AI Terminology & Abbreviations Commonly Used

AI has a lot of abbreviations and when you start learning, you will encounter them at every step. In fact, AI itself is abbreviated from Artificial Intelligence. Don’t get lost when you hear them and to help you with that, I’ve listed the most commonly used AI terminology and what it stands for. 

• AI - Artificial Intelligence
• ML - Machine Learning
• DL - Deep Learning
• ANI - Artificial Narrow Intelligence
• AGI - Artificial General Intelligence
• ASI - Artificial Superintelligence
• XAI - Explainable AI
• NN - Neural Network
• ANN - Artificial Neural Network
• CNN - Convolutional Neural Network
• RNN - Recurrent Neural Network
• LSTM - Long Short-Term Memory
• GRU - Gated Recurrent Unit
• GAN - Generative Adversarial Network
• MCP - Model Context Protocol
• VAE - Variational Autoencoder
• SVM - Support Vector Machine
• k-NN - k-Nearest Neighbors
• PCA - Principal Component Analysis
• NLP - Natural Language Processing
• NLU - Natural Language Understanding
• NLG - Natural Language Generation
• LLM - Large Language Model
• SLM - Small Language Model
• VLM - Vision Language Model
• LMM - Large Multimodal Model
• RAG - Retrieval Augmented Generation
• GPT - Generative Pre-trained Transformer
• BERT - Bidirectional Encoder Representations from Transformers
• T5 - Text-to-Text Transfer Transformer
• CLIP - Contrastive Language–Image Pre-training
• NER - Named Entity Recognition
• S2S - Sequence-to-Sequence
• TF-IDF - Term Frequency-Inverse Document Frequency
• CV - Computer Vision
• OCR - Optical Character Recognition
• RL - Reinforcement Learning
• RLHF - Reinforcement Learning from Human Feedback
• DPO - Direct Preference Optimization
• PPO - Proximal Policy Optimization
• DQN - Deep Q-Network
• MDP - Markov Decision Process
• API - Application Programming Interface
• SDK - Software Development Kit
• GPU - Graphics Processing Unit
• TPU - Tensor Processing Unit
• NPU - Neural Processing Unit
• MLOps - Machine Learning Operations
• ETL - Extract, Transform, Load
• EDA - Exploratory Data Analysis
• ROC - Receiver Operating Characteristic
• AIaaS - AI as a Service
• AGI Safety - Artificial General Intelligence Safety
• AI TRiSM - AI Trust, Risk, and Security Management

I’ve also created the below infographics with the most common AI terms you can take a printout and share with your friends.

AI Terms & Definitions

Next, I've listed the popular AI terms and their definition in easy language to help you understand.

Artificial Intelligence (AI): The science of making computers or machines perform tasks that usually require human intelligence, like learning or problem-solving.

Machine Learning (ML): A way for computers to learn from experience, by finding patterns in data rather than following fixed rules.

Deep Learning (DL): A type of machine learning that uses many layers of computer “neurons” to understand more complicated things, such as recognizing faces or understanding language.

Neural Network: A computer system designed to imitate how the human brain works, with layers of artificial “neurons” that process information.

Model: A program or mathematical formula that is trained using data so it can make predictions or decisions.

Natural Language Processing (NLP): A field of AI that helps computers understand, interpret, and generate human language, like text or speech.

Large Language Model (LLM): A very big AI model that’s been trained on huge amounts of data (text, images, videos, etc)  so it can answer questions, write stories, or chat like a person.

Computer Vision (CV): A field of AI that allows computers to see and understand pictures and videos.

AI Chat Client: An app that lets you chat with an AI model. It provides a chat window where you type questions or messages and the AI responds, like chatting with a virtual assistant. Examples: ChatGPT, Gemini.

Multi AI Chat Client: An app or chat program that lets you talk to several AI models at once, or lets you switch between AIs in the same chat window. This way, you can compare answers, use different AI services, or get help from the best-suited AI for each task. Example: Geekflare AI.

Bias: When an AI system makes unfair decisions because the data it was trained on was incomplete or unbalanced.

Algorithm: A clear list of steps or instructions computers follow to solve a problem or process data.

Chatbot: A program that can have a conversation with humans, usually through text or speech.

Generative AI: AI that can create new things, such as writing stories, making pictures, or composing music by learning from lots of examples.

