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Continual Learning Futures

Chart pathways beyond static language models by integrating continual learning, hybrid architectures, and on-the-job adaptation.

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Ethical and societal considerations

  • Accountability: Who is responsible when a continually learning system makes a new error? Define stewardship roles.
  • Transparency: Inform users when models adapt and how their data influences behavior.
  • Equity: Ensure adaptation pipelines do not reinforce biases by overfitting to vocal user segments.
  • Safety: Combine automated defenses with human oversight to prevent emergent harmful behaviors.
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