CyberTraining: Pilot: An Artificial Intelligence Bootcamp for Cyberinfrastructure Professionals

人工智能（AI）被用于现代生活的许多方面，如语言翻译和图像分析。除了消费者和商业应用，研究人员越来越多地在他们的科学过程中使用人工智能技术。人工智能的增长严重依赖于新的深度学习（DL）和机器学习（ML）方案。随着数据集、DL和ML模型变得越来越复杂，人工智能的计算需求也在增加，研究人员转向高性能计算（HPC）设施来满足这些需求。这导致了对网络基础设施（CI）劳动力的迫切需求，这些劳动力支持具有AI技术和底层技术专业知识的HPC系统。该项目将根据专业人员的工作要求为CI专业人员提供人工智能训练营。参加训练营后，CI专业人员将更好地为AI研究用户提供计算和数据服务。这反过来又将扩大广泛学科研究人员对先进CI的采用和有效使用，并将对科学产生影响，并从他们的成功中为社会带来相应的利益。在此项目中开发的培训材料将与CI社区公开共享，以便其他人可以使用和调整材料用于类似的培训活动。该项目是一个新颖的整体方法，以解决CI专业人员的AI专业知识差距。该项目将为CI专业人员开发一个AI训练营，其总体目标是提高他们对AI研究人员支持的信心和有效性。该项目利用校园研究计算联盟（CaRCC）的CI专业化努力，根据四个“面向”（面向战略/政策，面向研究人员，面向软件/数据和面向系统）组织培训成果。 该项目将确定每个CI面临的学习成果，并组织针对特定角色定制的培训课程。对于这个试点项目的重点是开发一个全面的培训经验，软件/数据面对CI专业人员。AI Bootcamp将在12周内提供。教学材料将酌情以笔记本、幻灯片和容器的形式公开分享，以便用于其他培训活动。项目团队由CI专业人员组成，他们在培训CI用户和提供CI操作方面经验丰富，计算机科学教师在提供数据分析，AI和高性能AI课程方面经验丰富，并具有积极的基于AI的研究计划。利用人工智能实践体验式学习的丰富经验和材料，项目团队将创建一个涵盖基础人工智能，软件框架，以模块化的虚拟形式为人工智能提供高性能计算，以最大限度地减少CI专业学习者的访问障碍。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值进行评估，被认为值得支持和更广泛的影响审查标准。

Artificial Intelligence (AI) is used in many aspects of modern life such as language translation and image analysis. In addition to consumer and business applications, researchers are increasingly using AI techniques in their scientific processes. The growth in AI is heavily dependent on new Deep Learning (DL) and Machine Learning (ML) schemes. As datasets and DL and ML models become more complex the computing requirements for AI increase and researchers turn to high performance computing (HPC) facilities to meet these needs. This is leading to a critical need for a Cyberinfrastructure (CI) workforce that supports HPC systems with expertise in AI techniques and underlying technology. This project will pilot an AI bootcamp for CI professionals that is targeted based on the professional's job requirements. After attending the bootcamp CI professionals will be better equipped to provide computing and data services to AI research users. This in turn will broaden adoption and effective use of advanced CI by researchers in a wide range of disciplines and will have an impact on science and corresponding benefits to society from their successes. The training materials developed during this project will be openly shared with the CI community so that others can use and adapt the materials for similar training activities.This project is novel in taking a holistic approach to addressing the AI expertise gap for CI professionals. The project will develop an AI Bootcamp for CI professionals with the overarching goal of increasing the confidence and effectiveness of their support of AI researchers. The project leverages the CI professionalization efforts of the Campus Research Computing Consortium (CaRCC) to organize the training outcomes based on four "facings" (Strategy/Policy facing, Researcher facing, Software/Data facing, and Systems facing). The project will identify learning outcomes for each CI facing and organize training tracks customized to specific roles. For this pilot the project is focused on developing a comprehensive training experience for Software/Data facing CI professionals. The AI Bootcamp will be offered virtually over twelve weeks. The instructional materials will be shared openly as notebooks, slide-decks and containers as appropriate so that they can be used for other training offerings. The project team is comprised of CI professionals, experienced in training CI users and providing CI operations, and Computer Science faculty members, experienced in offering courses in Data Analytics, AI and High Performance AI with active AI-based research programs. Drawing on extensive experience and materials in hands-on experiential learning for AI, the project team will create a comprehensive curriculum spanning foundational AI, software frameworks, and high performance computing for AI in a modularized virtual format to minimize barriers to access for the CI professional learner.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.