a basic AI that chases a target
11In the realm of artificial intelligence, even the simplest models can demonstrate intriguing behaviors. Consider a basic AI that chases a target, much like a predator tracking its prey. The fundamental principle behind this AI is distance minimization—it continuously adjusts its trajectory to move toward the shortest path to its goal.
This mechanism, though elementary, shares similarities with many machine learning techniques. For instance, gradient descent, a core algorithm in optimization and deep learning, follows a similar concept: finding the steepest path to minimize error. In our case, instead of minimizing an abstract cost function, the AI minimizes spatial distance in a two-dimensional space.
This simple logic forms the backbone of many real-world applications. Self-driving cars use similar principles to stay within lanes and avoid obstacles, while robotics employ pathfinding algorithms like A* or reinforcement learning to navigate dynamic environments. By understanding the core concept of distance minimization, one can appreciate the foundation of more complex decision-making systems.
The next step in the evolution of such AI could involve obstacle avoidance, adaptive movement speeds, or even predictive algorithms that anticipate the target’s trajectory. As AI advances, even these fundamental principles continue to scale, evolving from simple vector calculations to sophisticated, context-aware decision-making systems. The journey from basic chasing behavior to intelligent navigation highlights the seamless progression of artificial intelligence from simple heuristics to advanced autonomy.