Let AI speak in its mother tongue (manidoraisamy.com)

Meta has announced innovative advancements in AI model capabilities, particularly with its Llama 3 model, which has shown to outperform larger models like Llama 2 by leveraging coding techniques to enhance reasoning abilities. This shift garnered attention across the AI community as it highlights the potential of teaching models to code—not just for syntax comprehension but as a method for abstract reasoning. The discussion also addresses the structural limitations of current transformer architectures that struggle with the hierarchical nature of code, suggesting that traditional linear tokenization leads to inefficiencies in reasoning tasks. To address these shortcomings, the essay proposes the use of Graph Transformers, which utilize structured representations, namely Abstract Syntax Trees (ASTs), to preserve the inherent hierarchy of code. This new approach aims to improve the accuracy of code generation by maintaining clear relationships between components, thereby enabling models to reason like software engineers rather than relying on execution calls to external Python interpreters. Additionally, the article outlines practical implementation challenges, including the transition from natural language to a more structured form called Attempto Controlled English and the computational demands of Graph Attention scaling, indicating a promising yet complex path forward for AI’s understanding of coding and reasoning.