Nonlinear Trends in Modern Artificial Intelligence: A New Perspective

  • Elena N. BenderskayaEmail author
Part of the Topics in Intelligent Engineering and Informatics book series (TIEI, volume 4)


Artificial intelligent systems capable of learning, setting goals, solving problems, finding new solutions and unforeseen behavior scenarios without external assistance exist in a variety of odd models, each using different sets of assumptions and each having its own limitations. The author argues whether the modern artificial intelligent systems can be called truly intelligent so that to be able to realize the vast capabilities of the human brain. In order to generalize core issues of the main artificial intelligence domains such as fuzzy logics, probabilistic reasoning, bio-inspired techniques, neural networks apparatus, and neuroscience advances together with the new areas of chaos theory and nonlinear dynamics, a multidisciplinary analysis has been employed in the work. In the analysis, the evolution of a particular mathematical apparatus is being considered to justify the application of dynamic models with unstable dynamics used to solve intelligent problems of the next generation. The conclusion is soundly based on the idea that the future of artificial intelligence lies in the sphere of nonlinear dynamics and chaos that is absolutely critical to understanding and modeling cognition processes.


Fuzzy Logic Chaotic System Nonlinear Trend Chaotic Neural Network Dynamic Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Andreyev, Y.V., Dmitriev, A.S., Chua, L.O., Wu, C.W.: Associative and random access memory using one-dimensional maps. International Journal of Bifurcation and Chaos 2(3), 483–504 (1992)MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Baruchi, I., Ben-Jacob, E.: Towards Neuro-Memory Chip: Imprinting Multiple Memories in Cultured Neural Networks. Physical Review E 75, 50901 (2007)CrossRefGoogle Scholar
  3. 3.
    Baum, S.D., Goertzel, B., Goertzel, T.: How long until human-level AI? Results from an expert assessment. Technological Forecasting and Social Change 78, 185–195 (2011)CrossRefGoogle Scholar
  4. 4.
    Benderskaya, E.N., Zhukova, S.V.: Fragmentary Synchronization in Chaotic Neural Network and Data Mining. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds.) HAIS 2009. LNCS (LNAI), vol. 5572, pp. 319–326. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Benderskaya, E.N., Zhukova, S.V.: Oscillatory Chaotic Neural Network as a Hybrid System for Pattern Recognition. In: IEEE SSCI 2011 - Symposium Series on Computational Intelligence - HIMA 2011: 2011 IEEE Workshop on Hybrid Intelligent Models and Applications, pp. 39–45 (2011)Google Scholar
  6. 6.
    Bobrow, D.G., Brady, M.: Artificial Intelligence 40 years later. Artificial Intelligence 103, 1–4 (1998)CrossRefGoogle Scholar
  7. 7.
    Cristianini, N.: Are we still there? Neural Networks 23, 466–470 (2009)CrossRefGoogle Scholar
  8. 8.
    Ditto, W.L., Murali, K., Sinha, S.: Chaos computing: ideas and implementations. Phil. Trans. R. Soc. A 366, 653–664 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  9. 9.
    Dmitriev, A.S., Kyarginsky, B.Y., Panas, A.I., Starkov, S.O.: Direct chaotic communications schemes in microwave band. J. Commun. Technol. Electron. 46, 224–233 (2001)Google Scholar
  10. 10.
    Dmitriev, A.S., Efremova, E.V., Kletsov, A.V., Kuzmin, L.V., Laktyushkin, A.M., Yu, Y.V.: Ultra Wideband Signals and Sensor Networks. In: Proc. 1st Int. Conf. Management of Technologies and Information Security (ICMIS 2010), Allahabad, India, January 21-24, pp. 48–64 (2010)Google Scholar
  11. 11.
    Freeman, W.J., Skarda, C.A.: Spatial EEG patterns, nonlinear dynamics and perception: the neo-Sherringtonian view. Brain Res. 357, 147–175 (1985)Google Scholar
  12. 12.
    Freeman, W.J.: Neurodynamics: an exploration in mesoscopic brain dynamics. In: Perspectives in Neural Computing. Springer (2000)Google Scholar
  13. 13.
    Freeman, W.J.: Evidence from human scalp electroencephalograms of global chaotic itinerancy. Chaos 13(3), 1067–1077 (2003)CrossRefGoogle Scholar
  14. 14.
    Goertzel, B., Ruiting, L., Itamar, A., Hugo, G., Shuo, C.: A world survey of artificial brain projects, PartII: Biologically inspired cognitive architectures. Neurocomputing 74, 30–49 (2010)CrossRefGoogle Scholar
  15. 15.
    Granichin, O., Gurevich, L., Vakhitov, A.: Discrete-time minimum tracking based on stochastic approximation algorithm with randomized differences. In: Proceedings of the Combined 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference, Shanghai, pp. 5763–5767 (2009)Google Scholar
  16. 16.
    Haken, H.: Neural and Synergetic Computers. Springer (1988)Google Scholar
