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Artificial Intelligence (AI) Glossary: 152 Terms You Should Know

Maksym Babych

Maksym Babych


19 min

Artificial intelligence (AI) terminology can be confusing, with words like ‘machine learning’ and ‘neural networks’ often thrown around.

To help you understand what they mean, we’ve compiled an AI glossary explaining key terms in simple language and covering popular techniques like computer vision, natural language processing, and robotics.

This glossary aims to make AI more accessible by providing a helpful reference for the technologies driving recent advances in artificial intelligence.


  • Artificial Intelligence (AI): The simulation of human intelligence processes by machines, especially computer systems, encompassing learning, reasoning, self-correction, and more.
  • Artificial General Intelligence (AGI): A level of AI development where a machine can perform any intellectual task that a human being can.
  • Artificial Neural Network (ANN): Computing systems vaguely inspired by the biological neural networks that constitute animal brains, capable of learning from observational data.
  • Adversarial Machine Learning: A technique employed in AI that attempts to fool models through malicious input, used to improve model robustness.
  • Augmented Intelligence: An alternative conceptualization of artificial intelligence that focuses on AI’s assistive role, emphasizing the fact that it is designed to enhance human intelligence rather than replace it.
  • AI Ethics: The branch of ethics examines the moral implications and challenges of artificial intelligence and machine learning.
  • Anomaly Detection: The identification of rare items, events, or observations that raise suspicions by differing significantly from most of the data.
  • Adaptive Learning: A system or software that adjusts the content and pace of learning based on the learner’s performance, often used in educational technology.


  • Backward chaining: A reasoning technique that starts with the goal and works backward to deduce the facts or conditions that can lead to the goal.
  • Bayesian Networks: Probabilistic graphical models that represent a set of variables and their conditional dependencies via a directed acyclic graph (DAG).
  • Big data: The collection, processing, and analysis of extremely large datasets that cannot be handled by traditional data-processing applications.
  • Binary Classification: A type of classification task where the output is restricted to two classes.
  • Brain-Computer Interface (BCI): A direct communication pathway between an enhanced or wired brain and an external device, often explored in AI for controlling computers or prosthetics.


  • Convolutional Neural Network (CNN): A class of deep neural networks, most commonly applied to analyzing visual imagery, characterized by their use of convolutional layers that automatically and adaptively learn spatial hierarchies of features from images.
  • Clustering: An unsupervised learning technique that involves grouping sets of similar data points based on their features to discover underlying patterns.
  • Classification: A supervised learning task that involves predicting the category or class of an input data point based on learned mappings from previous examples.
  • Chatbot: A software application used to conduct an online chat conversation via text or text-to-speech, in lieu of providing direct contact with a live human agent, designed to convincingly simulate how a human would behave as a conversational partner.
  • Computer Vision: A field of AI that trains computers to interpret and understand the visual world, enabling machines to identify objects, classify images, and react to what they “see” using digital images and deep learning models.
  • Capsule Networks (CapsNets): A type of artificial neural network that aims to improve the efficiency and accuracy of learning by understanding the spatial hierarchies between features in an image.
  • Curriculum Learning: A training strategy for machine learning models where the tasks are presented in a meaningful order that gradually increases in complexity and difficulty.
  • Cross-Validation: A statistical method used to estimate the skill of machine learning models by partitioning the original sample into a training set to train the model and a test set to evaluate it.
  • Cognitive Computing: A technology that mimics human thought processes in a computerized model, aiming to create automated IT systems capable of solving problems without human assistance.
  • Context-Aware Computing: A computing paradigm in which situational and environmental information about people, places, and things is used to anticipate immediate needs and proactively offer enriched situation-aware, and usable content, functions, and experiences.
  • Convolution: A mathematical operation used in CNNs to process data for features such as edges, shapes, and textures, crucial for tasks like image and video recognition.


