Assessment of cognitive characteristics in intelligent systems and predictive ability

TL;DR

This paper introduces a dual-axis assessment scale for intelligent systems that considers environmental context, anticipatory ability, and cognitive complexity to evaluate their problem-solving effectiveness.

Contribution

It presents a novel universal assessment scale that accounts for environmental dynamics and cognitive factors influencing intelligent system performance.

Findings

The scale incorporates environmental, anticipatory, and cognitive complexity factors.

It emphasizes the role of 'common sense' in problem-solving.

The assessment links problem-solving success with temporal and contextual appropriateness.

Abstract

The article proposes a universal dual-axis intelligent systems assessment scale. The scale considers the properties of intelligent systems within the environmental context, which develops over time. In contrast to the frequent consideration of the 'mind' of artificial intelligent systems on a scale from 'weak' to 'strong', we highlight the modulating influences of anticipatory ability on their 'brute force'. In addition, the complexity, the 'weight' of the cognitive task and the ability to critically assess it beforehand determine the actual set of cognitive tools, the use of which provides the best result in these conditions. In fact, the presence of 'common sense' options is what connects the ability to solve a problem with the correct use of such an ability itself. The degree of 'correctness' and 'adequacy' is determined by the combination of a suitable solution with the temporal characteristics of the event, phenomenon, object or subject under study.