Assessment of cognitive characteristics in intelligent systems and predictive ability
This work addresses the problem of evaluating cognitive characteristics in intelligent systems for researchers and developers, but it appears incremental as it builds on existing concepts like weak vs. strong AI without introducing new methods or data.
The article proposes a universal dual-axis scale for assessing intelligent systems by considering their properties within an evolving environmental context, highlighting how anticipatory ability modulates their 'brute force' and how task complexity and critical assessment determine the optimal use of cognitive tools.
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.