What Is Artificial Intelligence?
A chess engine, a spam filter, and a chatbot all get called "AI," but they work nothing alike under the hood. Before this course goes any further, it's worth pinning down what the term actually covers.
A working definition
Artificial intelligence, broadly, is the effort to build systems that perform tasks which would normally require human intelligence — recognizing a face, translating a sentence, deciding the best next move in a game, predicting whether a transaction is fraudulent. Notice this definition says nothing about how the system does it. A chess engine that brute-force searches millions of positions and a language model that predicts text one token at a time are both "AI" by this definition, even though they share almost no machinery.
Narrow AI vs. general AI
Every AI system in real-world use today — including the most impressive ones — is narrow AI: built for a specific task or class of tasks, with no ability to transfer that competence somewhere else. A system that plays chess at a superhuman level cannot fold laundry, and a system that writes fluent paragraphs cannot, on its own, drive a car. General AI (sometimes called AGI) — a single system with human-level competence across essentially any task — does not exist yet. It's a research goal and a subject of real disagreement among experts about whether, or when, it's achievable, not something already running in a product you can buy.
Common misconceptions worth clearing up early
- "AI" doesn't imply consciousness, feelings, or genuine understanding — a system can produce very convincing output while doing nothing resembling what a human means by "thinking."
- "AI" isn't one technology. It's an umbrella covering search algorithms, statistical machine learning, neural networks, and more — this course covers several of them separately because they really are different tools.
- Impressive output doesn't imply general capability. A model that writes excellent code can be confidently, fluently wrong about basic arithmetic.