Advanced AI Systems and Applications

This capstone unit synthesizes advanced topics in AI systems engineering, including integration with specialized sensors and hardware, telepresence and automation, space and medical applications, and considerations for generative AI commercialization, security, and error mitigation. Students will apply systems-level design and evaluation skills to create robust, ethical, and commercially aware AI solutions for extreme and high-stakes environments.

Learning Objectives

• Analyze trade-offs among sensor selection, hardware capability, and data acquisition limits to justify a systems-level design for an AI application in a high-risk environment (space or medical).
• Apply integration techniques to produce a functional system schematic or prototype plan that combines AI models with specialized sensors, telepresence, and automation to perform a defined task.
• Demonstrate methods for detecting and mitigating AI-based errors and for ensuring data security and reliability by developing and executing test plans, redundancy strategies, and security assessments.
• Evaluate the ethical, workforce, and commercialization impacts of deploying generative AI and automation in medical and space contexts and prepare a recommendation report that includes mitigation strategies and go-to-market considerations.