From Human to Artificial Intuition: Transcribing Instinct in AI Agents

Nowadays, it is an indisputable fact that the ever-growing Artificial Intelligence (AI) is one of the most effective applied technologies ever developed, which can be attributed to its inherent trait of making decisions based on real-time information, with data collected from sensors and a wide range of sources within a fraction of a second. As their storage systems are radically advancing, the processing and analyzing speed of such algorithms has now become faster than ever, pushing the horizons of what we have until now considered possible. Nonetheless, AI might be losing a part of what it could eventually evolve into. Despite being framed as ‘‘mythical’’ or ‘‘irrational’’, Intuition is an essentially significant characteristic of humans, introducing new perspectives to their problem-solving abilities and enhancing their decision-making skills; therefore, it would be highly intriguing to incorporate this concept into Artificial Intelligence systems. In this paper, the fundamental characteristics of the intuitive mechanism have been defined, and novel social research assessing the significance of Intuition for the strategic decision-making skills of individuals from different backgrounds was carried out. The results of the study assessed the predisposition of different demographic groups to use Rationality or Intuition to make decisions, but also revealed the correlations between these two Thought Systems and the Intuition Sub-Types. Finally, discussing the further implications of this study, a theoretical framework was developed to approach how each Intuition Sub-Type can be artificially imitated in AI agents, while simultaneously considering the potential ethical or social implications of such an integration.

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Publication Details

Journal
Journal of Student Research
Published
2025-02-28
DOI
https://doi.org/10.47611/jsrhs.v14i1.8938
Primary Topic
Evolutionary Algorithms and Applications
Type
article
Field-Weighted Citation Impact
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article

From Human to Artificial Intuition: Transcribing Instinct in AI Agents

ANTONIOS MISTHOS, Markos Hatzitaskos
Journal of Student Research
Evolutionary Algorithms and Applications
article

From Human to Artificial Intuition: Transcribing Instinct in AI Agents

ANTONIOS MISTHOS, Markos Hatzitaskos
article en

Abstract

Nowadays, it is an indisputable fact that the ever-growing Artificial Intelligence (AI) is one of the most effective applied technologies ever developed, which can be attributed to its inherent trait of making decisions based on real-time information, with data collected from sensors and a wide range of sources within a fraction of a second. As their storage systems are radically advancing, the processing and analyzing speed of such algorithms has now become faster than ever, pushing the horizons of what we have until now considered possible. Nonetheless, AI might be losing a part of what it could eventually evolve into. Despite being framed as ‘‘mythical’’ or ‘‘irrational’’, Intuition is an essentially significant characteristic of humans, introducing new perspectives to their problem-solving abilities and enhancing their decision-making skills; therefore, it would be highly intriguing to incorporate this concept into Artificial Intelligence systems. In this paper, the fundamental characteristics of the intuitive mechanism have been defined, and novel social research assessing the significance of Intuition for the strategic decision-making skills of individuals from different backgrounds was carried out. The results of the study assessed the predisposition of different demographic groups to use Rationality or Intuition to make decisions, but also revealed the correlations between these two Thought Systems and the Intuition Sub-Types. Finally, discussing the further implications of this study, a theoretical framework was developed to approach how each Intuition Sub-Type can be artificially imitated in AI agents, while simultaneously considering the potential ethical or social implications of such an integration.

Journal of Student ResearchVol. 14(1)
Athens Technical College (US)
Openalex Percentile: Top 89%
Evolutionary Algorithms and Applications
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