AI vs Conventional Algorithms

There are two broad types of algorithms used in software: conventional algorithms and AI algorithms. This section gives a brief comparison between the newer, increasingly popular AI-based algorithms and the older, conventional approach.

1. Conventional algorithms

A conventional algorithm is used when we already know exactly how to solve a problem.

We can clearly write down:

the steps
the rules
the calculations
the decisions

Some examples include:

adding two numbers
calculating the area of a circle
sorting names alphabetically
checking temperature and turning a device on or off

2. AI algorithms

AI algorithms on the other hand, are used when the rules are not clearly defined or are too difficult to explicitly write down.
In many real-world problems, humans can recognize patterns easily, but it is hard to express exact rules step by step.

Examples:

Identifying one person from another
Distinguishing dog breeds
Voice recognition
Handwrite recognition

This is difficult because features can overlap:

For example,there may not clear-cut boundaries to distinguish between two different animals
or
There may not be a way to write down how voice of people vary and how humans recognize them

Since these features can be subjective, there is no single rule set that works for everyone.
Instead of fixed rules, AI systems learn patterns from data and examples.

3. How AI Learns from data

For conceptual understnading, one example of an AI algorithms is mentioned below (there are many more algorithms nowadays).
Instead of manually writing strict rules, AI systems learn patterns from large amounts of examples and data.

Step 1: Collect data
Large numbers of samples are collected.

Step 2: Label data
Each sample is labeled with the correct output (e.g., husky, wolf, cat, dog).

Step 3: Train the model
An algorithm extracts patterns and uses optimization techniques to improve accuracy.

Step 4: Generate the model
The final result is a set of numerical values called weights, which represent learned patterns.

The model is not a set of human-written rules, but a learned numerical system.
The irony is that humans often cannot directly interpret what these weights mean, even though the system performs well.
This is why AI is often called a black box, though there are esearches going on to improve this, such as "Explainable AI"
So AI learns patterns instead of fixed logic.

4. Algorithms that train AI block boxes (models)

AI systems are trained using optimization-based algorithms (such as gradient descent) that adjust weights to reduce errors.

Examples	In these cases
Neural Networks Support Vector Machines (SVM) Decision Trees Random Forests Gradient Boosting Machines XGBoost / LightGBM / CatBoost	Learn patterns from data

5. When to use AI (and when not to)

When AI is NOT needed (clear rules)

AI is unnecessary when rules are already clearly defined.

Examples	In these cases
Calculating interest Basic arithmetic Unit conversion Sorting data Fixed rule-based decisions	Rules are explicit Outcomes are predictable Conventional algorithms are better suited

When AI IS needed (unclear rules)

AI is useful when rules are unclear or subjective.

Examples	In these cases
Face recognition Speech recognition Image classification Spam detection Recommendation systems	Features overlap Definitions vary between humans Rules cannot be explicitly written

Takeaway

Conventional algorithms follow explicit human-defined rules, while AI algorithms learn patterns from data when those rules are unclear, overlapping, or hard to define.