This study addresses the problems involving the adaptation of cybernetic models to operational realities. More precisely, three epistemological views are in turn investigated so as to determine the problems regarding information transfer between a model and the real world. Of the three epistemologies under investigation: realism, a priorism, and phenomenology, the latter demonstrates the most promise in terms of opening up operational possibilities for the model, but introduces problems involving the adaptation of the model to the reality.
Preface
The purpose of the present work is to discuss concepts underpinning the building of truly intelligent machines: to offer meaning to biological systems in psychological settings. Such an endeavor is ipso facto within the scope of Artificial Intelligence (AI), yet it is an area which remains largely ignored by AI researchers, efforts being instead concentrated on the development of faster and larger rule-based systems.
In tracing the emergence of AI-related paradigms one finds that world views have shifted from the Newtonian mechanics model of explaining phenomenon, to information theory, to the information processing level of modeling which presently characterizes cognitive science (McCorduck, 1979). Indeed with each shift in paradigm, cybernetics/AI research has turned to philosophy in order to secure a new paradigm. Once secured, however, the research strangely ceased further investigation into the philosophical grounds of the work at hand. Even though not sustained by research, it is this author's contention that such alienation hinders research in AI.
The information community would benefit greatly from such exposure since it may lead to rethinking some of the basic aspects of "intelligent" systems, which