| Peer-Reviewed

Unlocking the Quest for Artificial Wisdom as Integration of Artificial Intelligence with Desire

Received: 20 March 2015     Accepted: 21 March 2015     Published: 27 May 2015
Views:       Downloads:
Abstract

Wisdom = Knowledge + Desire. Desire = Need - Knowledge of Self - Unbiased Reasoning. Wisdom is the process of dynamic correlations among knowledge quanta (KQ), and desire quanta to generate new knowledge, and desire quanta, that in turn generates new propositions as priori, or, counterbalanced, or self-presenting to have 'true belief de re' to enable belief without sufficient evidence or dis-belief with sufficient evidence. Dynamic correlation procedure is the use of generalizability thesis (GZT) to synthesize inside intelligence improvement loop (IIL). The collection of data, creation of information, crashing of information to KQ and conceiving of KQ in long term memory (LTM) on generation of explicit links to other KQs those are already in existence and subsequent generation of wisdom module to be collected as data is termed as IIL. We may define artificial wisdom (AW) as integration of artificial intelligence (AI) with desire. AI is the p proposition of GZT, desire is the q proposition, and r is the integration operator (INO). Thinking and creation is manifestation of dynamic correlation of desire with knowledge. INO should have two parts - integration process (IP) and integration rules (IR). IP will be the set of propositions to effect the AI to satisfy needs. IP always follows IR to fulfill the growth needs. As per IIL the set of rules or algorithms are the scholar’s capability to reference different KQ simultaneously. The edge of discovery comes from the effectiveness of the parallel processing activities of the multiprocessor environment that again in turn depends on the rules and algorithms defined with propositional knowledge. The thinking capability of AW is to be branched out in 'mutually exclusive and/or inclusive' hardware and software standardizations. The term 'mutually exclusive and/or inclusive' refers a multiprocessor parallel processing system, with simplified linking and loading scheme to work in real time. That is a machine that can behave, think like a human and be trained or else upgraded with very simple instruction sets. This seems to be easier if there is a hardware interpreter for high-level language. It is interpreter because while referencing a KQ for any (possible) remark, KQ will interpret only the present information (focal knowledge with respect to the comprehensive whole for which it is called for).

Published in Science Research (Volume 3, Issue 3)
DOI 10.11648/j.sr.20150303.16
Page(s) 79-88
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), 2015. Published by Science Publishing Group

Keywords

Artificial Wisdom, Artificial Intelligence, Desire, Need, Dynamic Correlations, Generalizability Thesis, High-Level Hardware Interpreter

