Meta-learning, also known as "learning to learn", enables models to acquire great generalization abilities by learning from various tasks. Recent advancements have made these models applicable across various fields without data constraints, offering new opportunities for general artificial intelligence. However, applying these models can be challenging due to their often task-specific, standalone nature and the technical barriers involved. To address this challenge, we develop AwesomeMeta+, a prototyping and learning system that standardizes different components of meta-learning and uses a building block metaphor to assist in model construction. AwesomeMeta+ allows users to assemble compatible algorithm modules to meet the application needs in practice. To optimize AwesomeMeta+, we provide the interface to 50 researchers and refine the design based on their feedback. Through machine-based testing and user studies, we demonstrate that AwesomeMeta+ enhances users' understanding of the related technologies and accelerates their engineering processes by offering guidance for meta-learning deployments.

翻译：元学习，亦称“学会学习”，使模型能够通过从多种任务中学习获得卓越的泛化能力。最新进展使得这些模型能够不受数据限制地应用于各领域，为通用人工智能带来了新机遇。然而，由于这些模型通常具有任务特定性、独立性强且存在技术门槛，其实际应用仍面临挑战。为应对这一挑战，我们开发了AwesomeMeta+——一个采用构建模块化理念的原型设计与学习系统，该系统对元学习的各组件进行了标准化，以辅助模型构建。AwesomeMeta+允许用户组合兼容的算法模块，以满足实际应用需求。为优化系统设计，我们向50位研究人员提供了系统接口，并根据反馈持续改进设计。通过自动化测试与用户研究表明，AwesomeMeta+不仅增强了用户对相关技术的理解，还能通过提供元学习部署指导来加速工程实践进程。