Recent discussions in the field of artificial intelligence highlight advancements in neural networks and continual learning methodologies. A new approach called 'Deep Unlearning' has been introduced, which offers a fast and efficient gradient-free method for class forgetting. This technique aims to enhance the ability of neural networks to learn continuously without the issue of catastrophic forgetting. Additionally, research on 'Learning to Learn without Forgetting using Attention' emphasizes the importance of attention mechanisms in improving learning efficiency. Another contribution focuses on practical tools for utilizing large language models in continual learning contexts, underscoring the growing interest in developing systems that can adapt and learn over time without losing previously acquired knowledge.