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articles/ai-services/personalizer/concept-auto-optimization.md

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Personalizer uses these reward estimations to decide whether to change the current Learning Settings for others. Each estimation is a distribution curve, with upper and lower 95% confidence bounds. Personalizer will only apply new Learning Settings if:
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* They showed higher average rewards in the evaluation period, AND
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* They have a lower bound of the 95% confidence interval, that is *higher* than the lower bound of the 95% confidence interval of the online Learning Settings.
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This criteria to maximize the reward improvement, while trying to eliminate the probability of future rewards lost is managed by Personalizer and draws from research in [Seldonian algorithms](https://aisafety.cs.umass.edu/overview.html) and AI safety.
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This criteria to maximize the reward improvement, while trying to eliminate the probability of future rewards lost is managed by Personalizer and draws from research in [Seldonian algorithms](https://aisafety.cs.umass.edu/index.html) and AI safety.
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#### Limitations of Auto-Optimize
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* [Offline evaluations](concepts-offline-evaluation.md)
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* [Learning Policy and Settings](concept-active-learning.md)
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* [How To Analyze Personalizer with an Offline Evaluation](how-to-offline-evaluation.md)
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* [Research in AI Safety](https://aisafety.cs.umass.edu/overview.html)
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* [Research in AI Safety](https://aisafety.cs.umass.edu/index.html)

articles/ai-services/personalizer/whats-new.md

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### 2019 Build videos
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* [Deliver the Right Experiences & Content like Xbox with Azure AI Personalizer](https://azure.microsoft.com/resources/videos/build-2019-deliver-the-right-experiences-and-content-with-cognitive-services-personalizer/)
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* [Deliver the Right Experiences & Content like Xbox with Azure AI Personalizer](https://www.youtube.com/watch?v=agaJr25Y8Ek)
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## Service updates
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