Algorithms of artificial intuition for implementation of strong ai

Abstract

Purpose. Intuition and Logic are two strategies for prediction and problem solving. Most humans have not been taught logical thinking, but most humans are still intelligent. Contrary to the majority view, it is implausible that the brain should be based on Logic. How does Intuition work? I believe intelligence emerges from millions of nested micro-intuitions, and that Artificial Intelligence requires Artificial Intuition. It is necessary to introduce Human-like Intuition Mechanism into Artificial Intelligence. The aim of this work is the development of constructive algorithms for artificial intuition. Methodology. Modeling the work of intuition is proposed on the basis of the Limiting-Generalizations Paradigm (LGP). Findings. The key components of the intuition model are: basic entities of the LGP; a task-inductor space, event space, an "artificial connectom", coherence mechanisms; Thin Slices. Originality. With the help of formal models and constructive algorithms, it is shown that the basis for rapid cognition and intuition is the adaptive unconscious - the thought process that works automatically when we have relatively little information to make a decision. These models form a new approach to the concept of "Strong AI". Practical value. The proposed model is the methodological basis for creating promising IT, as well as intuitive agents, robots.