Which statement best distinguishes weak or narrow AI from Artificial General Intelligence (AGI)?

• A. Weak AI is designed for specific tasks, while AGI can understand, learn, and apply intelligence across a wide range of tasks.
• B. Weak AI is capable of general understanding; AGI is task-specific.
• C. AGI relies on predefined rules; weak AI learns from data.
• D. Weak AI can perform across many domains; AGI is limited to one domain.

Answer: (A)

The idea being tested is how the scope and flexibility of an AI system differ between narrow (weak) AI and Artificial General Intelligence. Narrow AI is built to excel at a specific task or a limited set of tasks; it doesn’t transfer its understanding well to unrelated areas. In contrast, Artificial General Intelligence would be capable of understanding, learning, and applying intelligence across a wide range of tasks, much like a human can.

That makes the best statement the one that says narrow AI focuses on specific tasks while AGI can understand and operate across many domains. It captures the core distinction: limited, task-focused performance versus broad, adaptable intelligence.

Context helps: today’s AI systems are largely narrow—think of language assistants, image recognizers, or game-playing programs that perform superbly in their own domains but struggle outside them. AGI, if realized, would be able to tackle novel problems, transfer knowledge from one area to another, and reason in more general ways.

The other options misstate the relationship. One claim suggests narrow AI has general understanding, which isn’t true; another implies AGI relies on predefined rules while narrow AI learns from data, but in practice both rely on data and learning, and AGI would imply flexible, wide-ranging learning rather than a fixed rule set. The last option reverses the domain breadth of narrow AI and AGI, which doesn’t fit how these concepts are defined.