  • Data science: A multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
  • Dataset: A collection of related sets of information that is composed of separate elements but can be manipulated as a unit by a computer.
  • Data Mining: The practice of examining large pre-existing databases to generate new information and find hidden patterns.
  • Deep Learning: A subset of machine learning involving neural networks with many layers, enabling computers to learn from experience and understand the world in terms of a hierarchy of concepts.
  • Decision Tree: A decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.
  • Deep Reinforcement Learning: Combining deep learning with reinforcement learning principles to create efficient algorithms that can learn from their actions and environments.
  • Dataset: A collection of data often used in machine learning and statistics for analyzing and modeling.
  • Distributed AI (DAI): A subfield of AI that deals with distributed systems where multiple agents interact to improve their individual performance or to solve problems too large for a single agent.
  • Data Preprocessing: The process of transforming raw data into an understandable format involves cleaning, normalization, transformation, feature extraction, and selection.
  • Deep Neural Network (DNN): An artificial neural network with multiple layers between the input and output layers, enabling the network to model complex nonlinear relationships.
  • Denoising: The process of removing noise from data, commonly used in the context of image processing or signal processing.
  • Dialogue Systems: Computer systems intended to converse with a human, with applications ranging from practical systems for information retrieval to theoretical models of human conversation.
  • Decision Theory: A branch of mathematics concerned with decision-making in the presence of uncertainty and with various aspects of mathematics of decision, used in AI for designing systems or algorithms that make decisions based on data.
  • Distributed Learning: A learning paradigm where the learning process is distributed across multiple nodes or devices, allowing for parallel processing and scalability.
  • Domain Adaptation: A technique in machine learning where a model trained on one domain is adapted to work on a different but related domain, addressing the issue of when the test data differs from the training data.


  • Expert System: An AI program that emulates the decision-making ability of a human expert by following a set of rules that analyze information and provide conclusions, recommendations, and reasoning.
  • Embedding: A representation of data where elements with a similar meaning have a similar representation in a vector space, commonly used in natural language processing to represent words or phrases.
  • Ethics in AI: The branch of ethics that examines the moral aspects and societal impacts of artificial intelligence, including issues of bias, privacy, autonomy, and the implications of AI decisions.
  • Entity Extraction: A process in natural language processing where specific categories of information or entities are identified and extracted from text, such as names of people, organizations, locations, dates, and quantities.


  • Feature Extraction: The process of reducing the amount of resources required to describe a large set of data accurately, by transforming the data into a set of features.
  • Fuzzy Logic: A form of many-valued logic that deals with reasoning that is approximate rather than fixed and exact, used in systems that must make decisions based on uncertain or incomplete information.
  • Feedforward Neural Network: A type of artificial neural network where connections between the nodes do not form a cycle, commonly used for static pattern recognition.
  • Fine-Tuning: The process of making small adjustments to a pre-trained model to adapt it to a specific task, improving performance by continuing the training process with a smaller learning rate on a new dataset.
  • Feature Engineering: The process of using domain knowledge to extract features (characteristics, properties, attributes) from raw data that make machine learning algorithms work.
  • False Positive: In machine learning and statistics, a false positive is an error in data reporting where a test result improperly indicates the presence of a condition (such as a disease when it is not present).
  • False Negative: An error in which a test result wrongly indicates no presence of a condition (the result is negative) when, in reality, it is present.
  • Facial Recognition: A form of computer vision that uses biometric markers to identify or verify a person’s identity using their face.
  • Feature Scaling: A method used to normalize the range of independent variables or features of data, often used in data preprocessing for machine learning.


