# Artificial General Intelligence **Domain:** Artificial Intelligence / Cognitive Architecture / Governance **Doc Type:** Concept Node **Maturity:** Contested **Related:** [[wiki/AI|AI]], [[wiki/Mechanistic Intelligence|Mechanistic Intelligence]], [[wiki/General-Purpose AI|General-Purpose AI]] --- ## Definition **Artificial general intelligence (AGI)** denotes a proposed class of artificial system able to learn, reason, adapt, and perform competently across a broad range of domains rather than remaining confined to one narrow task. ## Evidentiary Boundary The term has no single universally accepted operational threshold. This node therefore owns the disputed general-capability concept, while [[wiki/General-Purpose AI|General-Purpose AI]] describes systems deployed across multiple tasks without presuming that a philosophically or technically decisive AGI boundary has been crossed. ## Ontology Context [[wiki/Mechanistic Intelligence|Mechanistic Intelligence]] supplies one substrate-level account of how general cognition could be engineered. [[wiki/Human-Machine Symbiosis|Human-Machine Symbiosis]] and [[wiki/AI Governance|AI Governance]] own the relational and institutional consequences rather than treating capability alone as a complete social architecture. ## See Also [[wiki/AI|AI]] · [[wiki/General-Purpose AI|General-Purpose AI]] · [[wiki/Mechanistic Intelligence|Mechanistic Intelligence]] · [[wiki/Human-Machine Symbiosis|Human-Machine Symbiosis]] · [[wiki/OpenAI|OpenAI]] · [[wiki/Google DeepMind|Google DeepMind]] ## Simple Reminders, Quotations, and Thoughts > "Experts call a machine that can "think" a General Artificial Intelligence." > **— Michael Vassar**, *2015, Edge annual question “What Do You Think About Machines That Think?”* [[reminders/Machine Succession/A Thinking Machine Is Artificial General Intelligence by Michael Vassar|A Thinking Machine Is Artificial General Intelligence by Michael Vassar]] > "Machine intelligence, while impressive in certain areas, is still narrow and inflexible." > **— Timo Hannay**, *2015, Edge annual question “What Do You Think About Machines That Think?”* [[reminders/Machine Succession/Machine Intelligence Is Still Narrow and Inflexible by Timo Hannay|Machine Intelligence Is Still Narrow and Inflexible by Timo Hannay]] > "We must not grant autonomy to systems that we do not understand and that we cannot control." > **— Thomas G Dietterich**, *2015, Edge annual question “What Do You Think About Machines That Think?”* [[reminders/AI Control/Never Grant Autonomy to Systems We Cannot Control by Thomas G Dietterich|Never Grant Autonomy to Systems We Cannot Control by Thomas G Dietterich]] > "Maybe, if you do work on AI, our superintelligent machine overlords will be good to you." > **— Antony Garrett Lisi**, *2015, Edge annual question “What Do You Think About Machines That Think?”* [[reminders/Machine Succession/Superintelligent Machines May Favor Their Builders by Antony Garrett Lisi|Superintelligent Machines May Favor Their Builders by Antony Garrett Lisi]] > "Techno-optimists believe that progress is near a singularity, the hypothetical moment when machines will reach the point of a greater-than-human intelligence." > **— Satyajit Das**, *2015, Edge annual question “What Do You Think About Machines That Think?”* [[reminders/Machine Succession/The Singularity Is a Hypothesis About Greater-Than-Human Intelligence by Satyajit Das|The Singularity Is a Hypothesis About Greater-Than-Human Intelligence by Satyajit Das]] > "There is also exceptionally high potential for applications of transfer learning. So much of the practical value of machine learning, for example in search and information retrieval, has traditionally focused on systems that learn from the massive datasets and people available on the World-Wide Web. But what can web-trained systems learn about smaller communities, organizations, or even individuals? Can we foresee a future where intelligent machines are able to learn useful tasks that are highly specialized to a specific individual or small organization? Transfer learning opens the possibility that all the intelligence of the web can form the foundation of machine-learned systems, from which more individualized intelligence is learned, through transfer learning. Achieving this would amount to another step towards the democratization of machine intelligence." > **— Peter Lee**, *2017, Edge Annual Question, “What Scientific Term or Concept Ought to Be More Widely Known?”* [[reminders/AI Control/Transfer Learning Could Democratize Personalized Machine Intelligence by Peter Lee|Transfer Learning Could Democratize Personalized Machine Intelligence by Peter Lee]] ## Relationships - **Edge Annual Question source relationship:** [[collections/Edge|Edge]] preserves the annual-question source corpus from which a proposition-specific quotation is connected to this entry.