REVOLUTIONIZING MACHINE LEARNING WITH ODAM TILI THEORY: A NATURAL CODING PARADIGM FOR AI ADVANCEMENT
Abstract
Abstract: Recent developments in machine learning (ML) and artificial intelligence (AI) have achieved remarkable successes in tasks such as natural language processing (NLP), image recognition, and conceptual learning. However, current models remain limited by their reliance on vast labeled datasets, computationally intensive optimization, and abstract mathematical frameworks. This paper introduces Odam Tili (Human Language) theory, a groundbreaking linguistic paradigm that posits human language as a naturally evolved system of acoustic and semantic codes. By integrating Odam Tili principles into ML and AI, we propose a transformative approach to training models that reduces computational overhead, enhances generalization, and aligns AI with the natural efficiency of human cognition. Through an exploration of phonetic-semantic coding, hierarchical generational models, and diachronic linguistic evolution, we demonstrate how Odam Tili can redefine foundational AI architectures, yielding unprecedented advancements in NLP, multimodal learning, and reinforcement learning.
References
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