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AGI is defined by broad, human-level generalization across tasks, while narrow AI (ANI) is constrained to specific, well-defined capabilities.
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.
AGI is defined by broad, human-level generalization across tasks, while narrow AI (ANI) is constrained to specific, well-defined capabilities.
Common AGI traits include reasoning under uncertainty, knowledge representation, planning, learning, and natural-language communication; physical sensing/acting may also be important.
The AGI vs. narrow AI framing drives debates about feasibility, testing, and potential risks such as existential danger and large-scale unemployment.
A hypothetical AI system that can perform at or beyond human capability across virtually all cognitive tasks.
AI that is competent only in specific, well-defined tasks and lacks broad general cognitive abilities.
A hypothetical form of AGI that would outperform the best human abilities across essentially all domains by a wide margin.
A problem believed to require AGI to solve because it is beyond the capabilities of purpose-specific algorithms.
A test of machine intelligence based on whether a system can convince a human judge through natural-language conversation that it is human.
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