This tutorial provides a basic introduction to artificial intelligence, covering its core concepts, key types, and common applications. It explains how AI systems learn from data to perform tasks that typically require human intelligence, such as recognizing patterns, making decisions, and understanding language.

• AI is broadly categorized into narrow AI (designed for specific tasks like voice assistants) and general AI (hypothetical systems with human-like intelligence).
• Machine learning, a subset of AI, allows systems to improve their performance on tasks through experience and data without being explicitly programmed.
• Deep learning uses layered neural networks to process complex data, enabling advances in image recognition and natural language processing.
• Common real-world AI applications include recommendation systems, autonomous vehicles, medical diagnosis, and customer service chatbots.
• Ethical considerations such as bias in training data, job displacement, and privacy are important challenges in AI development.