Continual Learning for Enhancing Personal Assistants Using Machine Learning

Humans and animals can learn new things, improve their skills, and share what they know with others for the rest of their lives. Neurocognitive systems that help with skill growth, memory consolidation, and adaptation are what make this lifelong learning process possible. For models that need to process and adapt to information that is always changing, lifelong learning is very important in artificial intelligence. But AI systems have trouble with “catastrophic forgetting,” which is when new information replaces old information. This makes adaptive learning very hard. Continuous learning is especially helpful for personal AI assistants since it lets them improve their understanding of user preferences, learn new tasks, and remember past encounters. This makes it easier for them to give personalized, context‐aware answers, which makes the user experience smooth and easy to understand. This paper suggests a new model of continual learning called the elastic prototype‐aligned contrastive (E‐PAC). The network’s settings were changed while it was learning so that it could better remember how to forecast existing categories while simultaneously learning new ones. The experiment has been set up, and the recommended method is tested against the existing continual learning technique, elastic weight consolidation (EWC) on the “16 personality type” dataset that is available on Kaggle. The experimental results demonstrate that the proposed E‐PAC model achieves a test accuracy of 98.9%, outperforming the baseline EWC method, which achieves 97.2% accuracy. This makes the classification and prediction model much more scalable and intelligent.