AI Intelligence Measure

An AI Intelligence Measure is an intelligence measure that quantifies specific cognitive capabilities, knowledge, or problem-solving abilities of artificial intelligence systems across various dimensions of intelligence.

• AKA: AI Cognitive Metric, Artificial Intelligence Assessment, AI Capability Measure, Machine Intelligence Metric.
• Context:
  • It can typically evaluate AI Cognitive Performance across multiple AI intelligence dimension.
  • It can typically provide AI Capability Benchmark for comparing AI system effectiveness.
  • It can typically assess AI Problem-Solving Ability in diverse AI task domain.
  • It can typically measure AI Learning Efficiency through standardized AI intelligence test.
  • It can typically track AI Progress Trajectory via longitudinal AI intelligence assessment.
  • ...
  • It can often distinguish between different AI Intelligence Type including fluid and crystallized capabilities.
  • It can often reveal AI Capability Gap through systematic AI intelligence evaluation.
  • It can often predict AI Task Performance based on relevant AI intelligence score.
  • It can often inform AI Development Priority through AI intelligence profiling.
  • ...
  • It can range from being a Narrow AI Intelligence Measure to being a General AI Intelligence Measure, depending on its AI assessment scope.
  • It can range from being a Static AI Intelligence Measure to being a Dynamic AI Intelligence Measure, depending on its AI temporal adaptability.
  • It can range from being a Qualitative AI Intelligence Measure to being a Quantitative AI Intelligence Measure, depending on its AI measurement approach.
  • It can range from being a Single-Task AI Intelligence Measure to being a Multi-Task AI Intelligence Measure, depending on its AI evaluation breadth.
  • ...
  • It can integrate with AI Benchmark Suite for comprehensive AI intelligence profiling.
  • It can support AI Safety Assessment through capability AI intelligence monitoring.
  • It can enable AI Model Selection based on task-specific AI intelligence requirement.
  • It can facilitate AI Research Direction via identified AI intelligence limitation.
  • It can guide AI Training Strategy through AI intelligence feedback.
  • ...
• Example(s):
  • Fluid AI Intelligence Measure, assessing novel problem-solving ability.
  • Crystallized AI Intelligence Measure, evaluating accumulated knowledge.
  • ARC-AGI Benchmark Score, measuring abstract reasoning capability.
  • MMLU Performance Metric, testing multi-domain understanding.
  • BigBench Intelligence Score, evaluating diverse cognitive tasks.
  • ...
• Counter-Example(s):
  • Training Loss Metric, which measures optimization progress not AI intelligence level.
  • Computational Efficiency Metric, which assesses resource usage rather than AI intelligence capability.
  • User Satisfaction Score, which evaluates subjective preference not objective AI intelligence.
• See: Intelligence Measure, AI Evaluation Metric, Cognitive Assessment, AI Benchmark, Machine Learning Metric, AI Capability Assessment, Performance Measure.

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