AI Models Have an Expiry Date: Why Continual Learning is Essential

In the rapidly evolving world of artificial intelligence, one thing is certain: change is constant. As AI models become integral to various applications, the need for adaptive learning approaches like Continual Learning (CL) becomes more apparent. This newsletter explores why AI models have an “expiry date” and how CL can address this challenge.

The Problem: AI Models and the Changing World

Imagine you have a small robot designed to navigate your garden and water plants. Initially, you train this robot with data collected over a few weeks, teaching it to operate efficiently in a garden covered with grass and bare soil. However, as the seasons change and flowers bloom, the garden’s appearance transforms significantly. The robot, trained on outdated data, struggles to recognize its surroundings and perform its tasks effectively. This scenario illustrates the problem of AI models that become obsolete when faced with new, unseen data.

The Traditional Approach: Retraining from Scratch

One might consider adding new data examples to retrain the model from scratch. However, this approach is costly and impractical whenever the environment changes. Furthermore, retaining all historical training data for…