Table of Contents
- 1. Artificial Intelligence (AI)
- 2. AI Agent
- 3. AI Ethics
- 4. Chatbot
- 5. Backpropagation
- 6. Big Data
- 7. Artificial General Intelligence (AGI)
- 8. BERT
- 9. Convolutional Neural Network (CNN)
- 10. Cross-Validation
- 11. Chain of Thought Prompting
- 12. Data Augmentation
- 13. Deep Learning
- 14. Decision Tree
- 15. Data Mining
- 16. Generative AI
- 17. Hallucination
- 18. Large Language Model (LLM)
- 19. Machine Learning (ML)
- 20. Neural Network
- 21. Natural Language Processing (NLP)
- Final Thoughts
AI Glossary: 20 Need-to-Know Terms Explained
Confused by AI buzzwords? This quick glossary breaks down 20 must-know terms like LLMs, chatbots, deep learning, and more—in plain English.
Let’s be real—AI terms can get confusing fast. You hear things like “LLM,” “backpropagation,” or “chain of thought prompting,” and it’s easy to tune out. This blog is here to change that. Below, I’ve pulled together 20 of the most common (and most useful) AI terms. No tech jargon. No drawn-out definitions. Just clear, quick explanations so you can follow AI conversations with confidence—even if you’re just getting started.
1. Artificial Intelligence (AI)
- What is Artificial Intelligence? AI refers to systems that mimic human intelligence to perform tasks like problem-solving, learning, and decision-making.
- History of Artificial Intelligence: AI began in the 1950s with rule-based systems. Over the decades, it evolved into powerful machine learning models that can analyze data, generate content, and even carry on conversations.
2. AI Agent
- What is an AI Agent? An AI agent is a system that can observe its environment, make decisions, and take action to achieve a specific goal.
- Types of Agents: There are different kinds—simple reactive agents that respond to specific inputs, and more complex utility-based agents that weigh outcomes before acting.
- AI Agents Examples: Think of a self-driving car making lane changes or an AI scheduling assistant that books meetings on your behalf.
3. AI Ethics
- What is AI Ethics? AI ethics is about making sure AI systems are fair, safe, and transparent. It covers issues like bias, data privacy, accountability, and the responsible use of AI in society.
4. Chatbot
- AI Chatbot: A chatbot uses AI to simulate conversation with users. It can answer questions, help with customer service, or guide users through processes.
- Best AI Chatbot: Some of the most effective chatbots include ChatGPT, Google Bard, and Microsoft’s Bing AI—all offering flexible, real-time responses.
- AI Chat: These bots are widely used in websites, apps, and messaging platforms to create fast and human-like support experiences.
5. Backpropagation
- Backpropagation Algorithm: A critical technique for training neural networks. It works by adjusting weights in the network based on the error in predictions. This helps the model learn and improve over time.
6. Big Data
- What is Big Data? Big data refers to enormous sets of data that are too complex for traditional tools. It’s used to train AI systems by providing them with the volume of information they need to find patterns and make accurate predictions.
7. Artificial General Intelligence (AGI)
- What is AGI? AGI is a concept where an AI system would be capable of performing any intellectual task a human can do.
- Artificial General Intelligence Examples: AGI remains theoretical for now, but tools that can adapt across many domains (like advanced personal AI agents) are considered early signs of it.
8. BERT
- BERT Model: BERT stands for “Bidirectional Encoder Representations from Transformers.” It's an NLP model developed by Google that understands the context of words by looking at surrounding text, improving search accuracy and language understanding.
9. Convolutional Neural Network (CNN)
- What is a Convolutional Neural Network? A CNN is a type of deep learning model designed to work with visual data, such as photos or videos.
- Convolutional Neural Network Explained: It breaks down images into layers—detecting edges, textures, and objects—making it ideal for image recognition, facial detection, and even medical imaging.
10. Cross-Validation
- What is Cross Validation? Cross-validation is a technique used to test how well a machine learning model will perform on new data. It helps avoid overfitting by training and testing the model on different subsets of the dataset.
11. Chain of Thought Prompting
- What is Chain of Thought Prompting? This is a method used to get AI to explain its thinking step-by-step.
- Chain of Thought: It’s especially useful for solving complex problems like math or reasoning tasks, because it encourages the AI to “think out loud” and arrive at a better answer.
12. Data Augmentation
- What is Data Augmentation? Data augmentation is the process of creating new training examples by tweaking existing ones.
- Data Augmentation Techniques: In image data, this might include flipping, rotating, or changing brightness. In text, it can mean paraphrasing or translating to boost dataset diversity.
13. Deep Learning
- What is Deep Learning? Deep learning is a subfield of machine learning that uses large neural networks with many layers. These models learn to recognize patterns in data and are behind technologies like speech recognition, recommendation systems, and autonomous driving.
14. Decision Tree
- Decision Tree Algorithm: A decision tree splits data into branches based on simple rules. It’s often used for classification problems—like deciding whether an email is spam or not. The structure makes it easy to interpret and visualize.
15. Data Mining
- What is Data Mining? Data mining is the process of analyzing large sets of data to discover patterns, trends, or relationships. It’s used in marketing, finance, and business intelligence to inform better decision-making.
16. Generative AI
- What is Generative AI? Generative AI creates new content from scratch. This includes generating images, text, music, and even video based on a prompt or input. Tools like Midjourney and ChatGPT are examples of generative AI in action.
17. Hallucination
- AI Hallucination: In AI, a hallucination is when a model generates an answer that sounds correct but is actually false or made up.
- What is an Example of a Hallucination? For instance, an AI claiming that a person wrote a book they never wrote, or inventing statistics that don’t exist.
18. Large Language Model (LLM)
- What is a Large Language Model? LLMs are AI models trained on huge amounts of text. They can understand, summarize, and generate human-like language.
- LLM Full Form: Large Language Model—used in tools like ChatGPT, Gemini, Claude, and more.
19. Machine Learning (ML)
- What is Machine Learning? Machine learning is a method where computers learn patterns from data and use that knowledge to make decisions or predictions.
- Machine Learning Algorithms: These include decision trees, neural networks, linear regression, and clustering models—each suited to different types of tasks.
20. Neural Network
A neural network is a system of layers that process data, inspired by how the human brain works. It’s used in many AI applications, from image recognition to text generation. Deep learning uses neural networks with many layers to solve complex problems.
21. Natural Language Processing (NLP)
- What is NLP? NLP helps machines understand, interpret, and respond to human language.
- NLP Meaning: Natural Language Processing is behind chatbots, voice assistants, translation tools, and even smart email replies.
Final Thoughts
You don’t need to be a data scientist to speak the language of AI. Learning these key terms is a great way to build a solid foundation and feel a little more in control when navigating AI tools. Whether you’re exploring ai writing tools, trying out a chatbot, or curious about how machine learning works behind the scenes, these glossary terms will come in handy. Bookmark this page, revisit it when needed, and keep learning as the world of AI keeps growing.
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Vaidant Agrawal
When he's not geeking out about AI, Vaidant is busy breaking down complex tech into bite-sized stories that actually make sense to humans.