Lifelong Machine Learning

Lifelong machine learning (LML) aims to create AI systems that continuously learn and adapt throughout their operational lifetime, mimicking human learning's persistent and cumulative nature. Current research focuses on developing algorithms and architectures that mitigate "catastrophic forgetting" – the loss of previously acquired knowledge when learning new tasks – with techniques like continual learning strategies, novel optimizers (e.g., CoRe), and methods for generating synthetic training data. These advancements are crucial for building robust and adaptable AI systems applicable to diverse real-world scenarios, such as robotics, personalized medicine, and continuously evolving data streams.