The Complete AI Glossary: 200+ AI Terms Beginners Must Know In 2024

The complete AI dictionary for novices. Contains 200+ definitions of key AI terminology concisely explained for easy understanding.

Artificial intelligence (AI) is filled with specialized terminology and jargon that can be overwhelming for beginners.

As you start exploring the world of AI, you’ll inevitably encounter novel concepts and unfamiliar words used to describe them. However, grasping the core vocabulary is critical to unlocking a deeper understanding of AI.

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That’s why we created this AI glossary, covering common AI terms from A to Z. Understanding these foundational terms will equip you to better comprehend AI literature, research, and discussions.

With clear explanations of key concepts, this glossary aims to make the specialized vocabulary of AI more accessible. Whether you’re a student, developer, or simply AI-curious, this reference will help you navigate the complex lexicon of artificial intelligence. Let’s embark on this enlightening journey together.

Latest Updated: Apr 14, 2024


  • Activation Function: Math functions like sigmoid, ReLU applied to neurons to introduce non-linearity into neural networks.
  • Adversarial Attack: Carefully constructed inputs meant to fool AI systems by exploiting vulnerabilities.
  • Adversarial Robustness: The ability of AI systems to withstand adversarial attacks.
  • Agent: An autonomous entity that can perceive and act upon an environment.
  • AGI (Artificial General Intelligence): A hypothetical type of artificial intelligence that would have the ability to understand and reason about the world in a way that is indistinguishable from a human.
  • AI Ethics: Examining the ethical impact of AI systems and developing them responsibly.
  • AI Alignment: A subfield of AI safety that aims to ensure that artificial intelligence systems achieve desired outcomes.
  • AIGC (AI Generated Content): Any form of content that is created by artificial intelligence rather than human beings.
  • Algorithm: A set of rules or steps that a computer follows to complete a task.
  • Alexa: Amazon’s virtual assistant technology powered by NLP.
  • AlphaGo: Game-playing AI system by DeepMind that defeated world champion in Go.
  • ANI (Artificial Narrow Intelligence): AI systems that are designed and trained to perform a specific task without possessing the general problem-solving abilities that a human has. Unlike Artificial General Intelligence (AGI), which would have the capability to understand, learn, and apply knowledge in different domains, ANI focuses on a narrow, well-defined task.
  • Artificial intelligence: The field of computer science that deals with the creation of intelligent agents. AI research has been highly successful in developing effective techniques for solving a wide range of problems.
  • Artificial Neural Network (ANN): A computing system inspired by the human brain’s neural networks, used for processing complex patterns of information.
  • ASI (Artificial Superintelligence): A theoretical form of AI that surpasses human intelligence across all domains, including creativity, general wisdom, problem-solving, and emotional intelligence.
  • Attention Layers: Part of a neural network that helps the model focus on relevant features of the input data by weighting the significance of different parts of the data.
  • Attention Mechanism: A component of neural networks that allows them to focus on specific parts of the input.
  • Autoencoder: A type of neural network used for unsupervised learning and dimensionality reduction.
  • Autonomous: Operating without human intervention.
  • Azure Machine Learning: A cloud-based service by Microsoft for building, training, and deploying machine learning models.


  • Backpropagation: Algorithm used to calculate loss and adjust weights in neural networks.
  • Batch Normalization: Normalizing activations throughout a neural network to stabilize training.
  • Bayesian Networks: Probabilistic model representing variables and conditional dependencies via graph.
  • BERT (Bidirectional Encoder Representations from Transformers): A method from Google for pre-training language representations.
  • Bias: Systematic error introduced by flawed assumptions in data or algorithms.
  • Big Data: Extremely large data sets analyzed computationally to reveal patterns.
  • BigGAN: A large generative adversarial network known for producing high-resolution images.
  • Bing Chat: A conversational AI chatbot feature for Bing’s search engine. It allows users to interact with an AI chatbot instead of typing search queries.


