Human-Level Machine Intelligence Concept

A Human-Level Machine Intelligence Concept is a machine intelligence concept that is an artificial general intelligence benchmark defining cognitive capability equivalence with human intelligence.

• AKA: Human-Level AI, AGI Benchmark, Human-Equivalent Machine Intelligence, HLMI.
• Context:
  • It can typically encompass Human-Level Machine Intelligence Capabilities across human-level machine intelligence domains.
  • It can typically require Human-Level Machine Intelligence Architectures supporting human-level machine intelligence generalization.
  • It can typically enable Human-Level Machine Intelligence Tasks matching human-level machine intelligence performance.
  • It can typically demonstrate Human-Level Machine Intelligence Learning with human-level machine intelligence adaptation.
  • It can typically exhibit Human-Level Machine Intelligence Reasoning through human-level machine intelligence inference.
  • ...
  • It can often serve as Human-Level Machine Intelligence Milestone for human-level machine intelligence researchers.
  • It can often trigger Human-Level Machine Intelligence Transition to human-level machine intelligence superintelligence.
  • It can often inform Human-Level Machine Intelligence Timelines in human-level machine intelligence predictions.
  • It can often shape Human-Level Machine Intelligence Investment in human-level machine intelligence development.
  • ...
  • It can range from being a Narrow Human-Level Machine Intelligence Concept to being a Broad Human-Level Machine Intelligence Concept, depending on its human-level machine intelligence scope.
  • It can range from being a Weak Human-Level Machine Intelligence Concept to being a Strong Human-Level Machine Intelligence Concept, depending on its human-level machine intelligence consciousness requirement.
  • It can range from being a Specialized Human-Level Machine Intelligence Concept to being a General Human-Level Machine Intelligence Concept, depending on its human-level machine intelligence domain coverage.
  • It can range from being a Current Human-Level Machine Intelligence Concept to being a Enhanced Human-Level Machine Intelligence Concept, depending on its human-level machine intelligence baseline.
  • ...
  • It can be predicted by Ray Kurzweil for 2029.
  • It can precede technological singularity in singularity theory.
  • It can be measured by Turing Test and AGI benchmarks.
  • It can drive AI safety research and alignment problems.
  • It can influence AI governance policy and regulation frameworks.
  • ...
• Example(s):
  • Timeline-Based Human-Level Machine Intelligence Predictions, such as:
    • Kurzweil's 2029 HLMI Prediction based on computational capacity growth.
    • Metaculus AGI Timeline aggregating expert predictions.
    • OpenAI's AGI Mission targeting beneficial AGI development.
  • Benchmark-Based Human-Level Machine Intelligence Definitions, such as:
    • Turing Test HLMI requiring indistinguishable conversation.
    • Coffee Test HLMI performing everyday physical tasks.
    • Economic HLMI achieving human job performance.
  • Component-Based Human-Level Machine Intelligence Models, such as:
    • Cognitive Architecture HLMI implementing human cognitive functions.
    • Neural Network HLMI replicating brain computation.
    • Hybrid HLMI combining symbolic and subsymbolic approaches.
  • ...
• Counter-Example(s):
  • Narrow AI Systems, which excel in specific domains without general intelligence.
  • Superintelligence Concepts, which exceed rather than match human capability.
  • Animal-Level Intelligences, which fall below human cognitive threshold.
• See: Artificial General Intelligence, Ray Kurzweil, Technological Singularity, Turing Test, Machine Intelligence, Superintelligence, AI Development Timeline, Stanford AI Symposium 2000 Event.

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