Proposal

Narrative

The AI Research Computer Workstations benefits for both students and faculty: For Students: Hands-on Learning: Direct interaction with hardware enriches understanding of AI algorithms and applications. Skill Development: Students of all majors can learn Python and AI libraries, gaining marketable skills. Innovation: The sandbox encourages experimentation, fostering problem-solving skills. Career Readiness: Early AI exposure prepares students for higher education and job markets. Collaborative Learning: Group projects enhance teamwork and communication skills. For Faculty: Curriculum Enrichment: Faculty can integrate AI concepts into existing subjects. Professional Development: Opportunity for faculty to update their AI knowledge. Resource Optimization: Ready-to-use infrastructure allows a focus on interactive teaching. Interdisciplinary Education: Enables cross-disciplinary projects and collaborations. Assessment and Feedback: Real-time projects offer new assessment tools, going beyond traditional grading systems. This initiative not only equips participants with essential skills but also fosters a culture of innovation and lifelong learning, thereby preparing them for the future.

Facilities

Hardware Requirements

Hardware Requirements for Each $200 Workstation: Raspberry Pi 5 Unit: Cost: $80 Purpose: Acts as the core computing component for running AI algorithms and interfacing with cloud-based platforms. Monitor: Cost: $80 Purpose: Provides a high-resolution display for clear visualization, aiding in effective learning and project execution. Keyboard and Mouse: Cost: $25 Purpose: Ergonomic design for comfortable user experience, essential for coding and interacting with the workstation. Memory Card: Cost: $15 Purpose: High-speed card for quick read/write operations, necessary for data storage and running the Raspberry Pi. Total Cost per Workstation: $200 These hardware components have been carefully selected to offer a fully functional, plug-and-play workstation setup within the allocated budget of $200 per station. This cost-effective approach ensures that each workstation is not only ready for immediate use but also built for longevity, expected to be viable for at least 3 years. This strategic allocation allows the project to deliver a high-quality educational experience that is both impactful and sustainable.

Software Requirements

Software Requirements for Each Workstation: All software used for this educational project is free and open-source, aligning with the project's ethos of cost-effectiveness and accessibility. Python: Cost: Free Purpose: As the primary programming language for AI and machine learning, Python is essential for coding and algorithm development. PyTorch: Cost: Free Purpose: An open-source machine learning library, PyTorch is used for creating neural networks and other AI models. Google Colaboratory: Cost: Free Purpose: Offers a cloud-based Python notebook environment, allowing for real-time collaboration and access to high-compute resources. GitHub: Cost: Free Purpose: Provides a platform for version control and code sharing, facilitating collaborative work on projects. Jupyter Notebook: Cost: Free Purpose: An open-source web application that allows the creation of documents containing live code, equations, and visualizations. TensorFlow: Cost: Free Purpose: Another open-source machine learning library, TensorFlow is used for a variety of AI applications. SciPy & NumPy: Cost: Free Purpose: Libraries for scientific computing in Python, essential for data manipulation and mathematical computations. Matplotlib: Cost: Free Purpose: A plotting library for Python, used for data visualization. Scikit-learn: Cost: Free Purpose: Provides simple and efficient tools for data analysis and modeling, widely used for machine learning tasks. By exclusively using free and open-source software, the project ensures that there are no ongoing software costs, contributing to long-term sustainability. This approach also fosters a collaborative and inclusive learning environment, as students and faculty can freely access, modify, and distribute these software resources.

Personnel Costs

Personnel Support by MPCR Laboratory: The Machine Perception and Cognitive Robotics (MPCR) Laboratory will provide the essential personnel to ensure the project's success and sustainability. Their roles are as follows: Project Manager: Responsibilities: Oversees the entire project lifecycle, including hardware setup, curriculum development, and assessments. Benefits: Ensures the project stays on schedule, within budget, and meets educational goals. Technical Support Staff: Responsibilities: Handles setup, maintenance, and troubleshooting of workstations and software. Benefits: Guarantees that the technical infrastructure is robust and reliable, minimizing downtime and enhancing the learning experience. Curriculum Developer: Responsibilities: Designs and updates the AI-based curriculum, keeping it aligned with advancements in the field. Benefits: Ensures the educational content remains relevant and impactful, preparing students for future careers in AI. With these key personnel, the MPCR Laboratory aims to create a conducive educational environment that is not only cutting-edge but also sustainable over the long term. Their expertise will guide the project from its initial phase to completion, guaranteeing its success.

