The Law of Information Conversion and Intelligence Creation<strong> </strong>

Abstract

Due to the high importance of information theory to human kinds and the un-satisfaction toward the Shannon Theory of information, there have been hot discussions worldwide during the past decades over the subject of what information and information science should be. Numerals of forum, workshop, and conference have been organized and thousands of paper, journal and book published whereas the feeling of un-satisfaction is not decreasing yet. Having recognized the positive contributions from the discussions during the past, we have also noticed the following lessons associated with the discussions that need necessarily be overcome and improved, in the information studies in the future. Lesson 1: Background Issue. It is noted that different researchers joining in the discussions may have different backgrounds for their studies of information science. So, when speaking of the same term ‘information’, they may mean different things. This leads to big difficulties in mutual understanding among discussants. Lesson 2: Issue of View Angle. It is also noted that different researchers joining in the discussions may have different angle of view in their research of information science so that they can, in fact, acquire rather different impressions from the same information. This also makes difficulties and obstacles for achieving agreements. Lesson 3: Methodological Issue. More importantly, the methodology labeled by “reductionism”, or by “divide and conquer”, has made severe consequence to the information research, not only making the research being divided into a number of different schools but also making the schools diversified and hard to be integrated. As is well known, some authoritative scholar had once made comments on information science studies, by saying that information science is not yet qualified to be called ‘a science’ as it has not invented any laws concerning with the nature and/or society. Taking in mind the lessons, we have made continuous efforts for decades in information studies and have achieved a number of progresses among which the most significant one is the discovery of the “law of information conversion and intelligence creation” through the employment of the new methodology. In what follows, we will share with readers our results, including (1) a number of fundamental concepts closely related to the law, such as ontological information, epistemological information, knowledge, intelligent strategy, etc., (2) three theories supporting the law, that is the theory of comprehensive information, the theory of knowledge ecology, and the theory of intelligence formulation, (3) three categories of conversion needed for the law, consisting of the conversion from ontological information to epistemological information, the conversion from the epistemological information to knowledge, and the conversion from epistemological information, knowledge, and goal to intelligent strategy, and (4) explanation on the significance of the law and concluding remarks. References Shannon C E. A Mathematical Theory of Communication [J], BSTJ, Vol.27, p.379-423, p.632-656, 1948 Zhong Y X. Principles of Information Science (in Chinese) [M], Beijing: BUPT Press. 1988, reprint in 1996, 2002, 2005, and 2013 Zadeh L. Fuzzy Sets Theory, Information and Control, 8, p.338-353, 1965 Feigenbaum A. et al. The Art of Artificial Intelligence: Themes and Case studies in Knowledge Engineering, IJCAI 5, 1014-1029, 1977 Fayad U. et al. Advances in Knowledge Discovery and Data Mining [M]. MIT Press, 1996 Zhong Y X. A Framework of Knowledge Theory [J], Journal of China Engineering Science, Vol.2, p.50-64, 2000 Nilsson, N. J., Artificial Intelligence: A New Synthesis [M], Morgan Kaufmann Publishers, 1998 Russell, S. J. and P. Norvig, Artificial Intelligence: A Modern Approach [M], Pearson Education, Inc., as Prentice Hall, Inc, and Tsinghua University Press. 2006 Zhong Y X. Principles of Cognetics (in Chinese) [M], Beijing: Science Press. 2007 Zhong Y X. Advanced Theory of Artificial Intelligence (in Chinese) [M], Beijing: Science Press, 2014