The Definitive AI Glossary of Terms
Welcome to the "Definitive AI Glossary of Terms." This comprehensive guide is designed to help you understand the key terms used in the field of Artificial Intelligence (AI).
Matthew Clarkson

Matthew Clarkson

August 4, 2023

Welcome to the Definitive AI Glossary of Terms. This comprehensive guide is designed to help you understand the key terms used in the field of Artificial Intelligence (AI).

We cover fundamental AI concepts, delve into the specifics of AI algorithms, models, and deep learning, discuss the ethical and societal implications, explore AI tools, libraries, and frameworks, and more.

Whether you’re an AI enthusiast, a student, a researcher, or a professional in the field, we hope this glossary will be a valuable resource for you.

Artificial Intelligence (AI) Glossary of Terms

The field of computer science that is dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem-solving, perception, language understanding, etc.

Artificial General Intelligence (AGI)

Also known as “Strong AI,” AGI refers to a type of artificial intelligence that is capable of understanding, learning, and applying its intelligence to any intellectual task that a human being can do.

Narrow AI

Also known as “Weak AI,” it refers to AI systems that are designed to perform a narrow task (e.g., only facial recognition or only internet searches) and cannot exhibit human-like intelligence beyond their specific programmed task.

Superintelligent AI

A hypothetical AI that possesses intelligence far surpassing that of the brightest and most gifted human minds. Superintelligent AI would not merely be good at intellectual tasks; it would be supremely good at all tasks that are computationally feasible.

Turing Test

Proposed by Alan Turing in 1950, the Turing Test is a measure of a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.

Chinese Room Argument

A philosophical argument proposed by John Searle that challenges the notion of strong AI, suggesting that a program cannot give a computer a ‘mind’ or ‘understanding,’ regardless of how intelligently it may make it behave.

Machine Learning (ML)

A subset of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

Deep Learning (DL)

A subfield of machine learning that uses artificial neural networks with multiple abstraction layers to model and understand complex patterns in datasets.

Neural Network

Inspired by the human brain, a neural network is a computing system consisting of interconnected nodes, or “neurons,” that use algorithms to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.


A hypothetical future point in time when artificial intelligence will surpass human intelligence and be able to self-improve and replicate, leading to rapid technological growth, radically changing civilization.

Data Mining

The process of discovering patterns, correlations, trends, and useful information from large amounts of data using AI, Machine Learning, and Statistics.

Intelligence Explosion

This refers to a hypothetical scenario in which an artificial intelligence (AI) enters a ‘runaway reaction’ of self-improvement cycles, with each new generation of AI appearing faster, leading to a superintelligence that qualitatively far surpasses all human intelligence.

Friendly AI

A hypothetical strong AI that is programmed to have a positive impact on humanity and does not harm human beings even if it evolves into a superintelligent AI.

Algorithmic Bias

This occurs when an algorithm produces results that are systematically prejudiced due to erroneous assumptions in the machine learning process.

Explainable AI (XAI)

An area in AI that focuses on creating transparent AI systems able to clearly explain their actions and decision-making processes to the average user.

AI Research and Theory Glossary of Terms

Terms related to research methodologies, theoretical models, and concepts. Some examples are Supervised Learning, Unsupervised Learning, Reinforcement Learning, and Transfer Learning.

Supervised Learning

A type of Machine Learning where the model learns from labeled data. In supervised learning, each example in the training dataset is paired with its correct answer (i.e., the output value).

Unsupervised Learning

A type of Machine Learning where the model learns from unlabeled data. The aim is to model the underlying structure or distribution in the data in order to learn more about it.

Semi-Supervised Learning

This is a type of machine learning that uses a combination of a small amount of labeled data and a large amount of unlabeled data for training. It is used when the cost of labeling is too high to allow for a fully labeled training process.

Reinforcement Learning

A type of Machine Learning where an agent learns to make decisions by performing certain actions in an environment to achieve a goal. The agent learns from the consequences of its actions, rather than from being explicitly taught and it selects its actions based on its past experiences (exploration) and also by new choices (exploitation).

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. For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks.

Active Learning

A special case of semi-supervised machine learning in which a learning algorithm can interactively query the user (or some other information source) to obtain the desired outputs at new data points.

Ensemble Learning

A machine learning concept in which multiple models (such as classifiers or experts) are strategically generated and combined to solve a particular computational intelligence problem.


