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Artificial general intelligence (AGI) is a hypothetical type of AI that matches or surpasses human capabilities across virtually all cognitive tasks. Unlike narrow AI (ANI), which is limited to well-defined tasks, an AGI system would be able to generalize knowledge, transfer skills between domains, and solve novel problems without task-specific reprogramming. Beyond AGI, artificial superintelligence (ASI) is envisioned as AI that outperforms the best human abilities across every domain by a wide margin. The scope of AGI is often described through its expected capabilities and the lack of a single universally agreed definition of “intelligence” for computers. Commonly cited intelligence traits include reasoning and strategy under uncertainty, representing knowledge (including common sense), planning, learning, and communicating in natural language, with the ability to integrate these skills to achieve goals. Additional desirable “physical” traits may include sensing (e.g., seeing or hearing) and acting (e.g., moving, manipulating objects, and responding to hazards). Because no definitive definition exists, researchers also discuss tests aimed at human-level intelligence—such as the Turing test, the Ikea test, and other practical benchmarks—and debate how well these measures capture true AGI.
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Artificial general intelligence (AGI) is a hypothetical AI system that can match or surpass human capabilities across essentially all cognitive tasks. Unlike artificial narrow intelligence (ANI)—which is limited to well-defined tasks—AGI is expected to generalize knowledge, transfer skills between domains, and solve novel problems without task-specific reprogramming. AGI is sometimes discussed alongside artificial superintelligence (ASI), which would outperform humans by a large margin, and with “transformative AI,” which emphasizes broad societal impact. The article notes that there is no single agreed-upon definition of “intelligence” for computers, but researchers commonly associate AGI with abilities such as reasoning and strategy under uncertainty, representing knowledge (including common sense), planning, learning, and communicating in natural language, potentially integrating these skills toward any goal. It also describes tests aimed at “human-level” intelligence (e.g., the Turing test, Ikea test, and Coffee test) and “AI-complete” problems—tasks believed to require AGI rather than specialized algorithms. Finally, the AGI-versus-narrow-AI distinction connects to feasibility debates and risk discussions. While many researchers expect strong AI/AGI eventually, timelines and definitions remain contested, and some argue that AGI could pose existential risks or other harms (including loss of control and mass unemployment). Others are skeptical, suggesting AGI is too distant or that current concerns may distract from nearer-term issues related to existing narrow AI systems.
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