---
title: "AI glossary: 130+ AI terms that you should know"
description: "Here are 130+ easy-to-understand definitions for the most important AI terms"
canonical: "https://plusai.com/blog/ai-glossary/"
---

Resources

# AI glossary: 130+ AI terms that you should know

Here are 130+ easy-to-understand definitions for the most important AI terms

Dan Li September 3, 2025

Trying to understand AI? Start from the basics with our glossary of 100+ AI terms.

‍

**AI Alignment** - Effort to ensure that AI systems behave in ways that align with human values and intentions.

**AI Assistant** - AI-powered tools designed to assist users with various tasks, such as answering questions, providing recommendations, or offering guidance.

**AI Auditing** - Process of evaluating AI systems for compliance with ethical standards, performance metrics, and regulatory requirements.

**AI Bias** - Systematic errors in AI systems that can lead to unfair or discriminatory outcomes, often reflecting prejudices in training data.

**AI Governance** - Policies, practices, and structures for managing the development and use of AI technologies responsibly.

**AI Risk Management** - Strategies and practices to identify, assess, and mitigate potential negative impacts of AI systems.

**AI Safety** - Field focused on developing AI systems that are reliable, controllable, and do not cause unintended harm.

**Algorithmic Fairness** - Approaches to ensure AI systems make decisions without discriminating against particular groups or individuals.

**Artificial General Intelligence (AGI)** - A theoretical AI system that can rival and exceed the cognitive abilities of human beings across a wide variety of tasks.

**Artificial Intelligence (AI)** - Computer systems that can create, reason, and solve problems that would have historically required a human to perform.

**Artificial Super Intelligence (ASI)** - Hypothetical AI that surpasses human intelligence across all domains, raising profound ethical and existential questions.

**Attention (aka attention mechanism or attention model)** - Mechanism in neural networks that allows AI models to focus on specific parts of input data, improving performance on tasks like language translation and image analysis.

**Back Propagation** - Algorithm in machine learning that trains neural networks by calculating errors and adjusting weights backward through layers.

**BERT (Bidirectional Encoder Representations from Transformers)** - A transformer-based AI model that processes text by looking at both the preceding and following words for each token, aka bidirectional training.

**Big Data** - Extremely large datasets that require specialized AI techniques and tools for processing and analysis.

**Chain of Thought** - AI prompting technique that encourages language models to reason step-by-step when problem-solving, to improve output quality.

**Chatbot** - AI-powered tools like ChatGPT that are designed to engage in natural-sounding conversations with humans.

**Chinese Room** - A thought experiment challenging the notion that AI systems truly understand language, questioning the nature of machine intelligence.

**Compute** - Processing power and resources required for running AI tasks that influence the scale and complexity of models.

**Computer Vision** - Field of AI that trains computers to interpret and understand visual information from the world.

**Convolutional Neural Networks (CNNs)** - Specialized neural networks designed for processing grid-like data, particularly effective for image analysis tasks.

**Contrastive Language-Image Pre-training (CLIP)** - An NLP model that learns to connect text and images, enabling versatile visual recognition tasks.

**Context Window** - The amount of information an AI model can consider at once when making predictions or responses.

**Conversational AI** - AI systems that can interact with people in a natural, conversational manner, typically through a chat interface.

**Custom Model** - An AI model trained on specific data to perform a particular task, often customized for the unique needs of an individual or organization.

**Data Augmentation** - The process of artificially increasing the quantity or diversity of training data, helping AI models generalize better to new situations.

**Data Labeling** - The process of adding descriptive tags to raw data, allowing AI systems to learn from labeled examples.

**Data Preprocessing** - The process of cleaning and formatting raw data, making it suitable for AI model training and improving learning outcomes.

**Dataset** - A collection of information used to train and test AI models, enabling them to learn patterns and make predictions.

**Deep Learning** - A machine learning technique that uses neural networks to analyze and learn from large amounts of data.

**Deepfake** - AI image generation technique that replaces a person in an existing image or video with someone else’s likeness.

**Diffusion** - AI model that generates images by gradually denoising random patterns, producing high-quality and diverse outputs.

**Double Descent** - A phenomenon in machine learning where model performance initially improves, then worsens and improves again as model complexity increases.

**Embedding** - A machine learning technique that converts discrete data into number vectors, helping AI models understand relationships between items.

**Emergence** - A phenomenon where complex AI behaviors arise from simple rules or interactions, often in unexpected ways.

**End-to-End Learning** - A machine learning technique where a model learns all steps of a task directly from input to output, without manually designed intermediate stages.

**Ethical AI** - Guidelines and standards for developing and deploying AI systems in ways that respect human rights and values.

**Explainable AI (XAI)** - Approaches that make AI decision-making processes understandable to humans, enhancing transparency and trust.

**Expert Systems** - AI programs that emulate human expert decision-making in specific domains, using predefined rules and knowledge bases.

**Few-Shot Learning** - A machine learning technique where a model is trained to achieve good performance with only a small number of training examples.

