Towards a Proto Artificial General Intelligence: The Role of Large Language Model Ontologies in its Development

Authors

  • Christian E. Maldonado-Sifuentes Transdisciplinary Research for Augmented Innovation - Laboratory (TRAI-L)
  • Mariano Vargas-Santiago Consejo Nacional de Humanidades, Ciencias y Tecnologías (CONAHCYT-IxM)
  • Samuel Solis-Gamboa Consejo Nacional de Humanidades, Ciencias y Tecnologías (CONAHCYT-IxM)
  • Grigori Sidorov Instituto Politécnico Nacional
  • Luis Lechuga-Gutierrez Consejo Nacional de Humanidades, Ciencias y Tecnologías (CONAHCYT-IxM)
  • Francisco González-Andrade Consejo Nacional de Humanidades, Ciencias y Tecnologías (CONAHCYT-IxM)
  • María del Carmen Heras-Sánchez Universidad de Sonora

DOI:

https://doi.org/10.13053/cys-28-3-5200

Keywords:

Artificial General Intelligence, Large Language Models, Ontology, Hybrid Intelligent Systems, Multi-Agent Systems

Abstract

ProtoAGI aims to create a versatile artificial intelligence system capable of autonomously performing diverse tasks. A foundational element of ProtoAGI is the Large Language Model (LLM) ontology, which plays a crucial role in organizing and retrieving information about different LLMs, enabling the selection of the most appropriate model for specific tasks. This ontology, the first of several designed to support ProtoAGI, addresses key challenges in managing and accessing information regarding LLM capabilities, performance, and task suitability. We present the methodology for constructing this ontology, covering data extraction, enrichment, and model recommendation using a generalized LLM API. The initial version of this ontology involved processing over a million tokens, underscoring the system's complexity and the scale of information integrated. This ontology is designed for continuous updates, ensuring that ProtoAGI remains current with the latest advancements in LLMs. The ongoing development of this ontology marks a significant step in ProtoAGI's evolution, following an initial proof-of-concept demonstrated during the 2024 eclipse, where the feasibility of integrating such a comprehensive LLM ontology into a general-purpose AI system was shown. By making this ontology accessible to the broader AI community, we aim to accelerate further advancements in AGI research and applications.

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Published

2024-09-26

Issue

Vol. 28 No. 3 (2024): 28(3) 2024

Section

Articles

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