AI Learning Paradigm

A AI Learning Paradigm is a learning approach that defines how AI systems acquire and process knowledge.

• Context:
  • It can typically define how AI systems interact with their AI environments.
  • It can typically determine the AI data sources used for AI training.
  • It can typically influence the AI learning mechanisms employed by AI systems.
  • It can typically shape the AI capability ceilings and AI limitations of resulting AI models.
  • It can typically establish the knowledge acquisition methods that guide AI development.
  • ...
  • It can often govern the feedback mechanisms that enable AI improvement.
  • It can often dictate the evaluation metrics used to measure AI performance.
  • It can often determine the generalization capability of the resulting AI system.
  • ...
  • It can range from being a Simulation-Based AI Learning Paradigm to being an Experience-Based AI Learning Paradigm, depending on its knowledge acquisition approach.
  • It can range from being a Supervised AI Learning Paradigm to being a Self-Directed AI Learning Paradigm, depending on its learning control mechanism.
  • ...
• Examples:
  • AI Learning Paradigm Evolution Periods, such as:
    • AI Simulation Era (2010-2019), characterized by AI systems learning within constrained simulation environments with clearly defined rewards.
    • Human Data Era (2019-2024), characterized by AI systems learning from large-scale human-generated data corpuses.
    • AI Experience Era (emerging 2024-), characterized by AI systems learning through continuous interaction with real-world environments.
  • AI Learning Paradigm Implementations, such as:
    • Reinforcement Learning AI Paradigm for AI systems learning through trial and error interaction.
    • Supervised Learning AI Paradigm for AI systems learning from labeled examples.
    • Self-Supervised Learning AI Paradigm for AI systems deriving learning signals autonomously from data structure.
  • ...
• Counter-Examples:
  • AI Architecture, which specifies the AI structure but not its AI learning approach.
  • AI Training Dataset, which is a component used within an AI learning paradigm but not a paradigm itself.
  • AI Deployment Strategy, which concerns how AI is implemented rather than how it learns.
• See: Machine Learning Approach, AI Training Methodology, Learning Framework, Knowledge Acquisition System.

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