By starting from physical principles, the paper formulates the basis of the biological intelligence and coscience formation. The work shows that far from equilibrium, the principle of maximum energy dissipation, in a fluid phase, brings the molecules to organize themselves in living systems (ordered stationary states) that sustain energy-matter fluxes that determine the architecture of the dissipative system. The driver of the process is the energy; the matter is the substrate. On this base, and thanks to a “selection rule”, the living systems developed a bi-phasic structure consisting of a solid network permeated by a liquid where fluxes of ions and molecules can be maintained. In this way they realize a “wetware” where the energy and the organization/information are handled, and where a high specialization of functions can be obtained by using the spatial delocalization leading to the development of the complexity of the shape. The outputs of a living system can be divided in two categories: 1) macroscopic outputs: Movements, definition of an organized reaction or plan of action, and 2) microscopic ones: Plastic rearrangement of structures, migration of chemical species and electrochemical dynamics (transportation of matter, rearrangement, sensing, storage and information handling). The biological intelligence is a product of these basic processes where the wetware (matter substrate) is continuously modified by the energy-matter fluxes. On this base, the artificial intelligence of computers can be seen as a subclass of intelligent processes since it owns some limitations due to the invariance of the material substrate (that is not self-modified by the energy fluxes) and by the unique form of the flux of matter that is given by the electron current. Finally, the definition of a bio-inspired artificial intelligence is discussed.
Published in | American Journal of Applied Psychology (Volume 5, Issue 6) |
DOI | 10.11648/j.ajap.20160506.19 |
Page(s) | 98-103 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2016. Published by Science Publishing Group |
Generation of Life, Living Systems, Biological Intelligence, Biological Conscience, Artificial Intelligence, Matter Self-Organization, Maximum Free Energy Dissipation
[1] | I. Prigogine, Bulletin de la Classe des Sciences, Academie Royale de Belgique 31: (1945) 600–606. |
[2] | I. Prigogine, Étude thermodynamique des Phenomènes Irreversibles, (Desoer, Liege 1947). |
[3] | B. H. Lavenda, Thermodynamics of Irreversible Processes, (Macmillan, London, 1978). |
[4] | M. Šilhavý, The Mechanics and Thermodynamics of Continuous Media, (Springer, Berlin, 1997) p. 209. |
[5] | Y. Sawada, Progr. Theor. Phys. 66, 68-76 (1981). |
[6] | W. V. R Malkus, and G. Veronis, J. Fluid Mech. 4 (3), 225–260 (1958). |
[7] | L. Onsager, Phys. Rev. 37 (4), 405–426 (1931). |
[8] | M. Suzuky and Y. Sawada, Phys. Rew. A, 27-1 (1983). |
[9] | W. T. Grandy, Entropy and the Time Evolution of Macroscopic Systems, (Oxford University Press2008). |
[10] | E. Madelung, Z. Phys. 40, 322-6, (1926). |
[11] | I. Bialynicki-Birula, M. Cieplak and J. Kaminski, Theory of Quanta, (Oxford University Press, Ny 1992). |
[12] | J. H. Weiner, Statistical Mechanics of Elasticity (John Wiley & Sons, New York, 1983), p. 316-317. |
[13] | Chiarelli, P., “Can fluctuating quantum states acquire the classical behavior on large scale?” J. Adv. Phys. 2013; 2, 139-163; arXiv: 1107.4198 [quantum-phys] 2012. |
[14] | Chiarelli, P., “Far from equilibrium maximal principle leading to matter self-organization” submitted to J. Adv. Chem., 5 (3) (2013) pp. 753-83. |
[15] | Chiarelli, P., Does life needs water or can be generated other fluids?, O. J. BioPhys., 4,. (2014) pp. 29-38. |
[16] | Y. B. Rumer, M. S. Ryvkin, Thermodynamics, Statistical Physics, and Kinetics (Mir Publishers, Moscow, 1980). |
[17] | Suzuki, M., Sawada, Y., “Relative stabilities of metastable states of convecting charged-fluid system by computer simulation”, Phys. Rev. A, 1-27, (1982). |
[18] | C. P. McKay, H. D. Smith, “Possibilities for methanogenic life in liquid methane on the surface of Titan”, Icarus 178 (2005) 274–276. |
[19] | Committee on the Limits of Organic Life in Planetary Systems, Committee on the Origins and Evolution of Life, National Research Council; The Limits of Organic Life in Planetary Systems; The National Academies Press, 2007; page 74. |
[20] | De Rossi, D., C. Domenici, Chiarelli, P.,: Analog of Biological Tissue for Mechanoelectrical Transduction: Tactile sensor and muscle-like actuators, in Sensors and Sensory Systems for Advanced robots, P. Dario Ed., 210-18, Nato ASi series, Springer Verlag, Berlin, 1988. |
[21] | Chiarelli, P., The pro-elastic behavior of a gel thin tube Materials Science and Engineering C 24 (4): 463-471•June 2004. |
[22] | Chiarelli, P., De Rossi, D., Modelling and Mechanical Characterization of Thin Fibres of Contractile Polymer Hydrogels, J. Int. Mat. Syst. & Struc., 3, 396-417, 1992. |
[23] | Chiarelli, P., De Rossi, Polyelectrolyte Intelligent Gels: design and Applications, Ionic Interactions in Natural and Synthetic Macromolecules, First Edition. Edited by Alberto Ciferri and Angelo Perico. 2012 John Wiley & Sons, Inc DOI: 10.1002/9781118165850. ch15 |
[24] | Chiarelli, P., De Rossi, D., Basser, P.,: Hydrogel Stress-Relaxation, J. Int. Mat. Syst. & Struc., 4, 176-83, 1993. |
[25] | Grimby, L., Hannerz, J., Hedman, B., Contraction time and voluntary discharge properties of individual short toe extensor motor units in man. J. Pysiol. 1979 Apr; 289: 191–201. |