  • Generative Adversarial Network (GAN): A class of machine learning frameworks where two neural networks compete in a game (the generative network generates candidates, and the discriminative network evaluates them), primarily used to generate realistic synthetic data.
  • Graph Neural Network (GNN): A type of neural network that directly operates on the graph structure, allowing it to take graph data as input and return graph data as output. It is used for tasks that involve graph structures like social networks or molecule structures.
  • Generalization: The ability of a machine learning model to perform well on new, unseen data that was not used during the training process.
  • Game Theory: The study of mathematical models of strategic interaction among rational decision-makers used in AI for developing algorithms for systems that need to make decisions in competitive environments.
  • Gated Recurrent Unit (GRU): A type of recurrent neural network that is capable of learning long-term dependencies, designed to solve the vanishing gradient problem of traditional RNNs.
  • Graph Theory: A field of mathematics used in computer science, including AI, for studying graphs, which are mathematical structures used to model pairwise relations between objects.
  • GAN Inversion: A process in which a given image or data point is mapped back into the latent space of a Generative Adversarial Network, allowing for the manipulation of the data point’s attributes by altering its latent representation.


  • Hyperparameter: A parameter whose value is set before the learning process begins, determining the network structure (for example, the number of hidden units in neural networks) and how the network is trained (like the learning rate).
  • Hierarchical Clustering: A method of cluster analysis that seeks to build a hierarchy of clusters, typically visualized as a dendrogram, used in various AI applications for grouping similar data points.
  • Hardware Acceleration: The use of computer hardware specifically designed to perform some functions more efficiently than is possible in software running on a general-purpose CPU.
  • Human-in-the-Loop (HITL): A model that requires human interaction. In AI, HITL is used to improve algorithms, such as by providing feedback on the accuracy of outcomes or by teaching AI systems how to perform tasks.


  • Intelligent Agent: An autonomous entity that observes through sensors and acts upon an environment using actuators and directs its activity toward achieving goals.
  • Image Recognition: The ability of AI to automatically identify objects, places, people, writing, and actions in images, often used in applications like photo tagging and autonomous vehicles.
  • Iterative Learning: A learning process where the model learns incrementally by repeating a series of steps and adjusting at each iteration to improve the outcome.
  • Instance-Based Learning: A family of learning algorithms that compare new problem instances with instances seen in training, which were stored in memory rather than performing explicit generalization.
  • Image Segmentation: The process of partitioning a digital image into multiple segments (sets of pixels), typically used to locate objects and boundaries more meaningfully for tasks like object detection and recognition.


  • Joint Probability Distribution: In statistics and machine learning, it’s the probability distribution over all possible combinations of values for two or more variables. It’s fundamental to understanding the relationships between variables in probabilistic models and AI.
  • Jacobian Matrix: In the context of AI, particularly in neural networks and optimization, the Jacobian matrix consists of first-order partial derivatives of a vector-valued function. It’s crucial to understand how changes in input variables affect the output of the function used in backpropagation and gradient calculations.


  • Knowledge Base: In AI, a knowledge base is a centralized repository for information: a public library, a database of related information about a particular subject.
  • Knowledge Representation: Involves the abstraction of the real world to make a computational model of some domain, which can be used by AI systems to solve complex problems.
  • Knowledge Engineering: The field of artificial intelligence that involves integrating knowledge into computer systems in a way that it can be reasoned with to make complex decisions.
  • K-Means Clustering: A vector quantization method, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean.
  • Kernel Methods: A class of algorithms for pattern analysis, the most common of which is the kernel SVM. They operate in a high-dimensional, implicit feature space without ever computing its data coordinates.
  • Knowledge Graph: A knowledge base that uses a graph-structured data model or topology to integrate data. Search engines often use knowledge graphs to enhance the search results.
  • K-Fold Cross-Validation: A resampling procedure used to evaluate machine learning models on a limited data sample. The dataset is divided into k groups, and the model is trained and tested k times, each time with a different group as the test set.
  • Knowledge Discovery: The process of discovering useful knowledge from a collection of data, which is often used interchangeably with the term “data mining.”
  • Kappa Statistics: A statistical measure of inter-rater reliability or agreement for categorical items. It’s useful in machine learning for evaluating the accuracy of classification models.
  • K-nearest Neighbors Algorithm (k-NN): An algorithm that stores all available cases and classifies new cases by a majority vote of its k neighbors. The case being assigned to the class is most common among its k nearest neighbors measured by a distance function.