A statistical method of evaluating generalization performance that is more reliable than using the conventional training and test set split. It is typically used in machine learning to estimate the skill of a machine learning model on unseen data.

Feature Engineering

The process of using domain knowledge of the data to create features (feature vectors) that make machine learning algorithms work. Feature engineering is fundamental to the application of machine learning and is both difficult and expensive.


A technique used to prevent overfitting in a machine learning model by adding an additional penalty to the loss function, thereby decreasing the complexity of the model.

Overfitting and Underfitting

Overfitting refers to a model that models the training data too well. It has learned the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. Underfitting refers to a model that can neither model the training data nor generalize to new data.

Hyperparameter Tuning

The process of choosing a set of optimal hyperparameters for a machine learning model. A hyperparameter is a parameter whose value is set before the learning process begins.

Bias-Variance Tradeoff

In machine learning, the bias-variance tradeoff is the property of a set of predictive models whereby models with a lower bias in parameter estimation have a higher variance of the parameter estimates across samples, and vice versa.

Grid Search and Random Search

These are techniques for hyperparameter tuning. Grid search involves defining a grid of hyperparameters and systematically working through multiple combinations. Random search, on the other hand, selects random combinations of the hyperparameters to find the best solution.

Model Evaluation Metrics

These are a variety of metrics used to measure the quality of predictions of a machine learning model, such as accuracy, precision, recall, F1 score, ROC AUC score, mean squared error, log loss, etc.

AI Algorithms and Models Glossary of Terms

Specific algorithms and model types used in AI. For instance, Neural Networks, Decision Trees, Support Vector Machines, and Genetic Algorithms.

Artificial Neural Networks (ANNs)

Computation models inspired by the human brain’s neural networks. These models are designed to recognize patterns and interpret sensory data.

Convolutional Neural Networks (CNNs)

A special type of neural network designed for processing structured grid data, such as images, by applying a series of filters to extract features.

Recurrent Neural Networks (RNNs)

Neural networks where connections between neurons form a directed graph along a temporal sequence. They are excellent for sequential data and have been widely used for tasks such as speech recognition and natural language processing.

Long Short-Term Memory (LSTM)

A type of recurrent neural network that has feedback connections. It is capable of learning long-term dependencies, which makes it ideal for applications in handwriting and speech recognition.

Generative Adversarial Networks (GANs)

A class of machine learning systems designed by Ian Goodfellow and his colleagues in 2014. Two neural networks contest with each other in a game, making it useful for generating synthetic data.


A type of neural network used for learning efficient codings of input data. They are typically used for dimensionality reduction or feature learning.

Radial Basis Function Networks (RBFNs)

A type of artificial neural network that uses radial basis functions as activation functions. They are used in function approximation, time series prediction, and control.

Deep Belief Networks (DBNs)

A class of deep neural network which comprises multiple layers of graphical model having both directed and undirected edges. They are used for pattern analysis and recognition tasks.

Decision Trees

A type of model used for classification and regression. It models decisions based on the outcomes of certain actions, effectively creating a flowchart that predicts the outcome of a decision.

Random Forests

An ensemble learning method that operates by constructing multiple decision trees during training and outputting the majority class (classification) or mean prediction (regression) of the individual trees.

Support Vector Machines (SVMs)

A set of supervised learning methods used for classification, regression, and outliers detection. They are effective when the number of dimensions is greater than the number of samples.

Naive Bayes Classifier

A family of simple probabilistic classifiers based on applying Bayes’ theorem with strong (naive) independence assumptions between the features.

K-nearest neighbors (K-NN)

A type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until function evaluation.

Genetic Algorithms

A search heuristic that is inspired by Charles Darwin’s theory of natural evolution. This algorithm reflects the process of natural selection where the fittest individuals are selected for reproduction to produce offspring of the next generation.

Reinforcement Learning Algorithms

Algorithms used in reinforcement learning, a type of machine learning where an agent learns to behave in an environment, by performing certain actions and observing the results/rewards of those actions. Examples of reinforcement learning algorithms include Q-Learning, SARSA (State-Action-Reward-State-Action), and Deep Q-Learning.

Linear Regression and Logistic Regression

Linear regression is used to predict a continuous outcome variable (y) based on one or more predictor variables (x). Logistic regression is used when the dependent variable is binary in nature.

