Introduction to Artificial Intelligence: your first working example

Why this example matters

Imagine a weather helper for a family picnic. We can begin with a transparent rule about temperature and rain before collecting training data. This distinction matters: a simple rule may be easier to check, explain and repair than a learned predictor.

What you will be able to do

• Recognize the input values and describe what they represent in Introduction to Artificial Intelligence.
• Run the Introduction to Artificial Intelligence program and explain why its displayed output follows from the recorded operations.
• Identify a limitation of the Introduction to Artificial Intelligence activity before applying similar reasoning to a real system.

Original worked program

The following Introduction to Artificial Intelligence demonstration uses small invented values so you can trace every operation. The Introduction to Artificial Intelligence activity uses only Python standard-library features; it downloads no model and calls no paid service.

temperature = 31
rain_probability = 0.15
if temperature > 29 and rain_probability < 0.30:
    advice = "Carry water"
else:
    advice = "Check conditions"
print("Decision:", advice)
print("Uses learned model?", False)

Actual output from Python

Decision: Carry water
Uses learned model? False

Read each instruction

1. temperature = 31The assignment gives a name to a value or a calculated result in Introduction to Artificial Intelligence. In the Introduction to Artificial Intelligence activity, check the named values on that line rather than memorizing symbols.
2. rain_probability = 0.15The assignment gives a name to a value or a calculated result in Introduction to Artificial Intelligence. In the Introduction to Artificial Intelligence activity, check the named values on that line rather than memorizing symbols.
3. if temperature > 29 and rain_probability < 0.30:The condition checks a fact before selecting the next action in Introduction to Artificial Intelligence. In the Introduction to Artificial Intelligence activity, check the named values on that line rather than memorizing symbols.
4. advice = "Carry water"The assignment gives a name to a value or a calculated result in Introduction to Artificial Intelligence. In the Introduction to Artificial Intelligence activity, check the named values on that line rather than memorizing symbols.
5. advice = "Check conditions"The assignment gives a name to a value or a calculated result in Introduction to Artificial Intelligence. In the Introduction to Artificial Intelligence activity, check the named values on that line rather than memorizing symbols.

Observe the results

To verify the Introduction to Artificial Intelligence result, copy the program into a fresh file and run it once without modification. Then change only one input. For the Introduction to Artificial Intelligence trial, record what you predict before pressing Run again. Compare the Introduction to Artificial Intelligence prediction with the observed result.

Second original practice: Recognizing data inputs and decisions

The Introduction to Artificial Intelligence extension changes the task, not just the numbers in the first example. Study its new data structure or operation, then decide how the result supports the milestone “Recognizing data inputs and decisions”.

questions = {"hours": "Check timetable", "location": "Show map"}
request = "hours"
print("Visitor asked:", request)
print("Assistant action:", questions.get(request, "Ask a person"))

Actual second output

Visitor asked: hours
Assistant action: Check timetable

For the Introduction to Artificial Intelligence extension, read the code from its first assignment or import to its final print statement. Explain the additional Introduction to Artificial Intelligence idea introduced by this extension, then design a check that could reveal a mistake. The Introduction to Artificial Intelligence extension output above was recorded by running that independent program.

What the example does not prove

The displayed advice is not a weather forecast. It is a deterministic decision based on two invented numbers. Real decisions need reliable measurements, user context and a way to show uncertainty.

A reliable Introduction to Artificial Intelligence project also needs an explicit objective, test cases that were not used to tune the approach, documentation of the assumptions, and review of situations where the result might mislead the user. Do not mistake the small Introduction to Artificial Intelligence program for evidence of accuracy or safety in a deployed application.

How to validate your own result

For Introduction to Artificial Intelligence, a convincing result starts with a written claim about the actual task, not the appearance of a successful print statement. For Introduction to Artificial Intelligence, list the initial values and the one value you will vary; record a prediction that could be disproved. Save both Introduction to Artificial Intelligence outputs as separate records to avoid mixing up the results of the two investigations. A correct Python run only establishes that the particular instructions executed; it does not establish that the assumptions match every situation in which Introduction to Artificial Intelligence might be used.

