Why Machine Learning won’t cut it

  • Each narrow application needs to be specially trained
  • Require large amounts of hand-crafted, structured training data
  • Learning must generally be supervised: Training data must be tagged
  • Require lengthy offline/ batch training
  • Do not learn incrementally or interactively, in real time
  • Poor transfer learning ability, re-usability of modules, and integration
  • Systems are opaque, making them very hard to debug
  • Performance cannot be audited or guaranteed at the ‘long tail’
  • They encode correlation, not causation or ontological relationships
  • Do not encode entities, or spatial relationships between entities
  • Only handle very narrow aspects of natural language
  • Not well suited for high-level, symbolic reasoning or planning



Entrepreneur, Futurist, AGI Developer: agi-3.com Aigo.ai

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