K-means Clustering

An algorithm to classify or to group objects based on attributes/features into K number of groups. K is positive integer number

Hierarchical Clustering

Hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of cluster analysis which seeks to

Deep Learning Glossary of Terms

A subcategory of AI Algorithms and Models, focusing specifically on deep learning models and concepts, like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Generative Adversarial Networks (GANs).

Deep Learning (DL)

A subfield of machine learning that uses algorithms to model high level abstractions in data through the use of multiple layers of artificial neural networks.

Artificial Neural Networks (ANNs)

Computational models inspired by the human brain’s neural networks. These models are used to recognize patterns and interpret sensory data.

Deep Neural Networks (DNNs)

These are artificial neural networks with multiple layers between the input and output layers. Each mathematical manipulation as data moves from the input to the output layer can be considered a layer.

Convolutional Neural Networks (CNNs)

A class of deep learning neural networks, most commonly applied to analyzing visual imagery. They have their “neurons” arranged more like those of the frontal lobe, the area responsible for processing visual stimuli in humans and other animals.

Recurrent Neural Networks (RNNs)

A type of artificial neural network designed to recognize patterns in sequences of data, such as text, genomes, handwriting, or the spoken word

Long Short-Term Memory (LSTM)

A special kind of RNN, capable of learning long-term dependencies, which makes it ideal for applications in handwriting and speech recognition.

Gated Recurrent Unit (GRU)

A type of recurrent neural network that is similar to LSTM but has a simpler structure. It combines the forget and input gates into a single “update gate”.

Generative Adversarial Networks (GANs)

A class of AI algorithms used in unsupervised machine learning, implemented by a system of two neural networks contesting with each other in a zero-sum game framework.

Autoencoders (AE)

A type of artificial neural network used for learning efficient codings of input data. They are typically used for the purpose of dimensionality reduction or denoising.

Restricted Boltzmann Machine (RBM)

A generative stochastic artificial neural network that can learn a probability distribution over its set of inputs.

Self-Organizing Maps (SOMs)

A type of artificial neural network that is trained using unsupervised learning to produce a low-dimensional representation of the input space of the training samples, called a map.


A regularization technique for reducing overfitting in neural networks by preventing complex co-adaptations on training data. It is an efficient way of performing model averaging with neural networks.

Batch Normalization

A method used to make artificial neural networks faster and more stable through normalization of the layers’ inputs by re-centering and re-scaling.

Transfer Learning

The practice of using a neural network model trained on one problem as a starting point for a model on a second problem. It is a popular approach in deep learning where pre-trained models are used as the starting point on computer vision and natural language processing tasks.


The simplest form of a neural network, a perceptron is an algorithm for supervised learning of binary classifiers. It makes its predictions based on a linear predictor function combining a set of weights with the feature vector.


A method used to train neural networks, it is a way to update the weights in the network by propagating the error back into the network.

Activation Function

In artificial neural networks, the activation function defines the output of a node given an input or set of inputs.

Natural Language Processing (NLP) Glossary of Terms

All terms related to the intersection of AI and language, including concepts like Bag of Words, Tokenization, Sentiment Analysis, and Chatbots.

Natural Language Processing (NLP)

A field of AI that gives the machines the ability to read, understand, and derive meaning from human languages.


The process of segmenting running text into words and sentences. It’s the first step in NLP.

Bag of Words (BoW)

A method used in NLP to represent how often certain words appear in a document. It involves representing text data in terms of a ‘bag’ (multiset) of its words, disregarding grammar and word order but keeping multiplicity.

Stop Words

The most common words in a language like ‘is’, ‘in’, ‘the’, etc., which are often removed in the preprocessing stage.


The process of reducing inflection in words to their root forms, such as mapping a group of words to the same stem even if the stem itself is not a valid word in the Language.


Similar to stemming, it involves reducing a word to its base form. Unlike stemming, it reduces the word in the context of the sentence, considering the Part of Speech (PoS) and thus giving a valid word that exists in the language, unlike stemming.

Part of Speech (PoS) Tagging

The process of marking a word in a text as corresponding to a particular part of speech, based on its definition and its context.

Named Entity Recognition (NER)

A subtask of information extraction that seeks to locate and classify named entities in text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.

Sentiment Analysis

The use of NLP to systematically identify, extract, quantify, and study affective states and subjective information.


An AI software designed to interact with humans in their natural languages. These bots are typically used on the internet for various tasks, from information acquisition to customer service.

