Machine learning vs rule-based systems in AI for Everyone - Visual Side-by-Side Comparison

Concept Flow - Machine learning vs rule-based systems

Start: Input Data

↓

Apply Rules

↓

Output Result

↓

End: Decision Made

The system receives input data, then either applies fixed rules or uses a trained model to produce an output.

Execution Sample

AI for Everyone

Input: data
If rule-based:
  Apply fixed rules
Else:
  Use learned model
Output result

This pseudocode shows how input data is processed differently by rule-based and machine learning systems.

Analysis Table

Step	Input	System Type	Action	Output
1	Temperature=30	Rule-based	Check if temp > 25	Output: 'Hot'
2	Temperature=20	Rule-based	Check if temp > 25	Output: 'Not Hot'
3	Temperature=30	Machine learning	Model predicts based on data	Output: 'Likely Hot'
4	Temperature=20	Machine learning	Model predicts based on data	Output: 'Likely Not Hot'
5	Temperature=15	Rule-based	Check if temp > 25	Output: 'Not Hot'
6	Temperature=15	Machine learning	Model predicts based on data	Output: 'Likely Not Hot'
7	-	-	-	End of processing

💡 Processing ends after all inputs are evaluated by both systems.

State Tracker

Variable	Start	After 1	After 2	After 3	After 4	After 5	After 6	Final
Input Temperature	N/A	30	20	30	20	15	15	N/A
Rule-based Output	N/A	'Hot'	'Not Hot'	N/A	N/A	'Not Hot'	N/A	N/A
ML Output	N/A	N/A	N/A	'Likely Hot'	'Likely Not Hot'	N/A	'Likely Not Hot'	N/A

Key Insights - 3 Insights

Why does the rule-based system give exact outputs while machine learning gives 'likely' outputs?

Rule-based systems follow fixed rules (see steps 1,2,5 in execution_table) so outputs are certain. Machine learning predicts based on patterns and probabilities (steps 3,4,6), so outputs are probabilistic.

Can the rule-based system learn from new data during execution?

No, rule-based systems do not learn during execution; they only apply predefined rules as shown in the execution_table steps 1,2,5.

Why do both systems process the same input differently?

Because rule-based systems use fixed conditions, while machine learning models use patterns learned from data, leading to different outputs for the same input (compare steps 1 and 3).

Visual Quiz - 3 Questions

Look at the execution_table at step 2. What output does the rule-based system produce for temperature 20?

A 'Hot'

B 'Not Hot'

C 'Likely Hot'

D 'Likely Not Hot'

💡 Hint

Check the 'Rule-based Output' column in variable_tracker after step 2.

At which step does the machine learning system predict 'Likely Hot'?

A Step 1

B Step 3

C Step 5

D Step 6

💡 Hint

Look at the 'ML Output' column in variable_tracker and match with execution_table steps.

If the rule in the rule-based system changed to temp > 20, what would be the output at step 2?

A 'Hot'

B 'Not Hot'

C 'Likely Hot'

D 'Likely Not Hot'

💡 Hint

Refer to step 2 in execution_table and consider the condition change.

Concept Snapshot

Machine learning systems learn patterns from data to predict outputs.
Rule-based systems use fixed rules to decide outputs.
Rule-based outputs are exact; ML outputs are probabilistic.
ML adapts with data; rule-based does not change unless rules are updated.
Both process inputs but differ in flexibility and learning ability.

Full Transcript

This visual execution compares machine learning and rule-based systems. Input data is processed either by applying fixed rules or by using a trained model. The execution table shows step-by-step how each system handles temperature inputs, producing outputs like 'Hot' or 'Likely Hot'. Variables track input values and outputs over steps. Key moments clarify why rule-based outputs are exact and machine learning outputs are probabilistic. The quiz tests understanding of outputs at specific steps and effects of changing rules. The snapshot summarizes the main differences: rule-based systems use fixed rules, machine learning systems learn from data and predict probabilistically.