Artificial empirical hypothesis

This paper introduces the concept of the Artificial Empirical Hypothesis as a foundational element in the development of autonomous scientific reasoning within Artificial Intelligence. It explores how AI systems, particularly within the framework of Global Artificial Intelligence (GAI), can formulate, test, and refine empirical hypotheses through artificial research processes—both by Deduction and by Application. The paper outlines how AI can simulate empirical reasoning by integrating categorical databases with deductive logic, enabling the construction of hypotheses based on structured observations and automated model-building. Emphasis is placed on the epistemological status of these hypotheses, their validity in scientific research, and their capacity for self-correction through feedback loops. By bridging empirical data with internal rational architectures, the Artificial Empirical Hypothesis becomes a critical interface between observed reality and synthetic cognition. This marks a shift toward a machine-led model of scientific inquiry, where artificial systems generate and evaluate knowledge with increasing autonomy and methodological coherence.