Elementary epistemological features of machine intelligence

TL;DR

This paper provides a theoretical framework for understanding machine intelligence through canonical definitions, epistemological features, and algebraic measures, aiming to unify AI theory with philosophical epistemology.

Contribution

It introduces canonical definitions and algebraic measures for MI, linking AI epistemology with Hegelian monism to enhance theoretical understanding.

Findings

Defines key epistemological features of MI

Establishes algebraic measures of test quality

Connects MI learning with Hegelian epistemology

Abstract

Theoretical analysis of machine intelligence (MI) is useful for defining a common platform in both theoretical and applied artificial intelligence (AI). The goal of this paper is to set canonical definitions that can assist pragmatic research in both strong and weak AI. Described epistemological features of machine intelligence include relationship between intelligent behavior, intelligent and unintelligent machine characteristics, observable and unobservable entities and classification of intelligence. The paper also establishes algebraic definitions of efficiency and accuracy of MI tests as their quality measure. The last part of the paper addresses the learning process with respect to the traditional epistemology and the epistemology of MI described here. The proposed views on MI positively correlate to the Hegelian monistic epistemology and contribute towards amalgamating idealistic deliberations with the AI theory, particularly in a local frame of reference.