I would like to know what approaches make an AI model dynamic-meaning it can adapt and improve after deployment. What technologies or learning methods enable continuous updating instead of fixed, one-time training?
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From what I’ve seen, reinforcement learning and continuous fine-tuning make a static AI model dynamic. They let the AI update itself based on new feedback instead of staying stuck with its original training.
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Technologies like online learning and reinforcement learning help models evolve. They allow AI to learn from real-time data and adapt quickly without needing a full retrain.