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Also known as: ASI, Superintelligence
Hypothetical AI that far surpasses human intelligence across all cognitive domains and tasks.
DESCRIPTION
Artificial super intelligence (ASI) is a speculative future AI that exceeds the cognitive performance of humans in virtually every domain. It is central to debates on long-term AI safety, alignment, and governance because misaligned superintelligence could pose systemic risks. While no empirical pathway to ASI is proven, discussions often consider recursive self-improvement, capability scaling, and emergent reasoning. Research in alignment, interpretability, and control frameworks is motivated by ASI risk scenarios. Timelines, feasibility, and controllability remain contested, with strong disagreement across the field.
ALTERNATIVE DEFINITIONS
Wikipedia
Superintelligence is a hypothetical agent that possesses intelligence far surpassing that of the brightest and most gifted human minds.
Emphasizes hypothetical agent that vastly exceeds human intelligence.
Future of Life Institute
Superintelligence refers to a hypothetical kind of agent that would be far more intelligent than the best human brains in practically every field, including scientific creativity, general wisdom and social skills.
Highlights breadth of domains and societal implications.
RELATED TERMS
Alignment Singularity Reinforcement Learning Safety Alignment Model Risk Management Machine Intelligence AI Governance Safety-Critical AI
COMPETING TERMS
Artificial General Intelligence Artificial Narrow Intelligence
RELATIONS
- Broader: Artificial Intelligence
KNOWLEDGE AREAS
TARGET AUDIENCE
AI Engineer MLOps Engineer AI Product Manager Chief Technology Officer Chief Data Officer Chief Information Officer Enterprise Architect Data Governance Manager Data Strategy Consultant
EXTERNAL SOURCES
- Wikipedia - Superintelligence
- NIST - AI risk management framework for high-stakes AI
- Oxford University Press - Seminal book on superintelligence risks
- arXiv - Recent AI safety and alignment research
TOP QUESTIONS
Why is Artificial Super Intelligence considered risky?
ASI could act with capabilities beyond human control, amplifying misalignment and societal risks. Without robust alignment, even well-intended goals might produce harmful outcomes. This motivates stringent governance and safety research.
Category: Risk
How does Artificial Super Intelligence differ from Artificial General Intelligence?
AGI aims for human-level capabilities across domains, whereas ASI exceeds human abilities significantly. ASI thus raises qualitatively different control and governance challenges beyond AGI.
Category: Distinction
Is Artificial Super Intelligence feasible with current methods?
There is no empirical proof it is feasible. Opinions differ widely; some expect capability scaling to continue, while others anticipate limits requiring fundamentally new breakthroughs.
Category: Opinion
What governance measures relate to Artificial Super Intelligence?
Measures include model evaluations, safety benchmarks, incident reporting, red-teaming, and standards. International coordination, transparency, and compute governance are also debated.
Category: Governance
What is the role of alignment in Artificial Super Intelligence?
Alignment seeks to ensure ASI optimizes for human values and legal norms. It involves preference learning, interpretability, and robust oversight mechanisms.
Category: Requirements
What is a common misconception about Artificial Super Intelligence?
A frequent misconception is that ASI is merely a faster AGI. In fact, qualitative differences in capability can enable unforeseen strategies and risks.
Category: Misunderstanding
What is a competing concept to Artificial Super Intelligence?
Artificial General Intelligence is a conceptual precursor focusing on human-level capability. Artificial Narrow Intelligence is today’s domain-specific AI.
Category: Alternatives
What’s currently a hot topic around Artificial Super Intelligence?
Evaluations for emergent capabilities, interpretability breakthroughs, frontier model governance, and international coordination are intensely discussed topics.
Category: Topic
Where should practitioners start learning about Artificial Super Intelligence?
Begin with foundational texts like Bostrom’s Superintelligence, then review AI risk frameworks (e.g., NIST AI RMF) and current alignment research on arXiv.
Category: Learning