meta-learning
Meta-learning, also known as learning to learn, is a subfield of artificial intelligence and machine learning that focuses on developing algorithms and models capable of learning how to learn more efficiently and effectively. The goal of meta-learning is to design systems that can generalize and adapt to new tasks with minimal data or training.
In meta-learning, models are trained on a variety of tasks in such a way that they can rapidly adapt to new, unseen tasks by leveraging the knowledge and experiences gained from previous tasks. The model learns high-level strategies or algorithms that help it learn faster and better for new tasks. This can involve learning useful priors, initializations, or optimization methods that facilitate quick adaptation.
The applications of meta-learning are diverse, ranging from few-shot learning (learning from a small number of examples) to transfer learning and rapid adaptation in dynamic environments. Meta-learning aims to push AI systems towards more flexible and efficient learning paradigms.