  • Learning Rate: In machine learning, the learning rate is a hyperparameter that controls how much we adjust our network weights with respect to the loss gradient. It determines the size of the steps taken during the optimization process.
  • Logistic Regression: A statistical method for analyzing a dataset in which one or more independent variables determine an outcome. The outcome is measured with a dichotomous variable (in which there are only two possible outcomes).
  • Loss Function: A method of evaluating how well a specific algorithm models the given data. If predictions deviate too much from actual results, loss function would cough up a lot.
  • Latent Variable: In statistics and machine learning, a latent variable is a variable that is not directly observed but is inferred (through a mathematical model) from other variables that are observed and directly measured.
  • Layer (Neural Networks): A collection of neurons within a neural network. These layers are connected to each other and often play different roles. For example, input layers receive input, hidden layers process the input, and the output layer provides the final output.
  • Labeled Data: Data tagged with one or more labels identifying certain properties or categories, often used in supervised learning where the labels are the target outcomes for the predictive models.
  • Latent Dirichlet Allocation (LDA): A generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. It’s particularly used in natural language processing for topic modeling.
  • Linear Regression: A linear approach to modeling the relationship between a scalar response and one or more explanatory variables.


  • Machine Learning (ML): A subset of AI that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention, using algorithms that improve automatically through experience.
  • Model: In AI, a model represents what an AI system has learned from the training data. It is the output generated when you train your machine learning algorithm with data.
  • Multilayer Perceptron (MLP): A class of feedforward artificial neural network (ANN) that consists of at least three layers of nodes: an input layer, a hidden layer, and an output layer. Except for the input nodes, each node is a neuron that uses a nonlinear activation function.
  • Model Overfitting: A modeling error in machine learning that occurs when a function fits the training data too well, capturing noise or random fluctuations in the training data as if they were important features, leading to poor generalization to new data.
  • Model Underfitting: Refers to a model that can neither model the training data nor generalize to new data. An underfit model will be less flexible and cannot capture the underlying trend of the data.
  • Meta-Learning: Sometimes called “learning to learn,” it involves AI systems that can adapt to new tasks with minimal data by applying knowledge learned from previous tasks.
  • Model Generalization: The ability of an AI model to adapt properly to new, previously unseen data, drawn from the same distribution as the one used to create the model.
  • Machine Perception: The capability of a computer system to interpret data in a manner that is similar to the way humans use their senses to relate to the world around them. This can involve recognizing audio or visual patterns.
  • Model Validation: The process of evaluating a trained model’s performance with a testing data set to ensure it makes accurate predictions.


  • Natural Language Processing (NLP): A field of AI focused on the interaction between computers and humans through natural language. It enables computers to understand, interpret, and generate human language in a valuable way.
  • Neural Network: A computational model inspired by the structure of neural networks in the human brain, consisting of layers of interconnected nodes (neurons) that process information using dynamic state responses to external inputs.
  • Naive Bayes Classifier: A simple probabilistic classifier based on applying Bayes’ theorem with strong (naive) independence assumptions between the features. It is particularly suited for high-dimensional data.
  • Normalization: A process in data preprocessing that involves adjusting the values in a dataset to a common scale without distorting differences in the ranges of values, often used in machine learning to improve the convergence of training.
  • Nearest Neighbor Algorithm: An algorithm that classifies a data point based on how its neighbors are classified. It’s one of the simplest types of classification algorithms.
  • Noise: In the context of data and AI, noise refers to irrelevant or meaningless information that can obscure or distort the underlying signal in the data.
  • Normalization Layer: A layer within a neural network that standardizes the inputs to a layer for each mini-batch. This helps to stabilize and accelerate the training of deep networks.


