Using Agentic AI (primarily based on reinforcement learning) right from the start, instead of using supervised and unsupervised learning, has some risks and challenges:
What Could Go Wrong
– Inefficient Learning: Slow to train as it relies on trial and error. It may need many attempts in complex environments, making it less efficient than methods using existing data.
– Unpredictable Behavior: In early stages, the AI might take random actions, leading to unpredictable outcomes—especially risky in fields like healthcare or transportation.
– Difficult Environment Setup: Creating a realistic environment can be tough. If the simulation isn’t accurate, the AI may struggle when deployed.
– Reward Challenges: A poorly designed reward system might cause shortcuts or undesirable behaviors to maximize rewards.
– High Resource Consumption: Reinforcement learning requires significant computing power, which can be expensive and time-consuming.
Suitability of Agentic AI
– Self-Driving Cars: It excels in real-time decision-making scenarios like self-driving cars, learning and adapting from experiences.
– Not Always the Best Fit: For tasks with clear outcomes or abundant data (e.g., image recognition), supervised learning can be more effective, with Agentic AI adding unnecessary complexity.
Summary
Agentic AI is powerful for dynamic environments like self-driving cars, but using it from the start in all scenarios can lead to inefficiencies and unpredictability. It’s often better to start with supervised learning when suitable, and then use reinforcement learning for fine-tuning.