Semantic Emergence Modeling: How AI Systems Develop Higher-Level Understanding from Raw Data
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Abstract
The advancement of artificial intelligence has enabled systems to move beyond simple pattern recognition toward developing higher-level semantic understanding from raw data. This study investigates semantic emergence modeling, a framework in which AI systems extract latent concepts and relationships from unstructured and structured datasets to form coherent, abstract representations. By combining techniques from deep learning, representation learning, and knowledge graph construction, the framework allows AI to identify patterns, infer context, and generate emergent knowledge that supports decision-making, reasoning, and predictive analytics. Experimental evaluations on image, text, and multimodal datasets demonstrate that semantic emergence models outperform traditional feature-based AI systems in tasks requiring abstraction, analogy, and contextual reasoning. The results highlight the potential of semantic emergence modeling for applications in natural language understanding, scientific discovery, and autonomous systems. This research contributes to the field by formalizing a methodology for observing and quantifying emergent semantics in AI systems, offering insights into how machines can develop increasingly sophisticated cognitive representations from raw inputs.
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