Reinforcement Learning (RL): A type of machine learning where an AI agent learns to make decisions by getting rewards or penalties for its actions, kind of like learning a game.

Classifier: A tool in AI that sorts data into different groups or categories (like sorting emails into spam or not spam).

RAG (Retrieval-Augmented Generation): A way for AI to answer questions better by searching reliable documents or sources (retrieval) and then using the information to write a helpful answer (generation). It combines both searching and generating.

Context: The background information or conversation history that helps AI understand what’s going on. For example, in a chat, previous messages provide context for the current question.

Token: A small piece of text such as a word or part of a word that an AI model uses to process language. Long sentences are broken into many tokens, so the AI can understand them.

AI memory: The ability of an AI system to remember information from previous interactions or within a conversation, so it can give better, more relevant answers.

GPU (Graphics Processing Unit): A special computer chip designed to handle lots of calculations at once, making it very useful for training and running AI models fast.

NLP (Natural Language Processing): A branch of AI that helps computers understand, interpret, and respond to human language, written or spoken.

Intent: The goal or purpose behind what a person says or types; for example, if someone says "Book a flight" the intent is to make a flight reservation.

NLG (Natural Language Generation): An AI technique for creating human-like text or responses based on specific data or instructions.

NLU (Natural Language Understanding): A part of NLP focused on helping computers truly understand the meaning behind human language inputs.

Parameter: A value or setting inside an AI model that is learned from data during training and controls how the model makes decisions.

Semantic search: A search method where AI tries to find the true meaning behind your query, not just match exact words, so it gives smarter, more relevant results.

MCP (Model Context Protocol): A set of rules or a system for how AI tools and models share and use background information (context) with each other. It is like an API but for AI.

AI Gateway: A system or service that manages the connection between users and multiple AI models or tools, often directing requests to the most suitable AI.

Multimodel: Describes AI systems built from or able to use more than one type of model, like combining models for text, images, and audio to give more complete answers. 

AI function: A specific task or mini-program within an AI system that does one job, such as translating text, summarizing an email, or detecting objects in a photo.

Evals (Evaluations): Tests or checks that measure how well an AI model is performing, to make sure it gives accurate, helpful results.

Prompt: What you type or say to an AI system to tell it what you want, like a question or an instruction. 

Prompt Chaining: The process of connecting multiple prompts together, where the answer to one becomes the question or starting point for the next, creating step-by-step reasoning.

Hallucination: When an AI system makes up information that sounds convincing but is actually wrong or not based on real data.

Vector database: A special kind of database that stores information as numbers (vectors) so AI can quickly compare things and find items that are similar, useful for things like finding related images or documents.

AI inference: The process of using a trained AI model to make predictions, answer questions, or perform tasks with new information.

AI agent: A program that can make decisions or take actions to reach a specific goal, often working on its own and sometimes communicating with people or other agents.

AI humanizer: A tool or technique that changes or adapts AI-generated content so it sounds more natural and human-like, instead of robotic or artificial.

AI detector: A tool that analyzes text, images, or videos to tell if they were created by AI or by a real person.

Embedding: A way of turning words, sentences, or images into numbers (vectors) that capture their meaning, so the AI can compare them more easily.

Similarity Search: Finding things like texts or images that are most alike, often using vectors and embeddings.

Fine-tuning: Further training an existing AI model on new, often more specific data to improve it for a particular job or field.

Grounding: Making sure AI answers are connected to actual facts, sources, or real data, to reduce mistakes or hallucinations.

Zero-shot learning: When an AI model can do a new task it wasn't directly trained for, just by understanding the instructions or context.

Conversational AI: AI designed for talking with people in a natural back-and-forth way, like chatbots and virtual assistants.

AI Assistant: An AI-powered helper that can answer questions, help schedule appointments, send emails, or perform other tasks, all by chatting with you. Examples include Siri, Alexa, and Google Assistant.

Context window: The limit to how much recent conversation or text an AI chat client can remember at one time. If the conversation gets too long, old parts might be forgotten.

Model switching: A feature in some multi AI chat clients like Geekflare AI that lets you easily change between different AI models to get different perspectives or answers.

 I hope this gives you a fair idea about AI terminology and helps you in AI related tasks.