Other Costs

None

Timeline

Project Duration: Fall 2024 - Fall 2027 (3 years) Year 1: Fall 2024 - Spring 2025 Fall 2024: September: Procurement of Raspberry Pi 5 stations and peripherals. October: Setup and installation of workstations in the AI Sandbox. November - December: Curriculum development and faculty training. Spring 2025: January - February: Pilot program launch. March - May: Full-scale launch of the AI Sandbox and first round of workshops and hackathons. Year 2: Fall 2025 - Spring 2026 Fall 2025: September: Annual impact assessment and financial review. October - November: Software updates and minor hardware maintenance. December: Second round of workshops and hackathons. Spring 2026: January - February: Mid-year evaluation and curriculum update. March - May: Third round of workshops and hackathons. Year 3: Fall 2026 - Fall 2027 Fall 2026: September: Second annual impact assessment and financial review. October - November: Software updates and minor hardware maintenance. December: Fourth round of workshops and hackathons. Spring 2027: January - February: Final workshops and student project showcase. March - May: Project wrap-up, final evaluations, and sustainability transition. Fall 2027: September: Final impact assessment and financial closure. October - November: Project documentation and archiving. December: Project End Date and transition to sustainability phase.

Sustainability

Sustainability Plan for AI Sandbox and MPCR Laboratory Project Cost-Effectiveness: 1. Low-Cost Workstations: Each Raspberry Pi 5 station, including peripherals, costs around $200. This low initial investment ensures that financial resources can be allocated efficiently. 2. Longevity: Given the robust nature of Raspberry Pi 5, each workstation is expected to be viable for at least 3 years, minimizing the need for frequent upgrades or replacements. Cloud-Based Operations: 1. Resource Efficiency: The workstations primarily act as interfaces to cloud-based platforms, where the bulk of computational tasks occur. This reduces the wear and tear on local hardware, extending its lifespan. 2. Software Updates: Cloud-based systems allow for remote software updates, ensuring that each workstation remains current without the need for physical modifications. Financial Plan: 1. Operational Funds: The low cost and longevity of the workstations reduce the operational budget. Any additional requirements will be supported by the MPCR Lab. Maintenance: 1. Self-Sustaining: Due to the cloud-based nature, less emphasis is placed on local hardware, reducing maintenance costs and efforts. Future Upgrades: 1. Hardware: After the 3-year period, upgrades may be necessary. 2. Cloud Software is continuously updated. By leveraging the low-cost, durable workstations and cloud-based operations, this project is built for sustainability. The plan ensures that the educational initiative remains both financially and technologically viable for years to come.

Resource Matching

The AI Sandbox and the Machine Perception and Cognitive Robotics (MPCR) Lab will jointly offer the needed space and resources for this educational project. AI Sandbox: 1. Physical Space: Houses 50 Raspberry Pi 5 stations for hands-on learning. 2. Software: Specialized AI software for diverse projects. 3. Community Hub: A collaborative space for team projects and idea sharing. MPCR Lab: 1. Expertise: Provides depth in machine learning, computer vision, and robotics. 2. Facilities: Complements the AI Sandbox with advanced research resources. 3. Staff Support: Manages operations and assists students and faculty. Synergistic Collaboration: 1. Curriculum: MPCR develops an applied and interactive curriculum. 2. Events: Joint workshops, hackathons, and lectures to enrich learning. 3. Evaluation: Metrics like student participation and skill development for program assessment. 4. Outreach: Partnerships with local industries and institutions to scale impact. Together, the AI Sandbox and MPCR Lab create a robust ecosystem for AI education, blending practical and theoretical resources for a holistic learning experience.

Implementing Organization

The Machine Perception and Cognitive Robotics (MPCR) Laboratory within the AI Sandbox will be responsible for administering this transformative educational initiative. The lab's established expertise in machine learning, computer vision, and robotics makes it the ideal hub for overseeing project execution and curriculum development. Responsibilities: Project Management: MPCR will handle the procurement, setup, and maintenance of the Raspberry Pi 5 stations, ensuring they meet educational and technical standards. Curriculum Design: The lab will develop a hands-on curriculum focusing on AI algorithms, machine perception, and cognitive robotics, making education more interactive and engaging. Faculty Training: MPCR will provide training sessions for faculty members, equipping them to integrate AI concepts into existing courses. Student Engagement: The lab will organize workshops, hackathons, and guest lectures to stimulate interest and participation among students. Monitoring and Evaluation: MPCR will assess the program's effectiveness using metrics like student participation, skill development, and project completion rates. Research and Development: The lab will also use the AI Sandbox for applied research projects, offering advanced students an opportunity to contribute to real-world applications. Community Outreach: MPCR will collaborate with local industries and educational institutions, broadening the scope and impact of the project. By administering the project, the MPCR Laboratory not only ensures its success but also enhances its own research capabilities, forming a synergistic relationship that benefits both education and research.