AI, or artificial intelligence, is the field of computer science focused on building systems that can perform tasks that usually require human intelligence. This includes understanding language, recognizing images, learning from data, solving problems, and making decisions.

High-level ideas:
- How it works: AI systems learn from data using algorithms and models. They find patterns, make predictions or decisions, and sometimes improve over time with more data.
- Common approaches: machine learning (where a model learns from examples), deep learning (a subset of ML using neural networks), and rule-based systems (explicit rules).
- Types:
  - Narrow (or weak) AI: specialized for a single task (e.g., voice assistants, image classifiers).
  - General (or strong) AI: hypothetical systems that can perform any intellectual task a human can; not yet achieved.
- Examples you’ve likely used: virtual assistants, spam filters, recommendation engines, image and speech recognition, autonomous vehicles.

Key distinctions:
- AI vs automation: Automation repeats predefined tasks; AI aims to imitate flexible, intelligent behavior.
- AI vs ML vs data mining: AI is the broader goal; ML is a common way to build AI by learning from data; data mining focuses on discovering patterns in large datasets.

Ethical notes:
- Potential issues include bias, privacy, safety, and transparency. Responsible AI emphasizes fairness, accountability, and governance.

If you want, I can dive into a specific area (examples, how ML works, or how to get started learning AI).