Advanced AI Talent Cultivation Program
Agentic & Physical AI Robotics Curriculum Initiative
National Agentic & Physical AI Initiative
About the Program
Cultivating forward-looking talent in Agentic AI and Physical AI in response to the AI paradigm shift
Program Background
In recent years, AI technology has advanced rapidly, with continual breakthroughs in model capabilities and simulation results. Yet most AI education and training still remains at the stage of "using models" and "idealized simulation." It is often only when AI systems truly enter real life and industrial settings that we discover the challenges are just beginning.
The Real-World Gap in Physical Systems
Tasks that run smoothly in a simulated environment, once connected to physical hardware, must contend with motor error, sensor noise, response latency, and environmental uncertainty.
Complex Demands of Dynamic Scenarios
In everyday life, problems are often multifaceted and constantly changing, requiring simultaneous consideration of user needs, situational judgment, and resource constraints rather than being solved by a single command.
Many applied AI systems are not simply "answering questions"; they must start from the user's perspective—understanding needs, breaking down tasks, and continually adjusting their course of action.
This shows that what current agent development lacks is often not model capability, but rather experience drawn from real usage scenarios and the ability to integrate systems.
Therefore, NAPAI focuses its AI talent cultivation on two forward-looking pillars: Agentic AI and Physical AI:
Cultivating agentic capabilities that can proactively reason, plan, and respond to user needs
Cultivating hands-on skills to implement AI in physical systems under real-world constraints
By integrating these two pillars, NAPAI is committed to cultivating forward-looking AI talent who can bridge the gap between models, simulation, and the real world to genuinely solve real-world problems.
The next step for AI is not just stronger models, but systems that understand real needs and act in the real world.
Program Goals
NAPAI adopts a "lean but focused, results-driven" strategy, targeting two core technical pillars and integrating seed-teacher training, co-creation of course modules, pilot demonstration courses, student hands-on competitions, and community building.
Open-Source Courseware Platform
Co-edited course maps and a GitHub course repository
Forward-Looking Technical Vision
Cultivating AI professionals with cross-domain hands-on skills
Strengthening Teaching Capacity
Enhancing teachers' instructional and practical capabilities
Community Support System
Building scalable, maintainable course modules
Through hands-on projects and competitions, students are guided to focus on real industry scenarios and societal needs, laying a solid talent foundation for the future development of Taiwan's AI industry and intelligent applications.