  • Caffe: An open-source deep learning framework.
  • Capsule Networks: An alternative to CNNs using capsules to represent parts of objects.
  • Chatbot: A software application designed to simulate human conversation.
  • ChatGPT: A conversational AI model by OpenAI.
  • Classification: Categorizing input data among a set of target classes or categories.
  • Claude: A next-generation AI assistant.
  • Cloud AutoML: Google Cloud’s suite of machine learning products that enables developers with limited expertise to train high-quality models.
  • Clustering: Groupings inputs based on similarity, used in unsupervised learning.
  • CNN (Convolutional Neural Network): A type of deep learning neural network that is particularly well-suited for image recognition and classification tasks.
  • Cognitive Computing: Enabling systems to learn, reason, and engage with humans naturally.
  • Computer Vision: Algorithms for processing, analyzing and understanding visual data.
  • Connectionism: A framework for understanding intelligence as the emergent property of interconnected networks of simple processing units.
  • Convolutional Neural Network (CNN): Neural nets using convolutional layers for tasks like image classification.
  • Copilot: GitHub’s AI pair programmer to suggest code in real time.
  • CoT (Chain-of-thought): A technique for guiding large language models (LLMs) to generate reasoning steps when answering a question or completing a task.
  • Cross-modal Generalization: The ability to apply knowledge learned in one sensory modality to another modality.
  • CV (Computer Vision): Extract useful information from images and videos, such as identifying objects and people, tracking their movement, and understanding their interactions with the environment.


  • Data Augmentation: Artificially increase the size of a training dataset by creating modified versions of existing data.
  • Data Leakage: When information leaks from test data to training data, causing overfitting.
  • Data Mining: Extracting insights from large data sets to uncover patterns.
  • DALL·E: An OpenAI model that generates images from textual descriptions.
  • DeepBlue: IBM’s chess-playing computer that beat world champion in 1997.
  • Deep Learning: Neural networks with many layers that can extract high-level features from raw data.
  • DeepMind: A British AI company, subsidiary of Alphabet Inc., known for breakthroughs in deep learning and AI for games.
  • Decision Tree: A flowchart-like structure in which each internal node represents a test on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label.
  • Diffusion Models: A type of generative model that can be used to generate realistic images, text, and other types of data.
  • Dimensionality Reduction: Reducing number of variables considered, simplifying data.
  • Distributed Computing: Splitting computations across multiple processors to parallelize workload.
  • Double Descent: A phenomenon in machine learning where increasing the number of model parameters first leads to a decrease in performance, and then to an increase in performance.


  • Echo State Networks: A type of recurrent neural network with a sparsely connected hidden layer, primarily used for time-series prediction where traditional training methods are impractical.
  • Embedding: A technique for representing high-dimensional data as vectors in a lower-dimensional space.
  • End-to-end learning: A machine learning approach where a single model is trained to perform a task from input to output without any intermediate steps.
  • Ensemble Models: Combining multiple models to produce predictions that are more robust.
  • Epoch: One cycle through the full training dataset in machine learning.
  • Evolutionary Algorithm: Algorithms that use mechanisms inspired by biological evolution, such as reproduction, mutation, and selection.
  • Expert system: A computer system that emulates the decision-making abilities of a human expert. Expert systems are used in a variety of applications, such as medical diagnosis, financial planning, and legal reasoning.
  • Explainable AI (XAI): Making opaque AI systems interpretable by humans.


  • Facebook AI Research (FAIR): Facebook’s AI research division.
  • Facial Recognition: Identifying or verifying individuals by analyzing facial characteristics.
  • False Positive: Incorrect classification of an input as positive for the condition being tested.
  • Feature Engineering: Transforming raw data into features that better represent patterns.
  • Federated Learning: Training algorithm distributed across decentralized edge devices.
  • Few-shot Learning: A machine learning approach that enables models to learn new tasks from a very small number of examples.
  • Fine-tuning: Taking a pre-trained model and retraining it on a new dataset.
  • Fitting: The process of training a machine learning model on a dataset.
  • Forward Propagation: Calculating the output of a neural network
  • Foundation Model: A large language model (LLM) that has been trained on a massive dataset of text and code.
  • Fuzzy Logic: A computing approach based on “degrees of truth” rather than the usual true or false binary logic.


