The database in Artificial Research by Deduction in the Global Artificial Intelligence

This paper presents the conceptual and structural foundations of the database used in Artificial Research by Deduction within the framework of Global Artificial Intelligence (GAI). As the initial and essential stage in the automated scientific research process, the database takes the form of a comprehensive global matrix, designed to integrate all quantifiable factors relevant to human and natural systems—ranging from population demographics and economic indicators to planetary dynamics and astronomical phenomena. The database is initially constructed from a wide array of national, continental, and international data sources, including unstructured datasets and specific matrices derived from early experiments in Specific Artificial Intelligence for Artificial Research by Deduction (SAI-D). Through a rigorous process of standardization, this information is restructured into a unified matrix organized hierarchically into factors, sub-factors, and potentially sub-sub-factors, each defined in quantitative terms and capable of providing a continuous flow of data. This standardized global matrix serves as the foundational substrate for further stages in the Artificial Research by Deduction process. In the subsequent replication stage, robotic systems populate the matrix with real-time data flows, enabling deductive algorithms and GAI itself to identify potential mathematical relationships. These relationships are evaluated as empirical hypotheses and, if deemed rational, inform the construction of models that feed into GAI’s broader modelling and decisional systems. Ultimately, the database is not merely a passive repository of information, but an active and evolving structure that supports rational hypothesis generation, model construction, decision-making, and systemic self-improvement. It serves as the empirical backbone for a unified artificial rationality capable of supporting the management and ethical guidance of global systems.