  17. 17.
    Haken, H.: Synergetic Computers and Cognition: A Top-Down Approach to Neural Nets. SSS. Springer (2010)Google Scholar
  18. 18.
    Havel, I.M.: Artificial Intelligence and Connectionism: Some Philosophical Implications. LNCS (LNAI), pp. 25–41. Springer, Heidelberg (1992)Google Scholar
  19. 19.
    Havel, I.M.: Causal Domains and Emergent Rationality. In: Proceedings of the 23rd International Wittgenstein Symposium, Vienna, pp. 129–151 (2001)Google Scholar
  20. 20.
    Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice-Hall (1998)Google Scholar
  21. 21.
    Horio, Y., Aihara, K.: Analog computation through high-dimensional physical chaotic neuro-dynamics. Physica D: Nonlinear Phenomena 237(9), 1215–1225 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  22. 22.
    Jang, J.R., Sun, C., Mizutani, E.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice Hall (1997)Google Scholar
  23. 23.
    Kazanovich, Y.B.: Nonlinear dynamics modeling and information processing in the brain. Optical Memory and Neural Networks 16(3), 111–124 (2007)CrossRefGoogle Scholar
  24. 24.
    Kay, L.M.: Olfactory Coding: Random Scents Make Sense. Current Biology 21(22), R928–R929 (2011)Google Scholar
  25. 25.
    Korn, H., Faure, P.: Is there chaos in the brain? I. Concepts of nonlinear dynamics and methods of investigation. Life Sciences 324, 773–793 (2001)Google Scholar
  26. 26.
    Li, N., Tourovskaia, A., Folch, A.: Biology on a chip: microfabrication for studying the behavior of cultured cells. Critical Reviews in Biomedical Engineering 31, 423–488 (2003)CrossRefGoogle Scholar
  27. 27.
    Luger, G.F.: Artificial Intelligence: Structures and Strategies for Complex Problem Solving. Addison Wesley (2008)Google Scholar
  28. 28.
    Lukoviius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Computer Science Review 3(3), 127–149 (2009)CrossRefGoogle Scholar
  29. 29.
    Maass, W., Natschlaeger, T., Markram, H.: Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation 14(11), 2531–2560 (2002)zbMATHCrossRefGoogle Scholar
  30. 30.
    Mira, J.M.: Symbols versus connections: 50 years of artificial intelligence. Neurocomputing 71, 671–680 (2008)CrossRefGoogle Scholar
  31. 31.
    Oliveira, F.: Limitations of learning in automata-based systems. European Journal of Operational Research 203, 684–691 (2009)CrossRefGoogle Scholar
  32. 32.
    Pikovsky, A., Rosenblum, M., Kurths, J.: Synchronization: A Universal Concept in Nonlinear Sciences. Cambridge University Press, CNSS (2003)zbMATHGoogle Scholar
  33. 33.
    Potapov, A.A., German, V.A.: Detection of Artificial Objects with Fractal Signatures. Pattern Recognition and Image Analysis 8(2), 226–229 (1998)Google Scholar
  34. 34.
    Potapov, A.A.: The Textures, Fractal, Scaling Effects and Fractional Operators as a Basis of New Methods of Information Processing and Fractal Radio Systems Designing. In: Proc. SPIE, vol. 7374, pp. 73740E-1–73740E-14 (2009)Google Scholar
  35. 35.
    Potapov, A.V., Ali, M.K.: Nonlinear dynamics and chaos in information processing neural networks. Differential Equations and Dynamical Systems 9(3-4), 259–319 (2009)MathSciNetGoogle Scholar
  36. 36.
    Prigogine, I.: The End of Certainty. Free Press (1997)Google Scholar
  37. 37.
    Rojas-Lbano, D., Kay, L.M.: Olfactory system gamma oscillations: the physiological dissection of a cognitive neural system. Cognitive Neurodynamics 2(3), 179–194 (2008)CrossRefGoogle Scholar
  38. 38.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall (2002)Google Scholar
  39. 39.
    Sinha, S., Ditto, W.L.: Computing with distributed chaos. Phys. Rev. E 59, 365–377 (1999)Google Scholar
  40. 40.
    Varela, F.: Resonant cell assemblies: A new approach to cognitive functioning and neuronal synchrony. Biological Research 28, 81–95 (1995)Google Scholar
  41. 41.
    Velazquez, J.: Brain, behaviour and mathematics: Are we using the right approaches? Physica D 212, 161–182 (2005)MathSciNetCrossRefGoogle Scholar
  42. 42.
    Wolfram, S.A.: A New Kind of Science. Wolfram Media (2002)Google Scholar
  43. 43.
    Yang, T.: A survey of chaotic secure communication systems. Int. J. Comput. Cognit. 2, 81–130 (2004)Google Scholar
  44. 44.
    Zak, M.: An unpredictable-dynamics approach to neural intelligence. IEEE Expert: Intelligent Systems and Their Applications archive 6(4), 4–10 (1991)Google Scholar
  45. 45.
    Zak, M.: Quantum-inspired resonance for associative memory. Chaos, Solitons and Fractals 41, 2306–2312 (2009)MathSciNetzbMATHCrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Faculty of Computer ScienceSt. Petersburg State Polytechnical UniversitySt. PetersburgRussia

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