REVOLUTIONIZING MACHINE LEARNING WITH ODAM TILI THEORY: A NATURAL CODING PARADIGM FOR AI ADVANCEMENT

Abstract

Recent advancements in machine learning (ML) and artificial intelligence (AI) have achieved impressive feats in areas like natural language processing (NLP), image recognition, and conceptual learning. However, current models are held back by their reliance on massive labeled datasets, computationally heavy optimization, and abstract mathematical frameworks. This paper introduces the Odam Tili (Human Language) theory, a groundbreaking linguistic paradigm that views 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, improves generalization, and aligns AI with the natural efficiency of human cognition. Through an exploration of phonetic-semantic coding, hierarchical generational models, and the evolution of language over time, we show how Odam Tili can redefine foundational AI architectures, leading to unprecedented progress in NLP, multimodal learning, and reinforcement learning.