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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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Infrastructure and tooling implications

  • Version control for models: Treat updates like software releases with semantic versioning, changelogs, and dependency tracking.
  • Resource allocation: Continual learning demands persistent training infrastructure; plan for compute dedicated to micro-updates.
  • Data governance: Maintain consent records for streaming data and allow individuals to revoke contributions.
  • Explainability: Log why updates were applied, which data triggered them, and how risks were mitigated.
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