Word Embeddings/Word Vectorization

The representation of words for text analysis, typically in the form of a real-valued vector that encodes the meaning of the word such that the words that are closer in the vector space are expected to be similar in meaning.

Latent Semantic Analysis (LSA)

A technique in NLP that allows you to analyze relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms.

Term Frequency-Inverse Document Frequency (TF-IDF)

A numerical statistic that reflects how important a word is to a document in a collection or corpus.

Language Model

A probabilistic model that predicts the next word or character in a sentence. It is widely used in speech recognition, handwriting recognition, spelling correction, and machine translation.

Sequence to Sequence (Seq2Seq) Models

Models that convert sequences from one domain (e.g. sentences in English) to sequences in another domain (e.g. the same sentences translated to French).

Attention Mechanism

A mechanism in a neural network (often an RNN or LSTM) that allows the network to focus on certain parts of the input when producing the output, rather than treating all parts of the input equally.

Transformer Models

A model architecture that uses attention mechanisms, typically used for tasks like machine translation and text summarization. Transformers often form the basis for more complex models like BERT, GPT, etc.

BERT (Bidirectional Encoder Representations from Transformers)

A transformer-based machine learning technique for NLP. BERT is designed to pretrain deep bidirectional representations from unlabeled text by jointly

Machine Vision Glossary of Terms

Terms related to how machines see and interpret images and videos. Includes Computer Vision, Image Recognition, Object Detection, and Segmentation.

Machine Vision

An area of AI that trains machines to visually interpret their environment. This is often used in quality control, robot guidance, and other areas where visual data is needed.

Computer Vision

A field of AI that trains computers to interpret and understand the visual world. This includes methods for acquiring, processing, analyzing, and understanding images and high-dimensional data from the real world.

Image Recognition

The process of identifying and detecting an object or a feature in a digital image or video. This concept is used in many applications like systems for factory automation, toll booth monitoring, and security surveillance.

Object Detection

An AI technology that goes beyond image recognition. Instead of just identifying what’s in an image, object detection can also identify where those objects are within the image.

Image Segmentation

A process of partitioning a digital image into multiple segments (sets of pixels), typically to make the image easier to analyze.

Edge Detection

A technique used to identify the boundaries of objects within images. It works by detecting discontinuities in brightness.

Optical Character Recognition (OCR)

The technology used to convert different types of documents, such as scanned paper documents, PDF files, or images captured by a digital camera, into editable and searchable data.

Facial Recognition

A biometric software application capable of uniquely identifying or verifying a person by comparing and analyzing patterns based on the person’s facial contours.

Feature Extraction

In the context of machine vision, this involves reducing the amount of resources required to describe a large set of data. When performing analysis of complex data one of the major problems stems from the number of variables involved.

Scene Reconstruction

A process of creating a 3D model of a scene from a sequence of 2D images.

Image Synthesis

The process of creating new images. This can be done in various ways, including by using models trained via machine learning, such as GANs.

Convolutional Neural Networks (CNNs)

A class of deep learning neural networks, most commonly applied to analyzing visual imagery.

Capsule Networks (CapsNets)

A type of artificial neural network that can better model hierarchical relationships. Invented by Geoffrey Hinton and his team.

Pose Estimation

Pose estimation refers to computer vision techniques that detect human figures in images and videos, so that one could determine, for example, where someone’s elbow shows up in an image.

Semantic Segmentation

It refers to the process of linking each pixel in an image to a class label. It’s a form of pixel-level prediction because each pixel in an image is classified according to a category.

Instance Segmentation

It is an improvement of semantic segmentation. It classifies each pixel in the image and also differentiates each object instance in the image. For example, if there are 3 cars in an image, instance segmentation will not only classify pixels belonging to cars but will also distinguish between the 3 cars.

Robotics and Automation Glossary of Terms

Terms related to the application of AI in automating physical tasks. For example, Autonomous Vehicles, Industrial Robots, Robot Manipulators, and Swarm Robots.


The field of technology that deals with the design, construction, operation, and application of robots, as well as computer systems for their control, sensory feedback, and information processing.


The technique of making an apparatus, a process, or a system operate automatically.

Autonomous Vehicles (AV)

A vehicle that can guide itself without human conduction. This kind of vehicle has become a concrete reality and may pave the way for future systems where computers take over the art of driving.

Industrial Robots

Robots used in an industrial manufacturing environment. Usually, these are articulated arms specifically designed for applications such as material handling, painting, welding, and others.

