Artificial Intelligence
Systems that perform tasks normally requiring human intelligence.
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Overview
Artificial intelligence is the umbrella term for machine learning, deep learning, and generative AI -- systems that learn patterns from data or generate content, rather than following only explicitly hand-written rules. This platform's AI, LLMs & RAG course starts by drawing precise boundaries between these overlapping terms.
- What it is
- The broad field covering systems that learn from data or generate content, as opposed to purely hand-coded logic.
- Why it's used
- Modern AI systems (especially LLMs) can handle tasks -- open-ended text understanding, generation -- that are impractical to hand-code with explicit rules.
- Where it fits
- The umbrella category; this platform's own AI, LLMs & RAG course teaches the mechanics underneath, not just the vocabulary.
Core concepts
- AI vs. machine learning vs. deep learning vs. generative AI
- Training vs. inference
- What a model actually is (learned parameters, not rules)
Example
The core distinction: hand-written rules only handle cases the author anticipated; a trained model generalizes from examples to cases it never explicitly saw.
// Rule-based (not AI): if input contains "refund", route to billing.
// Learned (AI): a model trained on thousands of examples
// predicts the right routing, including cases no rule anticipated.Common use cases
- Recommendation systems
- Natural language understanding
- Generative content tools
Project ideas
- Write down three tasks and classify each as better suited to explicit rules vs. a learned model, and explain why