[26] | Chiarelli, P., Eng, J., Basser, P.,: "A Polymer-based method to measure contact stress", Proc. Symposium on Polymer Gels, Tsukuba, Japan, 1989 |
[27] | A. J. Grodzinsky and N. A. Shoenfeld., Polymer, 18, 5 (1977). |
[28] | Clegg, J., Walker; J., A., Miller, J., F., et. al., Genetic Programming for prevention of cyber terrorism through dynamic and evolving intrusion, Decision Support Systems, 43 (4): (2007). 1362–1374. |
[29] | Jamil El-Ali1, Peter K., et al., Cells on chips, NATURE 442, 27 (2006). |
[30] | Huh, D., Matthews, B., D., Mammoto, A., et al., Reconstituting Organ-Level Lung Functions on a Chip, Science, 328 (5986) (2010) pp. 1662-1668. |
APA Style
Piero Chiarelli. (2016). Artificial and Biological Intelligence: Hardware vs Wetware. American Journal of Applied Psychology, 5(6), 98-103. https://doi.org/10.11648/j.ajap.20160506.19
ACS Style
Piero Chiarelli. Artificial and Biological Intelligence: Hardware vs Wetware. Am. J. Appl. Psychol. 2016, 5(6), 98-103. doi: 10.11648/j.ajap.20160506.19
@article{10.11648/j.ajap.20160506.19, author = {Piero Chiarelli}, title = {Artificial and Biological Intelligence: Hardware vs Wetware}, journal = {American Journal of Applied Psychology}, volume = {5}, number = {6}, pages = {98-103}, doi = {10.11648/j.ajap.20160506.19}, url = {https://doi.org/10.11648/j.ajap.20160506.19}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajap.20160506.19}, abstract = {By starting from physical principles, the paper formulates the basis of the biological intelligence and coscience formation. The work shows that far from equilibrium, the principle of maximum energy dissipation, in a fluid phase, brings the molecules to organize themselves in living systems (ordered stationary states) that sustain energy-matter fluxes that determine the architecture of the dissipative system. The driver of the process is the energy; the matter is the substrate. On this base, and thanks to a “selection rule”, the living systems developed a bi-phasic structure consisting of a solid network permeated by a liquid where fluxes of ions and molecules can be maintained. In this way they realize a “wetware” where the energy and the organization/information are handled, and where a high specialization of functions can be obtained by using the spatial delocalization leading to the development of the complexity of the shape. The outputs of a living system can be divided in two categories: 1) macroscopic outputs: Movements, definition of an organized reaction or plan of action, and 2) microscopic ones: Plastic rearrangement of structures, migration of chemical species and electrochemical dynamics (transportation of matter, rearrangement, sensing, storage and information handling). The biological intelligence is a product of these basic processes where the wetware (matter substrate) is continuously modified by the energy-matter fluxes. On this base, the artificial intelligence of computers can be seen as a subclass of intelligent processes since it owns some limitations due to the invariance of the material substrate (that is not self-modified by the energy fluxes) and by the unique form of the flux of matter that is given by the electron current. Finally, the definition of a bio-inspired artificial intelligence is discussed.}, year = {2016} }
TY - JOUR T1 - Artificial and Biological Intelligence: Hardware vs Wetware AU - Piero Chiarelli Y1 - 2016/12/30 PY - 2016 N1 - https://doi.org/10.11648/j.ajap.20160506.19 DO - 10.11648/j.ajap.20160506.19 T2 - American Journal of Applied Psychology JF - American Journal of Applied Psychology JO - American Journal of Applied Psychology SP - 98 EP - 103 PB - Science Publishing Group SN - 2328-5672 UR - https://doi.org/10.11648/j.ajap.20160506.19 AB - By starting from physical principles, the paper formulates the basis of the biological intelligence and coscience formation. The work shows that far from equilibrium, the principle of maximum energy dissipation, in a fluid phase, brings the molecules to organize themselves in living systems (ordered stationary states) that sustain energy-matter fluxes that determine the architecture of the dissipative system. The driver of the process is the energy; the matter is the substrate. On this base, and thanks to a “selection rule”, the living systems developed a bi-phasic structure consisting of a solid network permeated by a liquid where fluxes of ions and molecules can be maintained. In this way they realize a “wetware” where the energy and the organization/information are handled, and where a high specialization of functions can be obtained by using the spatial delocalization leading to the development of the complexity of the shape. The outputs of a living system can be divided in two categories: 1) macroscopic outputs: Movements, definition of an organized reaction or plan of action, and 2) microscopic ones: Plastic rearrangement of structures, migration of chemical species and electrochemical dynamics (transportation of matter, rearrangement, sensing, storage and information handling). The biological intelligence is a product of these basic processes where the wetware (matter substrate) is continuously modified by the energy-matter fluxes. On this base, the artificial intelligence of computers can be seen as a subclass of intelligent processes since it owns some limitations due to the invariance of the material substrate (that is not self-modified by the energy fluxes) and by the unique form of the flux of matter that is given by the electron current. Finally, the definition of a bio-inspired artificial intelligence is discussed. VL - 5 IS - 6 ER -