Deep Learning as ETI Equivalent

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• ETIs are the evolutionary equivalent of deep learning: (i) required functional relationships encode non-decomposable functions, (ii) these are enacted by basal cognition mechanisms, and (iii) conditions for deep model induction predict ETI occurrence.hypothesis0.845
  Overarching three-part hypothesis stated in introduction
• ETIs are described by conditions necessary for deep learning / non-decomposable function learninghypothesis0.818
• Deep Learningconcept0.772
  Learning hierarchical representations of non-decomposable functions; proposed as formal equivalent to ETI process.
• Empathic Deep Q-Learning (DQN)framework0.742
  Related technique that mitigates harmful behaviors by simulating another agent's perspective, but relies on hand-coded mechanisms
• Deep Reinforcement Learningmethod0.738
  AI training method inspired by behaviorism, used for autonomous cars and drones; cited as bioinspired success
• H2: The conditions necessary for the induction of deep models, familiar in connectionist models of learning and cognition, are predictive of the conditions necessary for an ETI to occur.hypothesis0.734
  Second hypothesis linking learning theory directly to evolutionary transitions
• Deep adaptationconcept0.731
  The ultimate goal of participation: an environment so deeply fitted to its users that genuine satisfaction and life emerge
• Electronic Data Interchange (EDI)concept0.729
  Existing approach to standardized electronic communication between organizations; Elephant aimed at improving beyond fixed formats like X12.