Keynote, Proc. 12th International Conference on Computational Science and Applications (ICCSA'12), Salvador, Brazil, Springer, June 18-21.
Towards the Next Generation of Cognitive Computers: Knowledge vs. Data Processors Yingxu Wang, Prof., PhD, PEng, FWIF, FICIC, SMIEEE, SMACM President, International Institute of Cognitive Informatics and Cognitive Computing (ICIC) Director, Laboratory for Cognitive Informatics and Cognitive Computing Director, Laboratory for Denotational Mathematics and Software Science Dept. of Electrical and Computer Engineering, Schulich School of Engineering University of Calgary 2500 University Drive, NW, Calgary, Alberta, Canada T2N 1N4 Email:
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Abstract The contemporary wonder of sciences and engineering has recently refocused on the starting point of them: how does the brain process internal and external information autonomously rather than imperatively as those of conventional computers? In celebrating the 100th anniversary of Turing’s pioneer work, curiosity may lead to a fundamental question if more intelligent computers that think, perceive and learn may be developed. The Turing and von Neumann machines are generic data processors created on a basic assumption that objects and behavior of any computing problem can be reduced onto the bit level. However, there is an entire range of complex problems in the real world that may impossibly, or at least, inefficiently be reduced onto bits. This is in accordance with findings in denotational mathematics that most of the complex entities and problems in the real world cannot be abstracted and represented by pure numbers in (bits) or (real numbers). The complex objects beyond and are the hyper structures (), which is a type of mathematical entities modeled by complex n-tuples with multiple fields of attributes and constraints as well as their intricate relations. Examples of the complex objects in , inter alia, are abstract concepts, complex relations, perceptual information, formal knowledge, intelligent behaviors, behavioral processes, rational decisions, language/visual semantics, causations and generic systems. Is it possible to advance the classic computing theories and capabilities closer to those of human brains as a natural knowledge processor that does not reason in ? Instead of reducing every computing problem and solution onto as in conventional data computers, the next generation of knowledge computers known as cognitive computers need to be able to directly process human knowledge in . Because the basic unit of knowledge is an abstract concept in , the mathematical model of knowledge is a Cartesian product of power sets of formal concepts. It is recognized that the basic computing behaviors of the CPU of data computers are arithmetic and logical operations in . However, those of the CPU for cognitive computers are rigorously represented knowledge, causations and semantics in and their formal manipulations using denotational mathematics. The mathematical foundations of classic data computers are Boolean algebra and its logical counterparts in . However, those of the cognitive computers are based on contemporary denotational mathematics such as concept algebra, inference algebra, semantic algebra and process algebra in for rigorously modeling and manipulating knowledge, perception, leaning and inferences. On the basis of the aforementioned basic studies, this keynote lecture will demonstrate the latest advances towards the development of cognitive computers and their applications.
About the Speaker Yingxu Wang is professor of cognitive informatics and software science, President of International Institute of Cognitive Informatics and Cognitive Computing (ICIC), Director of Laboratory for Cognitive Informatics and Cognitive Computing, and of 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 Software Engineering 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 Intelligence (IJCINI), founding Editor-in-Chief of International Journal of Software Science and Computational Intelligence (IJSSCI), Associate Editor of IEEE Trans on System, Man, and Cybernetics (Part A), and associate 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 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 (such as cognitive computers, cognitive robots, cognitive agents, and the cognitive Internet), denotational mathematics (i.e., concept algebra, inference algebra, real-time process algebra, system algebra, granular algebra, and visual semantic algebra), software science (on unified mathematical models and laws of software, cognitive complexity of software, and automatic code generators, coordinative work organization theory, and built-in tests (BITs)), basic studies in cognitive linguistics (such as the cognitive linguistic framework of languages, the deductive semantics of languages, deductive grammar of English, and the cognitive complexity of text comprehension). He has published over 130 peer reviewed journal papers, 220+ peer reviewed conference papers, and 18 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.