References
[1] John Adair Decision Making and Problem Solving, University Press (India) Limited 2000.
[2] Baron Robert A., Psychology, Prentice-Hall of India Pvt. Ltd. 1995.
[3] Baron R. A. and Byrne D. Social Psychology Prentice-Hall of India Pvt. Ltd. 1995.
[4] Boisot M., Is Your Firm a Creative Destroyer? Competitive Learning and Knowledge Flows in the Technological Strategies of Firms, in: Michel H. Zack (editor), Knowledge and Strategy; (Butterworth – Heinemann) 1999.
[5] Boisot M. 1994, Information and Organization: The Managers as Anthropologist, Harper and Collins, London.
[6] Chisholm Roderick M. 1977, Theory Knowledge, second edition, Prentice-Hall Inc.
[7] Chomsky, N. 1980, Rules and Representations. Columbia University Press, New York.
[8] Chomsky, N. 1986, Knowledge of Language. New York, Praeger.
[9] Donaldson and Preston L.E. 1995, The Stakeholder Theory of the Corporation: Concepts, Evidence and Implication, Academy of Management Review, vol. 20, pp 65-91.
[10] Garvin D., 1993, Building a Learning Organization, Harverd Business Review, July-August 1993.
[11] Greenberg Jerald and Baron Robbert A. Behavior in Organizations Prentice-Hall Inc. 1995
[12] Dictionary of Philosophy, 2003, www.artsci.wustl.edu/-philos/MindDict.html Accessed February2003
[13] Drucker Peter F. Information, Control & Management, in Executive Decision Making edited by Davar Rustom S. 1966.
[14] Katz Daniel and Kahn Robert L. 1978, The Social Psychology of Organizations, 2nd edition John Wiley, New York
[15] KM-Foram 2002 What is Knowledge Management, KM-Forum.org January2003
[16] Keith Davis, Human Behavior at Work Organizational Behavior, Tata McGraw-Hill Publishing Co. 1994
[17] Keith Lehrer. 2000, Theory of Knowledge. Westview Press.
[18] Mattey G. J. Lecture Notes, Lehrer’s Theory of Knowledge, second edition, Chapter two, www.philosophy.ucdavis.edu/phi102/ tkch2.htm Accessed January 2003.
[19] Mattey G. J. 9, Lecture Notes, Lehrer’s Theory of Knowledge, second edition, Chapter nine, www.philosophy.ucdavis.edu/phi102/tkch9.htm Accessed January 2003
[20] Mattey G. J., 2001, Self-trust and the reasonableness of acceptance, www.philosophy.ucdavis.edu/phildept/GJMATTEY.htm Accessed January 2003.
[21] Michel H. Zack (editor), 1999, Knowledge and Strategy; pp-x, Butterworth – Heinemann.
[22] Mike Lehr, Intelligence vs. Wisdom (Pt 2): Magical Difference, http://blog.omegazadvisors.com/2013/03/21/intelligence-vs-wisdom-pt-2-magical-difference/ Accessed Jan 2015
[23] Pearce J. A. 1982, The Company Mission as a Strategic Tool, Sloan Management Review, Spring 1982, pp 15-24.
[24] Polanyi M., 1958, Personal Knowledge: Towards a Post- Critical Philosophy, Routledge and Kegan Paul, London. Japan, p. 301, 1982].
[25] Polanyi Michael 1962 Tacit Knowling: Its Bearing on Some Problem of Philosophy, review of Modern Physicss, 34 (4) Oct 1962,the Polanyi Society www.missourwestern.edu
[26] Prahalad C. K. and Hamel Gary, 1990, The Core Competence of the Corporation, in: Michel H. Zack (editor), Knowledge and Strategy; (Butterworth – Heinemann) 1999.
