1

Based on Capabilities

This classification focuses on how intelligent and capable the AI is compared to humans.

1. Narrow AI (Weak AI)

Definition

AI designed for a specific task.

Examples
  • Siri, Alexa (voice assistants)
  • Google Maps (navigation)
  • ChatGPT (language generation)
Limitations

Cannot generalize knowledge beyond its trained task.

2. General AI (Strong AI)

Definition

Hypothetical AI that can perform any intellectual task a human can.

Current Status

Does not yet exist.

Goal

Reason, learn, and apply knowledge across domains autonomously.

3. Super AI

Definition

A future AI that surpasses human intelligence in all aspects.

Capabilities

Creative thinking, emotional intelligence, decision-making better than humans.

Status

Purely theoretical as of now.

2

Based on Functional Types

This classification looks at how the AI functions and behaves.

1. Reactive Machines

Behavior

Responds to specific inputs with programmed rules. No memory or learning.

Examples

IBM's Deep Blue chess-playing computer.

2. Limited Memory

Behavior

Can use past data to make decisions but has short-term memory.

Examples

Self-driving cars, chatbots, recommendation systems.

3. Theory of Mind (In development)

Behavior

Would understand human emotions, beliefs, and intentions.

Goal

Interact socially with humans.

4. Self-aware AI (Theoretical)

Behavior

Possesses consciousness and self-awareness.

Status

A future concept. Not yet developed.

3

Based on Technologies/Approaches

This classification looks at the technical methods used to create AI.

1. Machine Learning (ML)

Definition

AI that learns patterns from data.

Types
  • Supervised Learning: Trained on labeled data (e.g., spam filters).
  • Unsupervised Learning: Finds patterns in unlabeled data (e.g., clustering).
  • Reinforcement Learning: Learns by reward and punishment (e.g., game-playing AIs like AlphaGo).

2. Deep Learning

Definition

Subset of ML using artificial neural networks (like the human brain).

Examples

Image recognition, speech-to-text, language models like GPT.

3. Natural Language Processing (NLP)

Definition

AI that understands and generates human language.

Examples

ChatGPT, Google Translate, sentiment analysis tools.

4. Computer Vision

Definition

AI that interprets visual information from the world.

Examples

Facial recognition, object detection, autonomous vehicles.

5. Expert Systems

Definition

Rule-based systems that emulate decision-making of human experts.

Examples

Medical diagnosis systems.

6. Robotics

Definition

Integration of AI with physical machines.

Examples

Industrial robots, surgical robots, household robots.

4

Specialized AI Types (By Domain)

1. Conversational AI

Purpose

Interact via voice or text.

Examples

ChatGPT, Alexa, customer support bots.

2. Generative AI

Purpose

Create new content (text, images, music, video).

Examples

DALL·E (images), ChatGPT (text), Sora (video), MusicLM (music).

3. Cognitive Computing

Purpose

Simulate human thought processes.

Used In

Healthcare, legal analysis, decision support.

4. Autonomous Systems

Purpose

Operate independently in the real world.

Examples

Drones, self-driving cars, delivery robots.