The loss of plasticity in deep learning poses significant challenges for real-world applications that demand continuous learning and adaptation, such as autonomous systems or lifelong learning agents. Without the ability to retain flexibility and adapt to new information, deep neural networks may struggle to incorporate new knowledge or skills, leading to performance degradation over time. In autonomous systems, this could result in decreased efficiency, safety risks, or inability to handle evolving environments. For lifelong learning agents, the inability to maintain plasticity may hinder their ability to acquire new tasks or knowledge without forgetting previously learned information. Addressing the loss of plasticity in deep learning is crucial for ensuring the long-term effectiveness and reliability of intelligent systems operating in dynamic and changing environments.