Robot Manipulator

A type of mechanical arm usually programmable and used to manipulate objects in a variety of applications.

Swarm Robots

A collection of robots working together to achieve a common goal, taking inspiration from social insects like ants and bees. Each robot operates individually, but their collective behavior results in accomplishing the task.

Robotic Process Automation (RPA)

The use of software robots or “bots” to automate highly repetitive routine tasks normally performed by knowledge workers.

Humanoid Robots

Robots that are built to resemble the human body.

Haptic Feedback

The use of the sense of touch in a user interface design to provide information to an end user.

SLAM (Simultaneous Localization and Mapping)

The computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent’s location within it.


A component of a machine that is responsible for moving and controlling a mechanism or system. In robotics, it’s what makes a robot move.

Robot Operating System (ROS)

A flexible framework for writing robot software, designed to simplify the task of creating complex and robust robot behavior across a wide variety of robotic platforms.

Sensor Fusion

Combining sensory data from disparate sources, providing more accurate, reliable, and contextualized data than would be possible from any individual sensor.


The ‘hand’ of a robot. They can be designed to perform any desired task depending on the application, such as gripping, lifting, etc.

Cobots (Collaborative Robots)

Robots intended to physically interact with humans in a shared workspace, designed to assist humans as a guide or assistor, rather than replacing them.

Teach Pendant

A device which remotely controls the robot. It is used to teach the robot points and paths along its operating route.

AI in Data Analysis Glossary of Terms

Terms related to the use of AI in data mining, interpretation, and prediction. Includes Data Preprocessing, Feature Extraction, Data Imputation, and Predictive Analytics.

Data Analysis

The process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.

Data Mining

The process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.

Data Preprocessing

The process of converting or mapping data from the initial “raw” form into another format, in order to prepare the data for further analysis.

Feature Extraction

The process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy on unseen data.

Data Imputation

The process of replacing missing data with substituted values.

Predictive Analytics

Encompasses a variety of statistical techniques from data mining, predictive modeling, and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events.

Descriptive Analytics

The examination of data or content, usually manually performed, to answer the question “What happened?”.

Diagnostic Analytics

A form of advance analytics which examines data or content to answer the question “Why did it happen?”, and is characterized by techniques such as drill-down, data discovery, data mining and correlations.

Prescriptive Analytics

The area of business analytics dedicated to finding the best course of action for a given situation.

Time Series Analysis

Methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data.

Outlier Detection

The process of identifying rare items, events or observations which raise suspicions by differing significantly from the majority of the data.

Anomaly Detection

The identification of items, events or observations which do not conform to an expected pattern or other items in a dataset.

Regression Analysis

A set of statistical processes for estimating the relationships among variables.


The process of predicting the class of given data points. Classes are sometimes called as targets, labels or categories.


The task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups (clusters).

Association Rule Learning

A rule-based machine learning method for discovering interesting relations between variables in large databases.

Ensemble Learning

The process by which multiple models, such as classifiers or experts, are strategically generated and combined to solve a particular computational intelligence problem.

Dimensionality Reduction

The process of reducing the number of random variables under consideration, via obtaining a set of principal variables.

AI Tools, Libraries, and Frameworks Glossary of Terms

Names and brief descriptions of popular software and libraries used in AI development. Examples are TensorFlow, PyTorch, Keras, and Scikit-learn.


An open-source software library developed by the Google Brain team, used for machine learning and deep learning for a range of tasks. It provides a flexible platform for defining and running computations that involve tensors, which are partial derivatives of a function with respect to its parameters.


An open-source machine learning library developed by Facebook’s AI Research lab. PyTorch has strong GPU acceleration support and a focus on usability, making it a popular choice for deep learning research.


An open-source neural network library written in Python designed to enable fast experimentation with deep neural networks. It focuses on being user-friendly, modular, and extensible. It is typically used with a backend like TensorFlow.


A free software machine learning library for Python programming language. It features various classification, regression, and clustering algorithms, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.


A software library written for data manipulation and analysis in Python. Offers data structures and operations for manipulating numerical tables and time series.


A library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.


An open-source Python library used for scientific and technical computing. It contains modules for optimization, linear algebra, integration, interpolation, signal and image processing, and many more.


A plotting library for Python and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits.


A Python data visualization library based on matplotlib, providing a high-level interface for creating attractive and informative statistical graphics.