  • GAN (Generative Adversarial Network): Neural networks that are trained to produce new data that mimics some existing data.
  • Generalization Ability: The ability of a model to perform well on new data that it has never seen before.
  • Generative AI: Artificial intelligence capable of generating text, images, or other media, using generative models. 
  • Genetic Algorithm: A search heuristic inspired by the process of natural selection.
  • Google Brain: Google’s deep learning artificial intelligence research team.
  • Google Bard: A large language model (LLM) chatbot developed by Google AI.
  • GPT (Generative Pre-trained Transformer): A series of language representation models by OpenAI.
  • Gradient Boosting: Successively training weak learners to improve predictive performance.
  • Gradient Descent: An optimization algorithm commonly used to train machine learning models. It works by iteratively adjusting the parameters of the model in the direction of the negative gradient of the loss function.
  • Graphical Model: A type of probabilistic model that uses a graph to represent and map out dependencies.


  • Hallucinate: Describing when an AI model generates incorrect information, but presents it as if it were a fact.
  • Hardware: Physical computer machinery needed to run machine learning algorithms.
  • Heuristic: An approach to problem solving relying on practical methods and experience.
  • Hidden Layer: A layer of neurons that is located between the input layer and the output layer.
  • Hugging Face: A company known for its Transformers library, which provides state-of-the-art general-purpose architectures for natural language understanding and generation.
  • Hypernetworks: A type of neural network architecture where one neural network generates the weights for another network, enabling dynamic adjustments of network architecture and functionality.
  • Hyperparameter Tuning: Optimizing hyperparameters to improve model performance on data.
  • Hypothesis: An initial assumption made about the data.


  • IBM Watson: A suite of AI tools and applications by IBM.
  • ImageNet: A large-scale dataset used for visual object recognition software research.
  • Image Recognition: Identifying objects or features within images or videos.
  • Incremental Learning: A method where the learning model is capable of learning continuously, adapting to new data without forgetting its previously learned knowledge.
  • Inference: Applying a trained machine learning model to make predictions on new data.
  • Information Retrieval: The process of obtaining information from a database using a query.
  • Instance: A single data point or example in a dataset.
  • Instruction Tuning: A technique for fine-tuning large language models (LLMs) to follow instructions more accurately and comprehensively.
  • Interpretability: Ability to explain why AI systems behave in certain ways.


  • Julia: A high-level, high-performance programming language for technical computing, with syntax familiar to users of other technical computing environments.
  • Jupyter Notebook: An open-source web application for interactive computing and data visualization.
  • Joint Probability: The probability of two events happening at the same time.
  • JSON (JavaScript Object Notation): A lightweight data-interchange format that’s easy for humans to read and write.


  • K-means Clustering: Unsupervised algorithm grouping data points into k clusters based on similarity.
  • Kernel: A function used in the machine learning algorithm to map a lower-dimensional data into a higher-dimensional data.
  • KNN (K-Nearest Neighbors): A simple, instance-based learning algorithm.
  • Knowledge Distillation: Transferring the knowledge from a large, complex teacher model to a smaller, simpler student model.
  • Knowledge Graph: Database of real-world entities and relationships between them.
  • Knowledge Representation: The field of AI dedicated to representing information about the world in a form that a computer system can utilize to solve complex tasks such as diagnosing a medical condition or having a dialogue in natural language.


  • LaMDA: Google’s conversational AI system meant to be indistinguishable from humans.
  • Latent Variable: A variable that’s not directly observed but inferred from other variables.
  • Lazy Learning: Algorithms that defer processing until needed, storing training data instead.
  • Learning Rate: Determines the step size at each iteration while moving towards a minimum of the loss function.
  • Lifelong Learning: The ability of an AI system to continuously learn and incorporate new knowledge over time, without forgetting or catastrophically interfering with previously learned skills and information.
  • Llama: An open-source large language model (LLM) from Meta.
  • LLM (Large Language Model): A type of machine learning model that is trained on a massive dataset of text and code. This allows the model to learn the statistical relationships between words and phrases, and to generate text that is both grammatically correct and semantically meaningful.
  • Logistic Regression: Predictive modeling method suited for binary classification tasks.
  • Loss Function: A mathematical function that quantifies the difference between the predicted and actual values in a machine learning model.
  • LSTM (Long short-term memory: A type of recurrent neural network (RNN) that is specifically designed to handle sequential data


