Assessment of cognitive characteristics in intelligent systems and predictive ability
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 8 of its 102 references that are in the map. First posted on arXiv 16 Sep 2022.
Builds on
8 of 102 references.
References · 8
- 2020Artificial Intelligence, Values and Alignmentcited by 443
- 2019Interpretable machine learning: definitions, methods, and applicationscited by 305
- 2019Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AIcited by 79
- 2021AutoDS: Towards Human-Centered Automation of Data Sciencecited by 32
- 2019AutoAIViz: Opening the Blackbox of Automated Artificial Intelligence with Conditional Parallel Coordinatescited by 24
- 2020Fairness in Streaming Submodular Maximization: Algorithms and Hardnesscited by 23
- 2019Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defensescited by 8
- 2020Three-stage intelligent support of clinical decision making for higher trust, validity, and explainabilitycited by 5
Built on by
Not cited yet.