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

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).