What It Means for AI to Understand
Year: 2026
Type: article
Abstract: This paper explores a fundamental question in artificial intelligence: what does it actually mean for a machine to understand something rather than simply memorize patterns? It introduces a formal framework for distinguishing genuine comprehension from statistical recall using transformation invariance and generalization tests. The paper argues that true understanding is revealed when an AI system can preserve performance across meaningful changes in context, representation, and structure. It further proposes mathematical criteria for evaluating understanding through equivariance and robustness. Beyond intelligence itself, the framework is extended to AI safety and value alignment, showing that capability alone is insufficient without stable alignment to human objectives. The result is a unified theory connecting machine understanding, generalization, and beneficial AI behavior.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7161389539
FWCI:
Cites:
Cited by:
Related to: