Abstract
The purpose of this paper is to prove the fundamental laws used in the three types of information processing through knowledge content and language form. Thus, the ability showing human-computer as twin smart system of information co-processing is analyzed, and its core intelligence is targeted to produce ambiguity or to reduce ambiguity. The method includes three steps: first, to explore logical rules of thinking process followed by human brain, second, to find out mathematical principles of information processing followed by computer, third, to accumulate a series of translation rules of bilingual information processing followed by human-machine collaboration. The result is that the fundamental laws of information processing are proved by both mental program contents and language expression symbols, including logical rules for content information processing, mathematical rules for formal information processing, generalized translation rules for all kinds of bilingual information processing. Its significance is that it not only clarifies the three rules of human-machine as the generalized bilingual information co-processing, but also finds sequencing and positioning in logic as smart system of language, knowledge, software, hardware, and as super-development environment for big production of knowledge, and all supporting with the generalized text gene information processing within smart system as the common focus collaborated with education, management, learning, using or application.
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References
Beziau, J.-Y.: What is “formal logic”? In: Proceedings of the XXII World Congress of Philosophy, vol. 13, pp. 9–22 (2008)
Chiswell, I.: Mathematical Logic. Oxford University Press, Oxford (2007)
Turing, A.M.: Computing machinery and intelligence. Mind 49, 433–460 (1950)
Searle, J.: Minds, brains and programs. Behav. Brain Sci. 3, 417–457 (1980)
Taylor, J.M., Raskin, V., Hempelmann, C.F.: From disambiguation failures to common-sense knowledge acquisition: a day in the life of an ontological semantic system. In: Web Intelligence (2011)
Ludwig, D.S.: Overlapping ontologies and indigenous knowledge from integration to ontological self-determination. Stud. Hist. Philos. Sci. 59, 36–45 (2016)
Saussure, F.: Selections from the course in general linguistics. In: Kearney, R., Rainwater, M. (eds.) The Continental Philosophy Reader. Routledge, Abingdon (1996)
Chomsky, N.: Syntactic Structures. De Gruyter Mouton (1957), 2nd edition 19 November 2002
Zou, X.: Collaborative Intelligent Computing System: Theoretical Model with Its Application. AAAS (2012)
Zou, X.: Basic law of information: the fundamental theory of generalized bilingual processing. In: ISIS Summit Vienna (2015)
Pinar Saygin, A., Cicekli, I., Akman, V.: Turing test: 50 years later. Mind. Mach. 10, 463 (2000)
Charlesworth, A.: The comprehensibility theorem and the foundations of artificial intelligence. Mind. Mach. 24, 439 (2014)
Gonzalez, W.J.: From intelligence to rationality of minds and machines in contemporary society: the sciences of design and the role of information. Mind. Mach. 27, 397 (2017)
Damper, R.I.: The logic of Searle’s Chinese room argument. Mind. Mach. 16, 163 (2006)
Rodríguez, D., Hermosillo, J., Lara, B.: Meaning in artificial agents: the symbol grounding problem revisited. Mind. Mach. 22, 25 (2012)
Bozşahin, C.: Computers aren’t syntax all the way down or content all the way up. Mind. Mach. 28, 543 (2018)
Zou, S., Zou, X.: Understanding: how to resolve ambiguity. In: Shi, Z., Goertzel, B., Feng, J. (eds.) ICIS 2017. IAICT, vol. 510, pp. 333–343. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68121-4_36
Zou, S., Zou, X., Wang, X.: How to do knowledge module finishing. In: Shi, Z., Pennartz, C., Huang, T. (eds.) ICIS 2018. IAICT, vol. 539, pp. 134–145. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01313-4_14
Wehmeier, K.F.: The proper treatment of variables in predicate logic. Linguist. Philos. 41, 209 (2018)
Kohlhase, M., Koprucki, T., Müller, D., Tabelow, K.: Mathematical models as research data via flexiformal theory graphs. In: Geuvers, H., England, M., Hasan, O., Rabe, F., Teschke, O. (eds.) CICM 2017. LNCS (LNAI), vol. 10383, pp. 224–238. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-62075-6_16
Zou, S., Zou, X.: Ecological characteristics of information and its scientific research. In: Multidisciplinary Digital Publishing Institute Proceedings, vol. 1, p. 59 (2017)
Zou, X., Zou, S., Ke, L.: Fundamental law of information: proved by both numbers and characters in conjugate matrices. In: Multidisciplinary Digital Publishing Institute Proceedings, vol. 1, p. 60 (2017)
Acknowledgement
Thanks to the recent academic exchanges on ICIS2018 in Peking University, we have made clear the formal relationship between human and machine in language cognition! I would like to thank the anonymous reviewers of ICCSIP in Tsinghua University for giving us some pertinent suggestions!
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Zou, S., Zou, X., Wang, X. (2019). How to Understand the Fundamental Laws of Information. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2018. Communications in Computer and Information Science, vol 1006. Springer, Singapore. https://doi.org/10.1007/978-981-13-7986-4_4
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