Building A Unified AI-centric Language System: analysis, framework and future work

TL;DR

This paper proposes a unified AI-centric language system designed to improve efficiency, reduce biases, and enhance clarity in AI communication by translating natural language into a streamlined, unambiguous format, supported by a proposed framework and future validation plans.

Contribution

It introduces a novel framework for translating natural language into an AI-friendly language, addressing biases and inefficiencies in current models, inspired by emergent communication systems and constructed languages.

Findings

Analysis of natural language limitations in AI models

Proposal of a translation framework for AI-friendly language

Outline of empirical validation pathway

Abstract

Recent advancements in large language models have demonstrated that extended inference through techniques can markedly improve performance, yet these gains come with increased computational costs and the propagation of inherent biases found in natural languages. This paper explores the design of a unified AI-centric language system that addresses these challenges by offering a more concise, unambiguous, and computationally efficient alternative to traditional human languages. We analyze the limitations of natural language such as gender bias, morphological irregularities, and contextual ambiguities and examine how these issues are exacerbated within current Transformer architectures, where redundant attention heads and token inefficiencies prevail. Drawing on insights from emergent artificial communication systems and constructed languages like Esperanto and Lojban, we propose a framework that translates diverse natural language inputs into a streamlined AI-friendly language, enabling more efficient model training and inference while reducing memory footprints. Finally, we outline a pathway for empirical validation through controlled experiments, paving the way for a universal interchange format that could revolutionize AI-to-AI and human-to-AI interactions by enhancing clarity, fairness, and overall performance.