Quo Vadis, Artificial Intelligence? A Neuro-Symbolic Approach to Artificial Intuition

While modern artificial intelligence excels at rational, data-driven tasks, it largely fails to replicate the efficiency and creativity of human intuition. Purely logical systems often struggle with ambiguity, context-switching, and the kind of non-obvious ”leaps” in reasoning that characterize expert decision-making. This paper introduces AI², a neuro-symbolic model designed to simulate artificial intuition as an emergent property of a dynamic knowledge system. Our model leverages a semantic network where knowledge is categorized as either factual (’existing’) or associative (’intuitive’). When presented with a natural language query, the system identifies multiple potential reasoning paths and evaluates them using a novel scoring mechanism that balances plausibility and creativity, based on the formalisms proposed by Olayinka (2020). We validate the model against a series of diverse queries, demonstrating its ability to perform multi-step causal inference, abstract reasoning, and robustly handle nonsensical inputs. The results show that by explicitly rewarding innovative paths that utilize associative knowledge, the model successfully exhibits flexible, context-aware, and human-like intuitive behavior, offering a promising direction for developing more agile and intelligent AI systems that bridge the gap between symbolic reasoning and sub-symbolic pattern recognition.