Which statement best captures a common critique of purely look-up table AI?

Which statement best captures a common critique of purely look-up table AI?

• A. It can acquire genuine understanding through fixed mappings.
• B. It requires embodiment to understand language.
• C. It can generate truly novel insights without learning.
• D. It cannot have genuine understanding because it relies on fixed mappings without internal meaning or learning.

Answer: (D)

The main idea here is that a purely look-up table AI cannot have genuine understanding because it relies on fixed mappings without any internal meaning or learning. When an AI is built as a giant table of input-output pairs, it simply reproduces precomputed responses for exactly those inputs it has seen. There’s no interpretation happening, no flexible reasoning, and no way to form internal representations that capture what things mean. Because there’s no mechanism to learn from new experiences or to ground language and concepts in real-world meaning, the system can’t genuinely understand beyond parroting stored patterns. That’s why this critique targets look-up table approaches: the fixed mappings limit understanding to what’s already been encoded, with no capacity to develop or adapt meaning over time. The other statements touch on related ideas, but they don’t capture the core limitation the critique emphasizes—the absence of internal meaning and learning due to fixed mappings.

The main idea here is that a purely look-up table AI cannot have genuine understanding because it relies on fixed mappings without any internal meaning or learning. When an AI is built as a giant table of input-output pairs, it simply reproduces precomputed responses for exactly those inputs it has seen. There’s no interpretation happening, no flexible reasoning, and no way to form internal representations that capture what things mean. Because there’s no mechanism to learn from new experiences or to ground language and concepts in real-world meaning, the system can’t genuinely understand beyond parroting stored patterns.

That’s why this critique targets look-up table approaches: the fixed mappings limit understanding to what’s already been encoded, with no capacity to develop or adapt meaning over time. The other statements touch on related ideas, but they don’t capture the core limitation the critique emphasizes—the absence of internal meaning and learning due to fixed mappings.