Continual Learning for Enhancing Personal Assistants Using Machine Learning

Abstract

The ability to learn and adapt continuously is a hallmark of human intelligence, yet artificial intelligence (AI) systems often struggle with catastrophic forgetting, where new learning disrupts prior knowledge. This limitation is particularly critical for personal AI assistants, which must refine user preferences, adapt to new commands, and retain past interactions in order to deliver context-aware and personalized responses. To address this, we apply the Elastic Weight Consolidation (EWC) method to enhance continual learning in personal assistants. By selectively constraining important network parameters, EWC mitigates forgetting while enabling the integration of new information. Using the 16 Personality Types dataset from Kaggle, we compare EWC against established incremental and machine learning approaches, including BLSTM, LwF, iCARL, hybrid neural circuits, and gradient boosting models. Experimental results show that EWC achieves 98.4% test accuracy and 98.3 validation accuracy, outperforming competing methods, and significantly improving the scalability and adaptability of AI assistants. These findings highlight the potential of EWC as a practical solution for building more intelligent, user-centered personal assistants.