What is the difference between AI and Machine Learning?

AI (Artificial Intelligence) and Machine Learning (ML) are related but distinct concepts. AI refers to the broad goal of creating systems that can perform tasks requiring human-like intelligence, such as reasoning, problem-solving, or perception. ML, on the other hand, is a specific approach within AI that focuses on training algorithms to learn patterns from data. While all ML falls under AI, not all AI systems use ML—some rely on predefined rules or logic.

AI encompasses a wide range of techniques. For example, a rule-based chatbot that follows decision trees to answer questions is an AI system, but it doesn’t learn from data. Similarly, classic algorithms like A* for pathfinding in games demonstrate AI without ML. These systems operate based on explicit instructions programmed by developers. ML, by contrast, involves algorithms that improve their performance by processing data. For instance, a spam filter trained on labeled emails (spam vs. not spam) uses ML to adapt its behavior over time without manual updates to its rules. The core difference lies in how they achieve intelligence: AI can be static and rule-driven, while ML systems evolve through data exposure.

The relationship between AI and ML becomes clearer when considering real-world applications. A self-driving car’s AI might combine ML components (like a vision system recognizing pedestrians) with non-ML components (like a rule-based controller managing speed limits). ML excels at tasks where patterns are too complex for humans to codify manually, such as image recognition using neural networks. However, AI also includes techniques like symbolic reasoning or genetic algorithms that don’t require data-driven learning. For developers, the choice depends on the problem: ML is ideal when large datasets exist and adaptability is key, while traditional AI methods may suffice for well-defined, predictable tasks. Understanding this distinction helps in selecting the right tools—whether that’s training a neural network (ML) or designing a state machine (non-ML AI).