CyberTraining: Pilot: Modular experiential learning for secure, safe, and reliable AI (MELSSRAI)

在这一试点项目中，将安全、安全、可靠(SSR)计算、高性能计算(HPC)和人工智能(AI)相结合的核心素养和高级技能整合到教育课程和培训材料中，为采用高级网络基础设施(CI)进行大规模安全数据分析的机构的教职员工、本科生和研究生做好准备。从自动驾驶车辆到智能数字个人助理和实时多语言翻译，人工智能的应用在我们的日常生活中无处不在。迫切需要确保当前和未来推动人工智能的科学家以及使用人工智能的从业者了解人工智能的局限性以及如何开发健壮和可靠的人工智能。该项目的长期目标是为SSR致力于人工智能的CI劳动力和一个自我维持的先进CI生态系统做出贡献。在这个项目中，受开放人工智能和人工智能伙伴关系等权威来源的启发，从一开始就开发了课程修改和材料，以教育计算机科学(CS)学生使用SSR技术。密集的、多方面的、模块化的体验式学习单元旨在快速提升当前和未来CI用户的技能，以便他们能够将新技能应用于他们的任务。松散耦合的模块可以集成到现有的班级中，包括非CS STEM学生参加的初级CS课程。学生参与研究活动，培养下一代跨学科科学家，包括许多来自代表性不足的群体。该项目包括不同层次和不同地点的大学以及社区学院。利用集体影响计划，一组多学科的公私部门专家提供指导，并参与培训培训者的活动，以扩大影响。总结的经验教训和最佳实践被编入可重复使用的蓝图，并在STEM学科中广泛采用。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

In this pilot project, core literacy and advanced skills at the intersection of Secure, Safe, Reliable (SSR) Computing, High Performance Computing (HPC), and artificial intelligence (AI) are integrated into educational curricula and training materials to prepare faculty, undergraduate, and graduate students at institutions with relatively low rates of advanced cyberinfrastructure (CI) adoption for large-scale secured data analytics. From self-driving vehicles to smart digital personal assistants and real-time multilingual translators, applications of AI have become omnipresent in our daily lives. There is an urgent need to ensure that current and future scientists who advance AI, as well as practitioners who use AI, understand the limitations of AI and how to develop robust and dependable AI. The long-term goals of this project are to contribute to a pipeline for a SSR AI-minded CI workforce and a self-sustaining advanced CI ecosystem. In this project, inspired by authoritative sources such as Open AI and Partnership on AI, curricular modifications and materials are developed to educate computer science (CS) students in SSR techniques from the outset. Intensive, multi-faceted, modular, experiential learning units are designed to upgrade the skills of current and future CI users rapidly, so they can apply their new skills to their tasks. The loosely coupled modules can be integrated into existing classes, including elementary CS classes taken by non-CS STEM students. Students participate in research activities, which train next generation interdisciplinary scientists, including many from underrepresented groups. Universities at varied levels and varied locations as well as community colleges are included in the project. Using a collective impact plan, a group of multi-discipline, public-private-sector experts provide guidance and participate in train-the-trainer activities to multiply the effect. Lessons learned and best practices are codified into blueprints for reusability and widespread future adoption across STEM disciplines.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.