  • Optimization: The process of adjusting the parameters of an AI model to minimize (or maximize) a certain objective function, such as a loss function, thereby improving the model’s performance.
  • Object Recognition: A computer vision technique for identifying objects in images or videos. It involves detecting an object’s instance in a digital image or video.
  • OpenAI: An AI research laboratory consisting of the for-profit corporation OpenAI LP and its parent company, the non-profit OpenAI Inc. OpenAI conducts research in the field of artificial intelligence with the stated goal of promoting and developing friendly AI in a way that benefits humanity as a whole.
  • Open Source AI: Refers to AI software and tools that are available with source code that anyone can inspect, modify, and enhance. Open-source AI initiatives promote collaborative development and wide accessibility of AI technologies.
  • Ordinal Regression: A type of regression analysis used in machine learning for predicting an ordinal variable, i.e., a variable that holds values with a known, specific order but unknown intervals between the values.
  • Object Detection: A computer vision technique that allows us to identify and locate objects in an image or video. Unlike object recognition, which only tells us what objects are present in an image, object detection also finds where the objects are located.


  • Principal Component Analysis (PCA): A statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.
  • Pattern Recognition: The automated recognition of patterns and regularities in data. Pattern recognition is closely related to artificial intelligence and machine learning, together with applications such as data mining and knowledge discovery in databases (KDD), and is often used to identify objects in images or characters in handwriting.
  • Predictive Modeling: The process of using a statistical model or machine learning algorithm to predict the likelihood of a future outcome based on historical data.
  • Polynomial Regression: A form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modeled as an nth-degree polynomial. Polynomial regression fits a nonlinear relationship between the value of x and the corresponding conditional mean of y.
  • Passive Learning: A learning model where the learning system receives a set of data points without specific outcomes or feedback, contrasting with active learning, where the system can query or interact with the information source.
  • Prototype Model: In machine learning, prototype models are used in instance-based learning algorithms, where the idea is to classify new instances based on their similarity to stored instances or prototypes.


  • Q-Learning: A model-free reinforcement learning algorithm to learn the quality of actions, telling an agent what action to take under what circumstances. It does not require a model of the environment and can handle problems with stochastic transitions and rewards without requiring adaptations.
  • Quantum Machine Learning: An emerging interdisciplinary research area at the intersection of quantum physics and machine learning. Quantum machine learning algorithms can potentially provide speedups over classical algorithms and offer novel approaches to learning from quantum data.


  • Recurrent Neural Network (RNN): A class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior for a time sequence. RNNs are used in models where the context from previous inputs is important to processing current input, such as in language modeling.
  • Regression Analysis: A set of statistical processes for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables when the focus is on the relationship between a dependent variable and one or more independent variables.
  • Rule-Based System: A set of “if-then” rules used for creating AI applications. It’s a simple kind of AI system that applies human-created rules to knowledge stored in a knowledge base to infer new knowledge or make decisions.
  • Recommender Systems: A subclass of information filtering systems that seek to predict the “rating” or “preference” a user would give to an item. They are used in a wide variety of applications, from filtering streaming content to product recommendations in e-commerce.
  • Representation Learning: A set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task.


  • Supervised Learning: A type of machine learning where the model is trained on a labeled dataset, which means that each training example is paired with the output label that the model should predict.
  • Stochastic Gradient Descent (SGD): An optimization algorithm often used in training machine learning models, particularly deep neural networks. It approximates the true gradient of the cost function by considering only a subset of the data at each iteration.
  • Semantic Analysis: In natural language processing, semantic analysis is the process of understanding the meaning and interpretation of words, phrases, and sentences in the context of the given language.
  • Sequence Modeling: A type of model in machine learning that takes sequences of items (like words in sentences or time-series data) as input and predicts the next item in the sequence or another related sequence.
  • Sentiment Analysis: A natural language processing technique used to determine whether data is positive, negative, or neutral, often applied to reviews and social media posts to understand consumer sentiment.
  • Self-Supervised Learning: A type of machine learning where the training data is automatically labeled, often by using part of the input data as the label, allowing the model to learn representations from the data itself without explicit manual labeling.
  • Semantic Segmentation: A computer vision task that involves labeling each pixel in an image with a class corresponding to what the pixel represents. It’s a way of partitioning an image into parts that have meaning.
  • Sequential Decision Making: The process of making a series of decisions over time, where the outcome of each decision influences future decisions. This is a key aspect of reinforcement learning and certain control systems.