  • Machine Learning: Algorithms that can learn from data to make predictions or decisions.
  • Machine Translation: Automated translation of text or speech from one language to another.
  • Magenta: Google AI project to generate art and music using ML.
  • Maximum entropy: A principle of machine learning that states that the best model is the one that makes the fewest assumptions about the data. Maximum entropy models are often used in natural language processing and computer vision.
  • Markov Chain: A sequence of events in which the probability of each event depends only on the state attained in the previous event.
  • Matrix Factorization: Decomposing a matrix into factors to discover latent features.
  • Meta-learning: The idea that AI systems can learn how to learn.
  • Midjourney: A generative artificial intelligence program and service created and hosted by San Francisco-based independent research lab Midjourney, Inc. 
  • Mixture of Experts: Combining the predictions of multiple expert models, each of which is specialized in a specific portion of the data.
  • Model: A representation of a system to help understand and predict the system’s behavior.
  • Multimodal: The use of multiple modalities of data, such as text, images, audio, and video.


  • Natural Language Processing (NLP): Automating the analysis and generation of natural human language.
  • NeRF: Creating photorealistic 3D models of objects and scenes from 2D images.
  • Neural Architecture Search: Automating the design of neural network architectures.
  • Neural Networks: Algorithms modeled loosely on the human brain’s neurons.
  • Neuroevolution: A form of artificial intelligence that uses evolutionary algorithms to generate artificial neural networks, parameters, topology, and rules.


  • Occam’s Razor: The problem solving principle that the simplest solution tends to be the best.
  • Optimization: Iteratively tuning parameters to minimize error and improve model performance.
  • Outlier: A data point that’s significantly different from other data points.
  • Overfitting: When a model fits the training data too closely but generalizes poorly.
  • Ontology: A set of concepts and categories in a subject area that shows their properties and the relations between them.


  • Perception: The process of acquiring information about the environment. Perception is a key component of many AI applications, such as robotics, computer vision, and speech recognition.
  • Perceptron: Early and simple neural network model for binary classification tasks.
  • Planning: The process of generating a sequence of actions to achieve a goal. Planning algorithms are used in a variety of AI applications, such as robotics, scheduling, and decision making.
  • Precision: Fraction of results returned by a model that are relevant.
  • Predictive Modeling: Using statistics to predict outcomes.
  • Principal Component Analysis (PCA): A method used to emphasize variation and bring out strong patterns in a dataset.
  • Probabilistic Modeling: Representing uncertainty and randomness to model real world data.
  • Prompt: In AI, a prompt is a piece of text that provides instructions or guidance to an AI model. It can be used to tell the model what task to perform, what kind of output to generate, or what style or tone to use.
  • Prompt Engineering: The process of designing and refining prompts—questions or instructions—to elicit specific responses from AI models.
  • PyTorch: An open-source machine learning framework developed by Facebook.


  • Q-Learning: Reinforcement learning technique using a reward system.
  • Quantum Computing: Computing using quantum bits or qubits.
  • Query: A request for data or information from a database.


  • Random Forest: Ensemble method combining predictions from many decision trees.
  • Recall: Fraction of total relevant results correctly classified by algorithm.
  • Recommender Systems: Predicting user preferences for products or content.
  • Recurrent Neural Network (RNN): Neural networks with loops, allowing information to persist.
  • Regression: Statistical models estimating relationships between variables.
  • Reinforcement Learning: Agents maximizing rewards through trial-and-error interactions.
  • Recurrent Neural Networks (RNN): A class of neural networks where connections between nodes form a directed graph along a temporal sequence, allowing it to exhibit temporal dynamic behavior.
  • Robotics: The field of engineering that deals with the design, construction, operation, and application of robots. Robots are machines that can be programmed to perform tasks. AI is used to control robots and give them autonomy.
  • Rule-based system: A computer system that uses a set of rules to make decisions. Rule-based systems are often used in expert systems and decision support systems.


