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.
01
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.
02
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:
- Agentic
Cultivating agentic capabilities that can proactively reason, plan, and respond to user needs
- Physical
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.