Exploring the kinds of problems AI is designed to solve.
Artificial Intelligence is not a monolithic solution; it's a toolbox of techniques applied to different classes of problems. A fundamental problem type is Search. Search problems involve finding a sequence of actions (a 'plan') that leads from an initial state to a goal state. This is applicable to everything from a GPS finding the best route, to solving a puzzle like a Rubik's Cube, to a game AI deciding its next move. Another major class is Reasoning and Knowledge Representation. This involves creating a formal way to store information about the world and then using logical rules to deduce new information. For instance, an expert system for medical diagnosis might store facts about diseases and symptoms and use rules to infer a likely illness. Planning problems are a specific type of search where the agent must devise a sequence of actions to achieve a goal, often in a complex environment with many constraints. This is critical for robotics and logistics. A fourth category is Perception. This involves interpreting raw sensory data from the world, such as images (computer vision), sound (speech recognition), and text (natural language processing). The goal is to transform this unstructured data into a structured representation that the AI can reason about. Finally, Learning is a core problem type that underpins modern AI. This is where machine learning comes in, with the goal of building systems that can automatically improve their performance on a task by learning from experience or data, without being explicitly programmed for every scenario. These problem categories often overlap; for example, a self-driving car uses perception to see the road, planning to navigate, and learning to improve its driving skills over time.