#named entity recognition tutorial

Artificial Intelligence (AI) is no longer a niche field—it’s the backbone of modern innovation. Whether you're building chatbots, analyzing images, or automating workflows, understanding the core concepts of AI is essential.

This guide breaks down 30 foundational AI concepts, categorized across natural language processing, computer vision, multimodal systems, and more. It’s your go-to reference for mastering the building blocks of intelligent systems.

🗣️ Natural Language Processing (NLP) & Text-Based AI

1. Question Answering

AI retrieves answers from documents based on user queries. 📌 Use case: Chatbots, search engines

2. Information Extraction

Extracts structured data from unstructured text. 📌 Use case: Resume parsing, legal document analysis

3. Retrieval-Augmented Generation (RAG)

Combines semantic search with LLMs for accurate responses. 📌 Use case: Enterprise Q&A systems

4. Text Summarization

Generates concise summaries from long documents. 📌 Use case: News aggregation, legal briefs

5. Language Translation

Converts text between languages using neural models. 📌 Use case: Multilingual support, global content

6. Named Entity Recognition (NER)

Identifies names, places, and organizations in text. 📌 Use case: Financial analysis, medical records

7. Semantic Search

Finds relevant documents based on meaning, not keywords. 📌 Use case: Knowledge bases, internal search tools

8. Natural Language Processing (NLP)

Broad field focused on understanding and generating human language. 📌 Use case: Sentiment analysis, chatbots

9. Code Generation

Generates code from natural language instructions. 📌 Use case: Developer copilots, automation tools

🧬 Multimodal & Speech AI

10. Text-to-Speech (TTS)

Converts written text into spoken audio. 📌 Use case: Accessibility tools, voice assistants

11. Multi-Modal AI

Combines text, image, and audio inputs for richer understanding. 📌 Use case: AI agents, content moderation

12. Human-in-the-Loop (HITL)

Uses human feedback to improve model accuracy. 📌 Use case: Reinforcement learning, safety tuning

13. Tool Use / Function Calling

AI agents invoke external tools or APIs to complete tasks. 📌 Use case: Autonomous agents, workflow automation

🖼️ Computer Vision & Image-Based AI

14. Computer Vision

Enables machines to interpret visual data. 📌 Use case: Surveillance, autonomous vehicles

15. Optical Character Recognition (OCR)

Extracts text from images or scanned documents. 📌 Use case: Invoice processing, digitization

16. Object Detection

Identifies and locates objects in images. 📌 Use case: Retail analytics, robotics

17. Image Captioning

Generates descriptive text for images. 📌 Use case: Accessibility, content tagging

📊 Data Science & Forecasting

18. Time Series Forecasting

Predicts future values based on historical data. 📌 Use case: Stock prediction, demand planning

19. Task Classification

Categorizes tasks or inputs into predefined labels. 📌 Use case: Email sorting, workflow automation

20. Knowledge Graphs

Represents relationships between entities in a graph format. 📌 Use case: Search engines, recommendation systems

What is Retrieval-Augmented Generation (RAG)?

RAG combines semantic search with generative models to produce more accurate and context-aware responses.

How does semantic search differ from keyword search?

Semantic search understands meaning and context, while keyword search matches exact terms.

What is multimodal AI?

Multimodal AI processes multiple types of input—like text, images, and audio—simultaneously for richer understanding.

Can AI generate code?

Yes. Tools like GitHub Copilot and OpenAI’s Codex can generate code from natural language prompts.

What are knowledge graphs used for?