What is the difference between AI, Machine Learning, Deep Learning, and LLMs?

These concepts are similar to a Russian nesting doll, with each concept being a specific subset of the preceding one. They both have the same aim of emulating human intelligence in their respective ways and with different technologies.

1.Artificial Intelligence (AI)

The most general term. Any computer program or machine that imitates human thinking, learning and problem-solving. It has the power of simple rule-based chatbots all the way to advanced robotics. 

Examples: Voice assistants , Recommendation systems , Self-driving cars , Chatbots , Expert systems

Goal: Make machines capable of reasoning, learning, problem-solving, decision-making, and understanding language

2. Machine Learning (ML)

A particular subset of Artificial Intelligence. Whereas hard-coding specific rules for a specific task, ML algorithms are able to learn from large datasets and identify patterns and make predictions independently.

Traditional Programming : Rules + Data → Output

Machine Learning : Data + Output → Learn Rules

Examples: Spam email detection , Fraud detection , Product recommendations , Customer churn prediction

Popular Algorithms: Linear Regression , Decision Trees , Random Forest ,Support Vector Machines (SVM)

3. Deep Learning (DL)

A specialized area of Machine Learning. It employs large, layered “neural networks,” that resemble the human brain. This enables the computer to analyse extremely complex information (such as images or speech) without having to be manually labelled first.

Key Idea: Instead of manually selecting features, deep learning automatically learns complex patterns from large amounts of data.

Example:

For image recognition:

Traditional ML : Human defines features (edges, shapes, colors)

Deep Learning : Neural network learns features automatically

Applications: Image recognition , Speech recognition , Face detection , Autonomous vehicles , Natural Language Processing (NLP)

Popular Models: CNNs (Convolutional Neural Networks) , RNNs (Recurrent Neural Networks) , Transformers

4.Large Language Models (LLMs)

Large Language Models (LLMs): One of the most specialized deep learning models. By using a deep learning model called “transformers,” LLM’s can generate, understand and predict human language by being trained on an enormous amount of text. These can include OpenAI’s GPT or Anthropic’s Claude.

Built Using: Deep Learning , Transformer architecture

What LLMs Can Do:

Answer questions

Summarize documents

Translate languages

Generate code

Write content

Power AI agents

Examples: OpenAI’s ChatGPT , Claude ,Gemini , Llama