief of Intelligence (IJCINI), International Journal of Software Science and Computational Intelligence (IJSSCI), Associate Editor of IEEE Trans on System, Man, and Cybernetics - Systems, and Editor-in-Chief of Journal of Advanced Mathematics and Applications. Dr. Wang is the initiator of a few cutting-edge research fields such as cognitive informatics (CI, the theoretical framework of CI, neuroinformatics, the logical model of the brain (LMB), the layered reference model of the brain (LRMB), the cognitive model of brain informatics (CMBI), the mathematical model of consciousness, and the cognitive learning engine (CLE)); abstract intelligence; cognitive computing (cognitive computers, cognitive robots, cognitive agents, and the cognitive Internet); denotational mathematics (concept algebra, semantic algebra, behavioral process algebra, system algebra, inference algebra, granular algebra, and visual semantic algebra); software science (unified mathematical models and laws of software, cognitive complexity of software, automatic code generators, the coordinative work organization theory, and built-in tests (BITs)); basic studies in cognitive linguistics (such as the cognitive linguistic framework of languages, semantic algebra, formal semantics of languages, deductive grammar of English, and the cognitive complexity of text comprehension). He has published 400+ peer reviewed papers and 28 books in cognitive informatics, cognitive computing, software science, denotational mathematics, and computational intelligence. He is the recipient of dozens international awards on academic leadership, outstanding contributions, research achievement, best papers, and teaching in the last three decades.
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Wang, Yingxu (2002), Keynote: On Cognitive Informatics, Proceedings of the First IEEE International Conference on Cognitive Informatics (ICCI'02), Calgary, AB., Canada, IEEE CS Press, August, pp.3442. Wang, Yingxu (2002), The Real-Time Process Algebra (RTPA), Annals of Software Engineering, Springer, 14, 235-274. Wang, Yingxu (2003), On Cognitive Informatics, Brain and Mind: A Transdisciplinary Journal of Neuroscience and Neurophilosophy, 4(2), 151-167. Wang, Yingxu (2006), On the Informatics Laws and Deductive Semantics of Software, IEEE Transactions on Systems, Man, and Cybernetics (Part C), 36(2), March, 161-171. Wang, Yingxu (2007), Software Engineering Foundations: A Software Science Perspective, CRC Series in Software Engineering, Vol. II, Auerbach Publications, USA, July. Wang, Yingxu (2007), The Theoretical Framework of Cognitive Informatics, Int’l Journal of Cognitive Informatics and Natural Intelligence, 1(1), 1-27. Wang, Yingxu (2007), The OAR Model of Neural Informatics for Internal Knowledge Representation in the Brain, International Journal of Cognitive Informatics and Natural Intelligence, 1(3), 66-77.
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Wang, Yingxu (2008), On Contemporary Denotational Mathematics for Computational Intelligence, Transactions of Computational Science, Springer, August, 2, 6-29. Wang, Yingxu (2008), On Concept Algebra: A Denotational Mathematical Structure for Knowledge and Software Modeling, International Journal of Cognitive Informatics and Natural Intelligence, USA, 2(2), 1-19. Wang, Yingxu (2008), RTPA: A Denotational Mathematics for Manipulating Intelligent and Computational Behaviors, International Journal of Cognitive Informatics and Natural Intelligence, 2(2), April, 44-62. Wang, Yingxu (2008), Deductive Semantics of RTPA, International Journal of Cognitive Informatics and Natural Intelligence, 2(2), 95-121. Wang, Yingxu (2008), On the Big-R Notation for Describing Iterative and Recursive Behaviors, International Journal of Cognitive Informatics and Natural Intelligence, 2(1), Jan., 17-28. Wang, Yingxu (2009), A Formal Syntax of Natural Languages and the Deductive Grammar, Fundamenta Informaticae, 90(4), 353-368. Wang, Yingxu (2009), Toward a Formal Knowledge System Theory and Its Cognitive Informatics