**Fine-tuning** - The process of refining a pre-trained AI model by training it on a smaller dataset to enhance performance on specific tasks.

**Federated Learning** - Approach to train AI models across multiple devices or servers, preserving data privacy by keeping raw data localized.

**Forward Propagation** - Algorithm in machine learning where input data flows through a neural network’s layers to produce an output or prediction.

**Foundation Model** - Large-scale AI model trained on vast amounts of data, serving as a versatile base for various downstream tasks. Foundation models typically cost hundreds of millions of dollars to train.

**Frequency Penalty** - An AI parameter that discourages the model from repeatedly generating the same words or phrases.

**Generative AI** - AI systems that can create new content, such as images, videos, or text, based on patterns learned from its training data.

**Generative Pre-trained Transformer (GPT)** - An artificial neural network based on the transformer architecture that can generate human-like text by predicting the next word in a sequence.

**Gradient Descent** - Optimization method used in machine learning to train models by repeatedly adjusting parameters to minimize errors.

**GPU** - Specialized processor optimized for parallel computation to accelerate AI model training and inference.

**Hallucinate/Hallucination** - A phenomenon in which an AI model generates content that is not included in its training, resulting in output that’s nonsensical or false.

**Hidden Layer** - The middle layer in a neural network that learns and represents complex features from the input data.

**Hyperparameter Tuning** - The process of setting the optimal configuration settings for an AI model to achieve the best performance.

**Image Captioning** - AI image processing technique that generates natural language descriptions of the content in images.

**Image Colorization** - AI image processing technique that adds color to grayscale or black-and-white images.

**Image Denoising** - AI image processing technique that removes noise or unwanted artifacts from images to improve their quality.

**Image Generation** - AI image processing technique of creating new images from scratch, often based on text descriptions or other images.

**Image Inpainting** - AI image processing technique for reconstructing missing or damaged parts of an image.

**Image Recognition** - AI image processing technique that identifies and classifies objects or scenes in digital images.

**Image Segmentation** - AI image processing technique that divides an image into multiple segments or objects, identifying the pixels that belong to each.

**Image-to-Image Translation** - AI image processing technique that takes an image input (e.g. photo) and uses it to generate a new image.

**Image Upscaling** - AI image processing technique that increases the resolution of an image while maintaining or improving quality.

**Incremental Learning** - Capability of an AI system to continuously update its knowledge from new data without forgetting previously learned information.

**Inference** - Once an AI model has been trained, inference is the process of using the trained model to generate new predictions or outputs.

**Instruction Tuning** - The process of guiding a pre-trained language model to produce higher-quality outputs by using better prompts and examples.

**Large Language Model (LLM)** - An AI model trained on large amounts of text, capable of understanding and generating human-like language.

**Latent Space** - A compressed representation of data learned by a model that captures its most important features and patterns.

**Loss Function** - A method for quantifying the difference between a model’s predicted outputs and the actual values.

**Machine Learning (ML)** - A subset of AI that uses algorithms and statistics to identify patterns and extract insights from data without explicit programming.

**Machine Translation** - The use of machine learning algorithms to automatically translate text from one language to another.

**Mixture of Experts** - An AI model that combines multiple specialized models (experts) to solve different parts of a problem, with a gating network selecting the appropriate expert for a given input.

**Model Compression** - Techniques used to reduce the size and computational needs of AI models so they can be deployed on resource-constrained devices.

**Model Deployment** - The process of integrating a trained AI model into a production environment, making it available for real-world use.

**Model Evaluation** - Assessment of an AI model’s performance using test data, ensuring it meets accuracy and reliability standards.

**Model Explainability** - Techniques used to understand and interpret how an AI model arrives at its decisions, promoting transparency and trust.

**Model Interpretability** - Techniques used to measure how easily humans can understand the reasoning behind an AI model’s predictions, aiding in validation and improvement.

**Model Monitoring** - Continuous observation of deployed AI models to ensure consistent performance and detect potential issues or drift.

**Model Training** - The process of teaching an AI model to make accurate predictions by exposing it to labeled examples.

**Model Versioning** - System for tracking different iterations of AI models, facilitating collaboration and enabling rollback if needed.

**Multimodal AI** - AI systems that can process and output content in mixed data formats (e.g., text, images, audio).

**Named Entity Recognition (NER)** - An NLP technique that identifies and classifies named entities (like persons, organizations, locations) in text.

**Natural Language Processing (NLP)** - A subset of AI that focuses on enabling computers to understand, interpret, and generate human language.

**NeRF (Neural Radiance Fields)** - An artificial neural network that creates 3D scenes from 2D images by learning how light interacts with objects in the scene from different angles.

**Objective Function** - A method used in optimization problems (like machine learning) to define the goal that the algorithm aims to achieve.

**Overfitting** - Overfitting occurs when a model corresponds too closely to its training data and performs poorly on new data and predictions.