[27] Quinn James Brian, Anderson Philip and Finkelstein Sydney, 1996, Leveraging Intellect, in: Michel H. Zack (editor), Knowledge and Strategy; (Butterworth – Heinemann) 1999.
[28] Robert M. Grant, 1991, The Resource Based Theory of Competitive Advantage: Implication for Strategy Formulation, in: Michel H. Zack (editor), Knowledge and Strategy; (Butterworth – Heinemann) 1999
[29] Robbins Stephen P. 1999, Organization Theory: Structure, Design and Applications, Prentice-Hall Inc. Englewood Cliffs.
[30] Romer Paul M., 1995, Beyond the Knowledge Worker, in: Michel H. Zack (editor), Knowledge and Strategy; (Butterworth – Heinemann) 1999.
[31] Saint-Onge Hubert, Tacit Knowledge: The Key to the Strategic Alignment of Intellectual Capital, in: Michel H. Zack (editor), Knowledge and Strategy; (Butterworth – Heinemann) 1999
[32] Sarkar Aloke, “I.T for Integrating TQM, KM, Six Sigma & Organizational Behavior” International Conference on Information Technology: Prospects & Challenges, 2003 Nepal.
[33] Sarkar Aloke, “Hardware realization of level 5 virtual machine on Convergence in analog & digital computations” International Conference on Information Technology: Prospects & Challenges, 2003, Nepal.
[34] Sarkar Aloke, “Generate Organizational Wisdom to Survive in Market Turbulence,” National Seminar on Management Challenges - The Road Ahead at Indian School of Mines Dhanbad on February 4 & 5, 2005.
[35] Sarkar Aloke “Architecture definition of non-binary hardware processor for non-binary multivariate parallel processing using pulse position modulation,” ISCON – 2012 7th National Conference on Advancement of Technologies – Information Systems & Computer Network; GLA University, Mathura, India.
[36] Sarkar Aloke “Introduction to the artificial wisdom,” ISCON – 2012 7th National Conference on Advancement of Technologies – Information Systems & Computer Network; GLA University, Mathura, India.
[37] Scott Sturgeon, Knowledge, in Grayling A. C. (editor) Philosophy 1, Oxford University Press, 1998.
[38] Sarkar Aloke, “Architecture Definition of Non-Binary Hardware processor & Possible Applications,” INDICON 2012, Kochi, India.
[39] Schein E. H. 1992, Organizational Culture and Leadership, Jossey-Bass, San Francisco.
[40] Scott Sturgeon, 1998, Knowledge, in Grayling A. C. (editor) Philosophy 1, Oxford University Press, 1998.
[41] Senge P., Kleiner A., Roberts C., Ross R. B. and Smith B. J. 1994, The Fifth Discipline Fieldbook: Strategies and Tools for building a Learning Organization, Doubleday, NewYork.
[42] Michel H. Zack (editor), Knowledge and Strategy; (Butterworth – Heinemann) 1999
[43] Barbiero, www.artsci.wustl.edu/-philos/MindDict/tacitknowledge.htm
[44] Sarkar Aloke, “A Perspective to the Artificial Wisdom" INDICON 2012, Kochi, India.
[45] The Bhagavad-Gita, a Hindu Religion's sacred philosophical book.
[46] http://www.uhh.hawaii.edu/~ronald/310/310-BD-psychology.htm Downloaded on 13March 2015.
Cite This Article
  • APA Style

