Incremental learning is a paradigm in AI, particularly within ML, that focuses on the ability of models to learn continuously by updating their knowledge base with new data inputs over time. Unlike conventional batch learning methods where ML models are trained only once on a complete dataset, incremental learning enables models to adapt to new information as it becomes available, ensuring adaptability and efficiency in dynamic environments. This approach is essential for applications where data streams continuously or cannot be stored indefinitely, such as in online recommendation systems, anomaly detection, and robotics. The challenges of incremental learning include managing the stability-plasticity dilemma—balancing the retention of past knowledge with the acquisition of new information—and preventing catastrophic forgetting, where new learning could disrupt previously acquired knowledge.
The concept of incremental learning dates back to the early developments in AI and gained significant attention in the 1990s with the rise of adaptive systems and online learning algorithms. It became increasingly popular with the advancement of real-time data applications and streaming analytics in the 2010s.
Key contributors to the development of incremental learning include researchers like Robert M. French, who explored catastrophic forgetting in neural networks, and the community around adaptive learning algorithms, where significant progress was made in efficiently updating models without necessitating retraining from scratch.