This study explores the structure of a summer camp curriculum and the development of middle school students' understanding of Artificial Intelligence (AI). Pre-posttests and surveys measuring AI knowledge and attitudes toward AI were analyzed by paired t-tests. Thematic analysis was used to analyze over 30 hours of video footage and student artifacts to examine the integration of curriculum and the development of students' understanding. The curriculum encompassed machine learning (ML), natural language process (NLP), and computer vision (CV), focusing on improving students' understanding of AI through the creation of AI-based artifacts, and mini-projects addressing daily issues faced in homes and schools. The findings revealed that students' overall AI knowledge and attitudes toward AI significantly improved after the intervention. Additionally, students recognized the importance of data quality and quantity in training AI. These findings highlight the positive impact of summer camp curriculum with hands-on experiences, beneficial for fostering middle school students' understanding of AI.

This paper proposes an intelligent manufacturing system that can make decisions about the process in light of the uncertain outcome of these decisions and attempts to minimize the expected economic penalty resulting from those decisions. It uses robot weld bead grinding as an example of a process with significant process variation. A three tier hierarchical control system is proposed to plan an optimal sequence of grinding passes, dynamically simulate each pass, execute the planned sequence of controlled grinding passes, and modify the pass sequence as grinding continues. The top tier, described in this paper, plans the grinding sequence for each weld bead, and is implemented using stochastic dynamic programming, selecting the volumetric removal and feedspeed for each pass in order to optimize the satisfaction of the task requirements by the entire grinding sequence within the equipment, task, and process constraints. The resulting optimal policies have quite complex structures, showing foresight, anxiety, indifference, and aggressiveness, depending upon the situation.>