Lifelong Learning

Lifelong learning in artificial intelligence aims to create systems that continuously learn and adapt to new information without forgetting previously acquired knowledge, mirroring human learning capabilities. Current research focuses on developing algorithms and model architectures, such as those based on transformers, recurrent networks, and mixture-of-experts models, that address the "catastrophic forgetting" problem inherent in traditional machine learning approaches. This field is crucial for advancing robotics, autonomous systems, and natural language processing, enabling more robust and adaptable AI agents capable of operating in dynamic and unpredictable environments. The development of effective lifelong learning methods is also driving innovation in areas like continual learning benchmarks and efficient knowledge transfer strategies.