**Parameters** - The internal settings of an AI model that determine how it processes input data and generates output.

**Part-of-Speech (POS) Tagging** - An NLP technique that labels words in a text with their grammatical categories (e.g., noun, verb, adjective).

**Presence Penalty** - An AI parameter that discourages the model from generating words that have already appeared in the previous text.

**Pre-training** - The initial training phase of a language model on a large dataset of text and code before fine-tuning on a specific task.

**Post-training** - The training phase after a language model is pre-trained of “tuning” outputs using supervised fine-tuning on specific input output pairs and reinforcement learning based on human feedback to improve the quality of the model’s responses.

**Prompt** - The initial text input given to an AI model, which sets the context and guides the model’s output.

**Prompt Engineering** - The process of designing and refining prompts to extract the best responses from AI models.

**Prompt Injection** - The act of creating malicious AI prompts to manipulate an AI model into generating unintended or harmful outputs.

**Prompt Leaking** - A prompt injection attack that tricks an AI model into disclosing its system prompt, which can contain confidential or sensitive information.

**Reinforcement Learning** - A machine learning technique where an agent learns to make decisions by receiving rewards or penalties for actions taken in an environment.

**Reinforcement Learning from Human Feedback (RLHF)** - An AI technique in which human feedback is used to guide the learning process in reinforcement learning algorithms.

**Regularization** - An AI technique used to prevent overfitting by adding constraints to a machine learning model.

**Responsible AI by Design** - Approach that integrates ethical considerations and societal impact into every stage of AI system development.

**RoBERTa (Robustly Optimized BERT Pretraining Approach)** - An optimized version of BERT that is trained on more data for longer periods, resulting in better performance on text understanding tasks.

**Sentiment Analysis** - An NLP technique that determines the emotional tone or opinion expressed in a piece of text.

**Singularity** - Hypothetical point in time where AI surpasses human intelligence, leading to unpredictable technological and societal consequences.

**Stop Sequences** - Specific words or phrases that signal an AI model to stop generating text.

**Style Transfer** - AI image processing technique that applies the artistic style of one image to the content of another.

**Supervised Learning** - A machine learning technique where a model is trained on labeled data, learning to predict the output from the input.

**Symbolic Artificial Intelligence** - AI approach based on explicit representation of knowledge and logic-based reasoning, contrasting with machine learning methods.

**Temperature** - An AI parameter that controls the randomness of the output; lower temperatures make outputs predictable, while higher temperatures encourage more creative and unexpected outputs.

**TensorFlow** - Open-source software library for implementing and deploying machine learning models across various platforms.

**Token** - The smallest meaningful unit of text that an AI model can process, often representing a character or a word.

**Token Limit** - The maximum number of tokens a language model can process in a single input or generate in a single output.

**Top-k Sampling** - An AI technique that selects the next word from the top k most likely options, keeping the output more predictable.

**Top-p (Nucleus Sampling)** - An AI technique that selects the next word from the smallest set of most likely next words whose cumulative probability exceeds a threshold p.

**TPU** - Custom-built processor designed by Google for efficient execution of machine learning workloads.

**Transfer Learning** - A machine learning technique where a pre-trained model is adapted to a new, but related task, using its existing knowledge.

**Transformer** - An artificial neural network architecture that can track relationships for sequential data to transform input sequences into output sequences.

**Turing Test** - A test for intelligence proposed by Alan Turing, where intelligence is defined as the inability for a human to determine whether or not it is talking to another human or a computer.

**Underfitting** - Underfitting occurs when a model is too simple to capture the underlying relationships in the training data, resulting in poor performance on new data.

**Unsupervised Learning** - A machine learning technique that finds hidden patterns or intrinsic structures in input data without labeled responses.

**Validation Data** - A separate portion of a dataset that is not shown to the model during training in order to assess the model’s performance.

**XLNet** - A language model that enhances context understanding by predicting words in every possible order.

**Zero-Shot Learning** - A machine learning technique that refers to a trained model’s ability to correctly make predictions on new classes or tasks it hasn’t seen before during training.

## More in Resources

-   [
    
    Resources
    
    ### The evolution of the pitch deck (with real decks from LinkedIn, YouTube, and Airbnb)
    
    See how startup pitch decks changed from typed business plans to AI drafts, with real decks from LinkedIn, YouTube, Airbnb, and more.
    
    October 2, 2026
    
    
    
    ](https://plusai.com/blog/evolution-of-pitch-deck)
-   [
    
    Resources
    
    ### How to Convert a Document to a Presentation With AI
    
    See how to convert a document to a presentation with AI using Plus AI, with step-by-step instructions, examples, and tips for better slides.
    
    September 29, 2026
    
    
    
    ](https://plusai.com/blog/how-to-convert-a-document-to-a-presentation)
-   [
    
    Resources
    
    ### 9 best AI agents in 2026: What each does best
    
    We compare the 9 best AI agents in 2026 for research, decks, data, and meetings—clear strengths, honest limits, and real pricing for each.
    
    September 15, 2026
    
    
    
    ](https://plusai.com/blog/best-ai-agents)