Liquid Neural Network: Pioneering Adaptive Intelligence

Dynamic Neural Network with Real-Time Adaptation to Changing Data Patterns

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As an AI/ML user or practitioner, you must have encountered scenarios in which an effective and performant AI/ML model deteriorates in performance over time. This phenomenon, known as concept drift, occurs when the underlying data distribution changes. This is the new data to which the model was not exposed as part of the training, making the model’s predictions less reliable and accurate.

How do we address the Concept Drift in the AI/ML model?

To address concept drift in your model, you can

• Retrain the model periodically using the most recent data; this will allow the model to understand and adjust to the new data patterns.

Retraining the model is similar to a human learning new skills and knowledge to stay up-to-date.

• Incremental learning occurs when the model incrementally learns and updates its knowledge by learning from new data without a need to retrain on the entire dataset. As the model is exposed to new data, it updates its internal parameters to accommodate the new patterns and relationships while retaining knowledge from previously seen data.