  • Self-attention: A mechanism within neural networks that allows them to weigh the importance of different inputs independently, useful particularly in transformer models for tasks like language understanding and translation.
  • Self-Supervised Learning: Using unlabeled data to pretrain models.
  • Semantic Segmentation: A process in computer vision that involves dividing an image into parts and classifying each part at the pixel level, helping the model understand and label whole scenes in detail.
  • Semi-supervised Learning: Using both labeled and unlabeled data for training.
  • Sentiment Analysis: Determining emotional tone behind text data.
  • Sequence Model: Algorithms like RNNs and LSTMs for ordered data like text or time series.
  • Siamese Networks: Neural nets that compare two inputs.
  • Singular Value Decomposition (SVD): Matrix factorization method for reducing dimensionality.
  • Spatial Transformer Networks: A neural network module that explicitly allows the spatial manipulation of data within the network, enabling it to actively spatially transform feature maps according to the learned task.
  • Spike Neural Networks: Networks that incorporate the timing of neuron spike events, making them efficient for processing temporal data.
  • Supervised Learning: Creating models from labeled training data.
  • Support vector machine (SVM): A type of machine learning model that is used for classification and regression. SVMs work by finding a hyperplane that separates the data points into different classes.
  • Synthetic Data: Artificially generated training data.


  • Tensor: A mathematical object analogous to but more general than a vector.
  • TensorFlow: End-to-end open source platform for machine learning.
  • Transfer Learning: Applying knowledge gained in one domain to a related domain.
  • Transformer: Attention-based neural network architecture.
  • Tree-Based Models: Algorithms like random forests and gradient boosting using decision trees.
  • Triplet Loss: A loss function used in machine learning to learn embeddings or transformations by comparing a base input to a positive input (similar) and a negative input (differing) to ensure that similar items are closer in the embedding space than differing items.


  • Unsupervised Learning: Finding patterns in unlabeled, unclassified data.
  • Underfitting: When a machine learning model is too simple and doesn’t capture the underlying trend of the data.
  • Utility Theory: A decision-making approach based on the assumption that decisions are made to maximize pleasure and minimize pain.


  • Validation Set: Data used to tune hyperparameters and evaluate models while training.
  • Variable selection: The process of selecting a subset of features from a larger set of features. Variable selection is used to improve the performance of machine learning models by reducing the number of features that the model needs to learn.
  • Vector: A quantity having direction as well as magnitude.
  • Vision Recognition: The ability of software to identify objects, places, people, writing, and actions in images.


  • Weight Initialization: Strategies for initializing weights in neural networks prior to training.
  • Weights: Values in a neural network that are adjusted during training.
  • Word Embedding: The representation of words in dense vectors of real numbers.
  • Wrapper Method: A method for feature selection in a dataset.


  • XAI (Explainable AI): Making AI decisions understandable to humans.
  • XGBoost: Scalable, optimized implementation of gradient boosting algorithm.


  • YOLO (you only look once): A type of object detection algorithm that uses a single neural network to detect objects in an image. YOLO algorithms are fast and accurate, making them well-suited for real-time applications such as self-driving cars and robotics.


  • Zero-shot learning: A type of machine learning that involves training a model to recognize new classes that it has never seen before. Zero-shot learning is a challenging problem, but it has the potential to enable AI systems to learn new information quickly and easily.

Final Thoughts

And there you have it – an A to Z glossary covering key terminology and concepts in the world of artificial intelligence. From machine learning fundamentals to cutting-edge models like ChatGPT and Llama, this reference aimed to elucidate the complex lexicon and acronyms used in AI.

While we strived to make this guide comprehensive, AI is a rapidly evolving field with new advances emerging constantly. Did we leave out any important AI terms you feel warrant explanation? What AI concepts remain fuzzy or confusing even after reading this glossary? Let’s keep the conversation going in the comments below!

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