What It Means for AI to Understand
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| Format: | Recurso digital |
| Sprache: | Englisch |
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Zenodo
2026
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| _version_ | 1866902037376008192 |
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| author | Adedoyin, Ifeoluwa james NORA Research Lab |
| author_facet | Adedoyin, Ifeoluwa james NORA Research Lab |
| contents | <p>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.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20225147 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | What It Means for AI to Understand Adedoyin, Ifeoluwa james NORA Research Lab Artificial intelligence Artificial Intelligence Machine Understanding Generalization Memorization vs Understanding Transformation Invariance Equivariance Cognitive Systems AI Safety <p>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.</p> |
| title | What It Means for AI to Understand |
| topic | Artificial intelligence Artificial Intelligence Machine Understanding Generalization Memorization vs Understanding Transformation Invariance Equivariance Cognitive Systems AI Safety |
| url | https://doi.org/10.5281/zenodo.20225147 |