AI, Machine Learning, Deep Learning, and Generative AI
AI, Machine Learning, Deep Learning, and Generative AI
Key jargon
| Term | Plain-language meaning |
|---|---|
| Artificial intelligence (AI) | The broad field of building systems that perform tasks associated with human intelligence. |
| Machine learning (ML) | A way to build behavior by learning patterns from data instead of writing every rule. |
| Deep learning | ML based on multilayer neural networks that learn increasingly useful representations. |
| Generative AI | Models that produce new content—such as text, images, audio, or code—from learned patterns. |
Key concepts
- AI is the umbrella; ML, deep learning, and generative modeling are overlapping implementation families.
- A model is only one component of an AI system; data, software, people, policies, and tools shape the outcome.
Concept map
flowchart LR
A["AI field"] --> B["ML learns from data"]
B --> C["Deep learning uses neural nets"]
C --> D["Generative AI creates content"]Learning objectives
- Distinguish AI, machine learning, deep learning, foundation models, and generative AI.
- Identify whether behavior comes from a model or from surrounding software.
Mental model
Artificial intelligence is the broad goal of making machines perform tasks associated with perception, prediction, reasoning, language, or action. Machine learning fits functions from data rather than encoding every rule manually. Deep learning uses multilayer neural networks. A foundation model is trained broadly and adapted to many tasks. Generative AI produces new text, images, audio, video, code, or structured data.
An AI product is usually:
model + instructions + context + tools + software + data + controls + humans
The model does not independently know your database, permissions, current date, or business policy. The application supplies those—or fails to.
Common misconception
“The model is the AI system.” In practice, many failures attributed to the model are retrieval, permission, prompt, data, UI, or evaluation failures.
Exercise
Choose one assistant you use. List its likely model, context sources, tools, outputs, permissions, and human decision owner. Mark unknowns explicitly.
Operational checklist
- Name the task before selecting a model.
- Separate prediction from deterministic business rules.
- Identify data and action boundaries.
- Decide how success will be measured.