Knowledge, Reasoning, and Semantics in AI

How do AI systems represent the world and reason about it? This sub-article expands Section 1.2 of Philosophy.

The Grounding Problem

• How do symbols connect to the world?
• Harnad’s symbol grounding problem asks how arbitrary tokens acquire meaning.
• Modern models learn from text, which is itself grounded only in data—raising questions about genuine understanding.

The Frame Problem

• How does a system know what to update when the world changes?
• Relevant to planning, robotics, and world models.

Embodiment

• Brooks argued intelligence without representation—behavior through interaction.
• Embodied systems use perception and action to bootstrap meaning.

Representation vs. Prediction

• Symbolic AI used explicit representations; deep learning uses learned latent representations.
• Whether these are “semantics” or mere statistics is debated.

Why It Matters

The nature of AI “understanding” determines trust, explainability, and generalization.

Sources

• Harnad, S. (1990). The Symbol Grounding Problem.
• Brooks, R. (1991). Intelligence Without Representation.

Back to Philosophy.