    Aloke Sarkar. (2015). Unlocking the Quest for Artificial Wisdom as Integration of Artificial Intelligence with Desire. Science Research, 3(3), 79-88. https://doi.org/10.11648/j.sr.20150303.16

    Copy | Download

    ACS Style

    Aloke Sarkar. Unlocking the Quest for Artificial Wisdom as Integration of Artificial Intelligence with Desire. Sci. Res. 2015, 3(3), 79-88. doi: 10.11648/j.sr.20150303.16

    Copy | Download

    AMA Style

    Aloke Sarkar. Unlocking the Quest for Artificial Wisdom as Integration of Artificial Intelligence with Desire. Sci Res. 2015;3(3):79-88. doi: 10.11648/j.sr.20150303.16

    Copy | Download

  • @article{10.11648/j.sr.20150303.16,
      author = {Aloke Sarkar},
      title = {Unlocking the Quest for Artificial Wisdom as Integration of Artificial Intelligence with Desire},
      journal = {Science Research},
      volume = {3},
      number = {3},
      pages = {79-88},
      doi = {10.11648/j.sr.20150303.16},
      url = {https://doi.org/10.11648/j.sr.20150303.16},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sr.20150303.16},
      abstract = {Wisdom = Knowledge + Desire. Desire = Need - Knowledge of Self - Unbiased Reasoning. Wisdom is the process of dynamic correlations among knowledge quanta (KQ), and desire quanta to generate new knowledge, and desire quanta, that in turn generates new propositions as priori, or, counterbalanced, or self-presenting to have 'true belief de re' to enable belief without sufficient evidence or dis-belief with sufficient evidence. Dynamic correlation procedure is the use of generalizability thesis (GZT) to synthesize inside intelligence improvement loop (IIL). The collection of data, creation of information, crashing of information to KQ and conceiving of KQ in long term memory (LTM) on generation of explicit links to other KQs those are already in existence and subsequent generation of wisdom module to be collected as data is termed as IIL. We may define artificial wisdom (AW) as integration of artificial intelligence (AI) with desire. AI is the p proposition of GZT, desire is the q proposition, and r is the integration operator (INO). Thinking and creation is manifestation of dynamic correlation of desire with knowledge. INO should have two parts - integration process (IP) and integration rules (IR). IP will be the set of propositions to effect the AI to satisfy needs. IP always follows IR to fulfill the growth needs. As per IIL the set of rules or algorithms are the scholar’s capability to reference different KQ simultaneously. The edge of discovery comes from the effectiveness of the parallel processing activities of the multiprocessor environment that again in turn depends on the rules and algorithms defined with propositional knowledge. The thinking capability of AW is to be branched out in 'mutually exclusive and/or inclusive' hardware and software standardizations. The term 'mutually exclusive and/or inclusive' refers a multiprocessor parallel processing system, with simplified linking and loading scheme to work in real time. That is a machine that can behave, think like a human and be trained or else upgraded with very simple instruction sets. This seems to be easier if there is a hardware interpreter for high-level language. It is interpreter because while referencing a KQ for any (possible) remark, KQ will interpret only the present information (focal knowledge with respect to the comprehensive whole for which it is called for).},
     year = {2015}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Unlocking the Quest for Artificial Wisdom as Integration of Artificial Intelligence with Desire
    AU  - Aloke Sarkar
    Y1  - 2015/05/27
    PY  - 2015
    N1  - https://doi.org/10.11648/j.sr.20150303.16
    DO  - 10.11648/j.sr.20150303.16
    T2  - Science Research
    JF  - Science Research
    JO  - Science Research
    SP  - 79
    EP  - 88
    PB  - Science Publishing Group
    SN  - 2329-0927
    UR  - https://doi.org/10.11648/j.sr.20150303.16
    AB  - Wisdom = Knowledge + Desire. Desire = Need - Knowledge of Self - Unbiased Reasoning. Wisdom is the process of dynamic correlations among knowledge quanta (KQ), and desire quanta to generate new knowledge, and desire quanta, that in turn generates new propositions as priori, or, counterbalanced, or self-presenting to have 'true belief de re' to enable belief without sufficient evidence or dis-belief with sufficient evidence. Dynamic correlation procedure is the use of generalizability thesis (GZT) to synthesize inside intelligence improvement loop (IIL). The collection of data, creation of information, crashing of information to KQ and conceiving of KQ in long term memory (LTM) on generation of explicit links to other KQs those are already in existence and subsequent generation of wisdom module to be collected as data is termed as IIL. We may define artificial wisdom (AW) as integration of artificial intelligence (AI) with desire. AI is the p proposition of GZT, desire is the q proposition, and r is the integration operator (INO). Thinking and creation is manifestation of dynamic correlation of desire with knowledge. INO should have two parts - integration process (IP) and integration rules (IR). IP will be the set of propositions to effect the AI to satisfy needs. IP always follows IR to fulfill the growth needs. As per IIL the set of rules or algorithms are the scholar’s capability to reference different KQ simultaneously. The edge of discovery comes from the effectiveness of the parallel processing activities of the multiprocessor environment that again in turn depends on the rules and algorithms defined with propositional knowledge. The thinking capability of AW is to be branched out in 'mutually exclusive and/or inclusive' hardware and software standardizations. The term 'mutually exclusive and/or inclusive' refers a multiprocessor parallel processing system, with simplified linking and loading scheme to work in real time. That is a machine that can behave, think like a human and be trained or else upgraded with very simple instruction sets. This seems to be easier if there is a hardware interpreter for high-level language. It is interpreter because while referencing a KQ for any (possible) remark, KQ will interpret only the present information (focal knowledge with respect to the comprehensive whole for which it is called for).
    VL  - 3
    IS  - 3
    ER  - 

    Copy | Download

Author Information
  • Electronics & Communication Engg., Computer Engg. Associate Member of Institution of Engineers (India), Kolkata, India

  • Sections