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Feedback & Continuous Improvement: The Autonomy of Unsupervised Learning Meets the Precision of Supervised Models

Traditionally, supervised machine learning models required consistent oversight and manual intervention by data scientists to validate and correct annotations. This process ensured the model’s accuracy but was labor-intensive and time-consuming.

Enter AI Autopilot’s revolutionary Feedback & Continuous Improvement feature, a game-changer that dramatically simplifies this task. With AI Autopilot, our system effectively bridges the gap between supervised and unsupervised learning models. It provides the high accuracy of supervised models but with the operational efficiency of unsupervised ones.

Here’s how it works:

  1. User Feedback On-the-Fly: While interacting with the system, users can instantly provide feedback on document classifications and data extractions.
  2. Automated Training and Adjustments: This feedback is autonomously captured and processed by AI Autopilot. Based on the impact of the feedback on the model’s performance, the system will autonomously perform data and visual augmentations to maintain high performance levels.
  3. Continuous Improvement: Over time, the system’s performance not only maintains its high level of accuracy but actually improves, thanks to the autonomous learning features of AI Autopilot.


By autonomously making targeted adjustments based on user feedback, AI Autopilot ensures that your model maintains its high performance levels and adapts to new challenges, all without the need for manual oversight.

For deeper insights, be sure to read our accompanying articles on Data Acquisition & Annotation Performance and Privacy and Ethical Considerations.

Ready to Experience AI Autopilot?

If you’re intrigued by the potential of AI Autopilot and wish to witness firsthand how it can transform your business, don’t hesitate to contact us.

Schedule a demo today to see the future of data acquisition and annotation in action.