Foundations, Transactions of Computational Science, Springer, 5, 1-19. Wang, Yingxu (2009), On Cognitive Computing, International Journal of Software Science and Computational Intelligence, 1(3), 1-15. Wang, Yingxu (2009), On Cognitive Foundations of Creativity and the Cognitive Process of Creation, International Journal of Cognitive Informatics and Natural Intelligence, 3(4), 1-18. Wang, Yingxu (2009), On Abstract Intelligence: Toward a Unified Theory of Natural, Artificial, Machinable, and Computational Intelligence, International Journal of Software Science and Computational Intelligence, USA, 1(1), 1-17. Wang, Yingxu (2009), Paradigms of Denotational Mathematics for Cognitive Informatics and Cognitive Computing, Fundamenta Informaticae, 90(3), 282-303. Wang, Yingxu (2010), Cognitive Robots: A Reference Model towards Intelligent Authentication, IEEE Robotics and Automation, 17(4), 54-62. Wang, Yingxu (2010), On Formal and Cognitive Semantics for Semantic Computing, International Journal of Semantic Computing, 4(2), 203–237. Wang, Yingxu (2010), On Concept Algebra for Computing with Words (CWW), International Journal of Semantic Computing, 4(3), 331-356. Wang, Yingxu (2011), Inference Algebra (IA): A Denotational Mathematics for Cognitive Computing and Machine Reasoning (I), International Journal of Cognitive Informatics and Natural Intelligence, 5(4), 61-82. Wang, Y. (2012), On Abstract Intelligence and Brain Informatics: Mapping Cognitive Functions of the Brain onto its Neural Structures, International Journal of
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Cognitive Informatics and Natural Intelligence, 6(4), 54-80. Wang, Yingxu (2012), In Search of Denotational Mathematics: Novel Mathematical Means for Contemporary Intelligence, Brain, and Knowledge Sciences, International Journal of New Mathematics and Applications, 1(1), 4-25. Wang, Yingxu (2012), Editorial: Contemporary Mathematics as a Metamethodology of Science, Engineering, Society, and Humanity, Journal of Advanced Mathematics and Applications, 1(1), 1-3. Wang, Yingxu (2012), The Cognitive Mechanisms and Formal Models of Consciousness, International Journal of Cognitive Informatics and Natural Intelligence, 6(2), 23-40. Wang, Yingxu (2012), On Denotational Mathematics Foundations for the Next Generation of Computers: Cognitive Computers for Knowledge Processing, Journal of Advanced Mathematics and Applications, 1(1), 118-129. Wang, Yingxu (2012), On Long Lifespan Systems and Applications, Journal of Computational and Theoretical Nanoscience, 9(2), 208-216. Wang, Yingxu (2012), Inference Algebra (IA): A Denotational Mathematics for Cognitive Computing and Machine Reasoning (II), International Journal of Cognitive Informatics and Natural Intelligence, 6(1), 21-47. Wang, Yingxu (2013), On Semantic Algebra: A Denotational Mathematics for Cognitive Linguistics, Machine Learning, and Cognitive Computing, Journal of Advanced Mathematics and Applications, 2(2), 6.16.28. Wang, Yingxu (2013), Neuroinformatics Models of Human Memory: Mapping the Cognitive Functions of Memory onto Neurophysiological Structures of the Brain, International Journal of Cognitive Informatics and Natural Intelligence, 7(1), 98-122. Wang, Y. (2013), Keynote: Basic Theories for Neuroinformatics and Neurocomputing, Proceedings 12th IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC 2013), New York, USA, IEEE CS Press, July, pp.3-4. Wang, Yingxu (2014), Fuzzy Causal Inferences based on Fuzzy Semantics of Fuzzy Concepts in Cognitive Computing, WSEAS Transactions on Computers, 13, 430-441. Wang, Yingxu (2014), Keynote: Latest Advances in Neuroinformatics and Fuzzy Systems, Proceedings of 2014 International Conference on Neural Networks and Fuzzy Systems (ICNF-FS’14), Venice, Italy, March, pp. 14-15. Wang, Yingxu (2014), Towards a Theory of Fuzzy Probability for Cognitive Computing, Proc. 13th IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC 2014), London, UK, IEEE CS Press, Aug., pp. 21-29.
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