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Rapprochement of artificial intelligence and dynamics
Authors:Michael Conrad
Affiliation:1. Center of Excellence in Structures and Earthquake Engineering, Department of Civil Engineering, Sharif University of Technology, P.O. Box. 11365-9313, Tehran, Iran;2. Department of Civil Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran;1. State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Beijing, 100081, China;2. Department of Engineering Mechanics, Kunming University of Science and Technology, Yunnan, 650000, China;3. Chongqing Innovation Center, Beijing Institute of Technology, Chongqing, 401120, China;1. Dept. of High-tech Business and Entrepreneurship (ETM), University of Twente, Netherlands;2. KOF Swiss Economic Institute, Dept. of Management, Technology, and Economics, ETH Zurich, Switzerland;3. ISTARI.AI, Mannheim, Germany;4. Dept. of Economics of Innovation and Industrial Dynamics, Leibniz Centre for European Economic Research (ZEW), Germany;5. Dept. of Economics, Justus-Liebig Universität, Gießen, Germany;6. University of Mannheim, MCEI, Mannheim, Germany;7. Chair for Innovation Management, University of Hohenheim, Stuttgart, Germany;1. Akershus University Hospital, Sykehusveien 25, 1478 Lørenskog, Norway;2. BI Norwegian Business School, Nydalsveien 37, 0484 Oslo, Norway;3. Loccioni Group, Via Fiume, 16, 60030 Angeli di Rosora, Ancona, Italy;4. Uppsala University, Box 534, S-75121 Uppsala, Sweden;5. Università Politecnica delle Marche, P. le. Martelli 8, 60121 Ancona, Italy;6. Università della Svizzera italiana, USI, via G. Buffi 13, 6904 Lugano, Switzerland
Abstract:It is proposed that continuous time is in effect discretized in the brain by dynamic pattern recognition mechanisms in neurons. Time discretization is required to support formal computations in continuous time systems consisting of a large number of components. The ability to perform formal computations is necessary if the system is to execute high level algorithms of the type used in present day artificial intelligence. The weakness of such algorithms is that they work efficiently only when the forms of patterns and objects presented to them are highly constrained. The dynamic mechanisms which discretize the brain's time line also serve to code patterns into constrained forms suitable for high level processing.
Keywords:Computers   brain   adaptive processes   learning   artificial intelligence   time
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