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Distinguish AI, machine learning and a complete software system.
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AI 101. What AI is—and what it is not. Distinguish AI, machine learning and a complete software system. Start with the task. Artificial intelligence is a broad field concerned with systems that perform tasks associated with intelligence: recognizing patterns, using language, planning or making predictions. A useful first question is not “Is this intelligent?” but “What task does it perform, on which inputs, and how will we know whether it works?” A photo classifier, a route planner and a text generator solve different problems. Success on one does not establish competence on the others. Place the terms. Machine learning is one way to build AI: a model learns patterns from examples rather than receiving every decision as a handwritten rule. Deep learning uses neural networks with multiple processing layers. Generative AI produces content such as text or images. These labels overlap; they are not separate products. AI also includes approaches that use explicit rules or search. A rule that rejects an empty form is useful automation, but it does not learn from examples. Look beyond the model. A usable AI feature also needs input handling, permissions, a user interface, checks and a way to recover when something goes wrong. A confident description is not proof that an image contains what the model says it contains. Separate an observation, such as “a dark region is visible,” from an interpretation, such as “this material is damaged.” Keep a person responsible for consequential interpretations. HerbDev example. HerbDev’s case study connects field capture, local analysis, review, history and reporting. Use it to identify the workflow around a model. It is an engineering example, not evidence that every automated interpretation is correct. The process: Define a task. Then Collect an input. Then Produce a suggestion. Then Check the result. Try this. Choose a familiar task: sorting photos, drafting a note or finding a document. Write its input, useful output and one failure that matters. Pause the audio to try the exercise. A worked answer and checkpoint questions are available on the lesson page.
Start with the task
Artificial intelligence is a broad field concerned with systems that perform tasks associated with intelligence: recognizing patterns, using language, planning or making predictions. A useful first question is not “Is this intelligent?” but “What task does it perform, on which inputs, and how will we know whether it works?” A photo classifier, a route planner and a text generator solve different problems. Success on one does not establish competence on the others.
Place the terms
Machine learning is one way to build AI: a model learns patterns from examples rather than receiving every decision as a handwritten rule. Deep learning uses neural networks with multiple processing layers. Generative AI produces content such as text or images. These labels overlap; they are not separate products. AI also includes approaches that use explicit rules or search. A rule that rejects an empty form is useful automation, but it does not learn from examples.
Look beyond the model
A usable AI feature also needs input handling, permissions, a user interface, checks and a way to recover when something goes wrong. A confident description is not proof that an image contains what the model says it contains. Separate an observation, such as “a dark region is visible,” from an interpretation, such as “this material is damaged.” Keep a person responsible for consequential interpretations.
- Define a task
- Collect an input
- Produce a suggestion
- Check the result
Try it yourself
Choose a familiar task: sorting photos, drafting a note or finding a document. Write its input, useful output and one failure that matters.
Compare with a worked answer
For a photo-note assistant: input = a site photo plus the worker’s note; output = a draft description; important failure = inventing damage that the photo does not establish. A worker reviews the draft before it becomes a report.
Check your understanding
Choose one answer for each question, then check your score. Read the explanations and retry any question you missed.
Read the answer guide
1. Learning patterns from examples Machine learning fits patterns from examples. Permissions and hand-coded rules are separate design choices.
2. Human review and recovery Review, input handling, permissions and recovery help make a model useful in a real workflow.
3. It does not establish that the interpretation is correct A confident output can still be wrong. Check it against the available evidence and the task’s consequences.
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Apply it in the final project · Choose what to learn next →Keep exploring: What next? · AI vocabulary · ML 101 · Learning hub