  • Transfer Learning: A research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem.
  • Tree Search Algorithms: Algorithms used in AI for navigating decision trees, including depth-first, breadth-first, and best-first strategies, often used in game playing, optimization, and problem-solving tasks.
  • Time Series Analysis: A method of analyzing time series data to extract meaningful statistics and characteristics of the data, often used in forecasting, anomaly detection, and signal processing within AI applications.
  • Text Mining: The process of deriving high-quality information from text, which involves the discovery by computer of new, previously unknown information by automatically extracting information from different written resources.
  • Turing test: A test of a machine’s ability to exhibit intelligent behavior equivalent to or indistinguishable from a human’s.


  • Unsupervised Learning: A type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is cluster analysis.
  • Unstructured Data: Data that does not adhere to a predefined data model or is not organized in a predefined manner. Unstructured data is typically text-heavy but may contain data such as dates, numbers, and facts as well.
  • Unification: In logic and AI, unification is the process of identifying and merging two different pieces of information, terms, or expressions that are identical in structure and content, often used in automated reasoning and logic programming.


  • Validation Set: A set of data used to provide an unbiased evaluation of a model fit on the training dataset while tuning the model’s hyperparameters. The validation set is used to fine-tune the model’s parameters and provide an unbiased evaluation.
  • Vision Processing Unit (VPU): A type of microprocessor designed to accelerate machine vision tasks, such as image processing, edge detection, and object recognition, often used in autonomous vehicles, smartphones, and other devices requiring computer vision capabilities.
  • Voice Recognition: The ability of a machine or program to receive and interpret dictation or to understand and carry out spoken commands. Voice recognition technology is a key component of conversational AI systems.
  • Vocabulary: In natural language processing, the vocabulary is the set of unique words and phrases that appear in a text dataset. Managing the vocabulary size is crucial for many NLP models to balance between expressiveness and computational efficiency.


  • Word Embedding: A type of word representation that allows words to be represented as vectors in a continuous vector space. This helps capture semantic and syntactic similarities among words. Popular models for word embeddings include Word2Vec and GloVe.
  • Workflow Automation: The use of technology to automate complex and repeatable tasks within a workflow or process. In AI, this often involves using machine learning models to automate decision-making steps within a larger system.
  • White Box Model: In machine learning, a white box model is an algorithm that is understandable and interpretable by humans. Decision trees and linear regression are examples of white box models where the decision process can be traced and understood.
  • Wrapper Method: In feature selection, wrapper methods use a predictive model to evaluate the combination of features and determine which features contribute most to the prediction accuracy. 


  • YOLO (You Only Look Once): A real-time object detection system that applies a single neural network to the full image, dividing the image into regions and predicting bounding boxes and probabilities for each region. YOLO is known for its speed and accuracy in detecting objects in images and videos.


  • Zero-Shot Learning: A classification problem setup where during training, the model sees classes A, B, C, etc., but during testing, it is asked to classify data from classes X, Y, Z, etc., which it has never seen before. The goal is for the model to use its knowledge of the seen classes to infer the properties and characteristics of unseen classes.
  • Z-Score Normalization (Standardization): A method of normalizing data where the mean of the data is 0 and the standard deviation is 1. It’s a common preprocessing step for many machine learning algorithms to ensure that the scale of the inputs does not unduly influence the model.

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