The Functional Success of Intelligent Automata

Abstract

Based on the standpoint close to that of philosophical functionalism, the paper addresses the history and present state of scientific research on artificial reconstruction of some of the rational thinking functions, and acquisition of valid knowledge of phenomenally given (f)actuality, by way of engineering artificial intelligent automata. Thereby, the author examines the settings of emergence of an attendant basic ontology, and the place of logic in the process of knowing, by intelligent automata and humans, accordingly. Analysis and collations are made by proceeding from the phenomenalistic logic and ontology presupposition in the theory of experience and knowing, as previously detailed by the author. Under this presupposition, the (f)actual is considered temporally ordered (hence, ontologically rooted) facts with attached significance used to order them logically, in reciprocally defining them logical relations as a result of phenomenal statements on their identification and re-identification as such (recognition by significance). The paper offers an overview of primary logical and ontological foundations for operation of intelligent automata classed as “expert systems” in their collation with a correspondent model of functionally determined meaningful activity of humans, while making the case for the observation on a sufficient functional parallelism between them, against the background of their noted specifics. By moving on to engineering intelligent automata classed as “machine learning systems” grounded on “connectionism”, prospects for overcoming the deficit of reflexive plasticity (impressionability) in “artificial intelligence”, and the effect of fundamental convergence between the human functional intelligence and an “artificial” intelligence (at the level of the very natural mechanism implementing them both), are clarified. Admitting a demonstrated functional success of such intelligent automata in the functional context, along with their logic being affined to that of humans (yet virtually opaque), the notion of intelligence incurs significant de-humanization.

Keywords

• ontology
• logic
• theory of experience
• phenomenalism
• knowing
• fragmentary knowledge
• artificial intelligence
• intelligent automata
• machine learning
• neural networks
• de-humanization of knowledge