AI Algorithms and Techniques — Definition, Types, Examples & MCQs

Definition

Artificial Intelligence (AI) algorithms are step-by-step methods or instructions that help machines perform tasks requiring human-like intelligence such as learning, decision-making, and problem-solving.

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These algorithms are the backbone of AI systems. They allow computers to process vast amounts of data, recognize patterns, and make decisions with or without human guidance. Depending on how transparent and interpretable they are, AI algorithms can be divided into two broad categories:

Explainable (Whitebox) Algorithms → Transparent and easy to understand.
Unexplainable (Blackbox) Algorithms → Complex and hard to interpret.

1. Explainable (Whitebox) Algorithms

Whitebox algorithms are simple, transparent, and allow humans to understand how decisions are made. They are widely used in education, finance, healthcare, and other fields where interpretability and trust are very important.

a) Decision Trees

Decision Trees work like a flowchart with a series of “yes/no” questions. Each question leads to another branch until a decision or classification is reached.
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They are especially useful for classification and regression tasks.

Example: Identifying an animal:

Does it have feathers? → Yes → It’s a bird.
Does it have fur? → Yes → It’s a mammal.

Extra[You can skip this during writing on notebooks. It is just an extra knowledge.] Decision Trees are commonly used in medical diagnosis, credit scoring, and fraud detection because they are simple to explain to non-technical stakeholders.

b) Linear Regression

Linear Regression is one of the simplest and most widely used algorithms in AI and statistics. It finds a straight-line relationship between two or more variables. The model tries to fit the best line through the data points, minimizing the error.

Example: Study time vs. grades. The more hours you study, the higher your expected score.

Linear regression is used in predicting house prices, forecasting stock markets, and even in sports analytics to evaluate performance trends.

c) Rule-Based Systems

Rule-based systems are among the earliest AI methods. They rely on human-written rules in the form of "if-then" conditions to make decisions.

Example: In a game: If obstacle ahead → then jump.

Extra[You can skip this during writing on notebooks. It is just an extra knowledge.] These systems are still used in chatbots, medical expert systems, and automated troubleshooting because they are easy to build and explain, although they lack flexibility compared to modern AI.

2. Unexplainable (Blackbox) Algorithms

Blackbox algorithms are advanced algorithms where the decision-making process is very complex and difficult for humans to interpret.
Extra[You can skip this during writing on notebooks. It is just an extra knowledge.] While they are extremely powerful, they lack transparency. This can be problematic in fields where ethical decisions or fairness are important.

a) Neural Networks

Neural Networks are inspired by the human brain. They consist of interconnected layers of nodes (neurons) that process information. They are especially strong in pattern recognition.

Applications: Facial recognition, spam email detection, handwriting recognition.

The drawback of neural networks is their complexity. While they can achieve high accuracy, it is often difficult to explain exactly why they made a certain decision.

b) Deep Learning

Deep Learning is a subset of machine learning that uses multiple layers of neural networks to model complex patterns in data. Each layer extracts features and passes them to the next, creating highly sophisticated models.

Applications: Voice assistants (Siri, Alexa), self-driving cars, advanced medical image analysis.

While Deep Learning systems achieve state-of-the-art performance in many tasks, they are often referred to as "black boxes" because even experts struggle to interpret how the layers contribute to the final decision.