Connect this trial to “Separating rules from learned patterns” without claiming that the milestone is already complete. Name a measurement you would collect for Introduction to Artificial Intelligence, a plausible error source and a person able to review the decision. The later topic “Search problems and state spaces” deserves its own investigation with different evidence. Keeping these questions separate prevents your Introduction to Artificial Intelligence learning journal from becoming a collection of exercises that all make the same claim.

Finally, keep the Introduction to Artificial Intelligence experiment reproducible: include the Python version, exact script, input values, observed results and any unexpected behavior. If a Introduction to Artificial Intelligence investigation uses external data, document the source and your permission to use those records. A new learner should be able to explain every output line in ordinary words before moving into the next milestone of Introduction to Artificial Intelligence.

Second verified lab: Data in practice

To deepen Chapter 1 of Introduction to Artificial Intelligence, compare the first calculation with a different, executable experiment. The small data lab below illustrates a related technique, not every possible implementation of defining intelligence and automation.

# Course: Introduction to Artificial Intelligence | Chapter 1 | Subject: Defining intelligence and automation
# Validate a toy input table before using it downstream
records=[{"id":1,"value":8},{"id":2,"value":None},{"id":3,"value":8}]
valid=[row for row in records if isinstance(row["value"],int)]
print("Rows received:",len(records))
print("Valid rows:",len(valid))
print("Missing rows:",len(records)-len(valid))
print("Valid total:",sum(row["value"] for row in valid))

Actual output of the displayed code

Rows received: 3
Valid rows: 2
Missing rows: 1
Valid total: 16

While examining your first working example in the introduction to artificial intelligence course (chapter 1), apply this test: Explicit schema and missing-value checks make the transformation reproducible. The example does not silently treat missing observations as zeros. For the community recycling statistics exercise, distinguish what the code observes from any stronger predictive or causal claim.

While examining your first working example in the introduction to artificial intelligence course (chapter 1), apply this test: Interpretation limit. Filtering records changes the represented population; document what was excluded and why. Record the changed inputs and compare both outputs before extending the result to a project.Interpretation limit. Filtering records changes the represented population; document what was excluded and why. Record the changed inputs and compare both outputs before extending the result to a project.

20 practice questions and worked answers

Test your understanding of your first working example in the introduction to artificial intelligence course (chapter 1). Compare the claims in both runnable labs, and use the outputs to defend each numerical response.

Build one original mini-project

Chapter 1 checkpoint: Explain how defining intelligence and automation affects the community recycling statistics example; demonstrate one failing input; then propose a revision with an acceptance test and a recorded output. This checkpoint belongs to the Introduction to Artificial Intelligence course.

Sketch a three-question decision assistant for a library: determine whether a visitor needs directions, opening hours or accessibility help. Record where a human should take over.

A six-step practice plan

1. Defining intelligence and automation. For the “Defining intelligence and automation” exercise, identify one observable result and record what would demonstrate progress in Introduction to Artificial Intelligence.
2. Recognizing data inputs and decisions. For the “Recognizing data inputs and decisions” exercise, identify one observable result and record what would demonstrate progress in Introduction to Artificial Intelligence.
3. Separating rules from learned patterns. For the “Separating rules from learned patterns” exercise, identify one observable result and record what would demonstrate progress in Introduction to Artificial Intelligence.
4. Search problems and state spaces. For the “Search problems and state spaces” exercise, identify one observable result and record what would demonstrate progress in Introduction to Artificial Intelligence.
5. Knowledge representation with facts. For the “Knowledge representation with facts” exercise, identify one observable result and record what would demonstrate progress in Introduction to Artificial Intelligence.
6. Uncertainty in automated decisions. For the “Uncertainty in automated decisions” exercise, identify one observable result and record what would demonstrate progress in Introduction to Artificial Intelligence.

Save your original inputs, your changed inputs, the two actual outputs and a short explanation of what you learned about Introduction to Artificial Intelligence. The Introduction to Artificial Intelligence record forms evidence for the first milestone of your project portfolio.

Where you go next

Your Introduction to Artificial Intelligence roadmap has 18 subject-specific milestones progressing toward evaluation and a capstone. Future Introduction to Artificial Intelligence lessons will require new demonstrations and evidence, beyond the skills taught in this first chapter.