NLTK (Natural Language Toolkit)

A set of libraries for symbolic and statistical natural language processing for English written in the Python programming language.


An open-source software library for advanced natural language processing, written in Python and Cython.


A robust open-source vector space modeling and topic modeling toolkit implemented in Python. It uses NumPy, SciPy and optionally Cython for performance.

OpenCV (Open Source Computer Vision Library)

A library of programming functions mainly aimed at real-time computer vision.

Caffe (Convolutional Architecture for Fast Feature Embedding)

A deep learning framework that allows developers to create artificial neural networks (ANNs) on a brewed machine. The name Caffe is an acronym of the original organization that developed it (Berkeley AI Research lab).


A Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.

Apache MXNet

An open-source deep learning software framework, used to train, and deploy deep neural networks.

A deep learning library for PyTorch that makes AI simpler, providing high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains.

AI Ethics and Society Glossary of Terms

Terms related to the ethical and societal implications of AI. Topics might include Algorithmic Bias, Transparency, Explainability, and Privacy Issues.

AI Ethics

The aspect of AI research and application concerned with ensuring that AI systems operate in a way that is ethical, fair, and just. This encompasses a wide range of topics, including bias, transparency, and the impact of AI on society.

Algorithmic Bias

This occurs when an algorithm produces results that are systematically prejudiced due to erroneous assumptions in the machine learning process. This can lead to unfair outcomes, such as discrimination against certain demographic groups.


In the context of AI, transparency refers to the openness and understandability of an AI system. This includes understanding how the system works, what data it uses, and how it makes decisions.


The extent to which a machine learning model’s behavior can be understood by humans. For AI systems to be trusted and effectively used, it’s important that people can understand how decisions or predictions are being made.

Privacy Issues

These arise when AI systems collect, store, and analyze personal data. There are concerns about who has access to this data, how it’s being used, and how securely it’s stored.

Data Protection

The process of safeguarding important information from corruption, compromise, or loss.


In the context of AI, fairness refers to the idea that AI systems should make decisions without favoring or discriminating against certain groups of people.

Black-box Model

A type of AI system where the internal workings are not understood or made transparent to the user. This can raise ethical concerns as it can lead to a lack of accountability for the decisions made by the AI.

White-box Model

In contrast to black-box models, white-box models are AI systems where the internal workings are understood and can be examined. This transparency can improve trust and accountability.


The ethics of robotics, a sub-field of ethics focusing on how humans design, construct, use, and treat robots and other artificially intelligent beings.

Surveillance Capitalism

A business model in which a company provides free services to users, collects data on users while they use these services, and then monetizes that data, often through targeted advertising.

Social Scoring

The use of AI and machine learning to analyze a person’s social behaviors and attributes, and assign a numerical “score” that can be used for various purposes, such as credit assessment or eligibility for services.

Data Discrimination

A concept where unfair treatment is a consequence of algorithms and machine learning, data discrimination happens when prejudiced assumptions are built into data models, leading to harm or detriment to certain groups or individuals.

Data Sovereignty

The concept that information in digital form is subject to the laws of the country in which it is located or stored.

AI Hardware and Infrastructure Glossary of Terms

Covers hardware components and infrastructure needed to support AI operations. Examples include Graphics Processing Unit (GPU), Tensor Processing Unit (TPU), Cloud Computing, and Edge Computing.

Graphics Processing Unit (GPU)

A specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. In the context of AI, GPUs are often used for training and running neural networks due to their ability to perform many calculations in parallel.

Tensor Processing Unit (TPU)

An AI accelerator application-specific integrated circuit (ASIC) developed by Google specifically for accelerating machine learning workloads.

Central Processing Unit (CPU)

The primary component of a computer that performs most of the processing inside the computer. CPUs may not be as efficient as GPUs and TPUs for certain AI workloads due to their architecture, but they are still used in many AI applications.

Application Specific Integrated Circuits (ASICs)

A type of hardware component that is custom designed for a specific application or purpose. ASICs are often used in AI for specific machine learning workloads where general-purpose processors would be less efficient.

Field Programmable Gate Arrays (FPGAs)

A semiconductor device that can be programmed or reprogrammed to the desired application or functionality after manufacturing. FPGAs are used in some AI applications due to their versatility and efficiency.

Cloud Computing

The delivery of computing services—including servers, storage, databases, networking, software, analytics, and intelligence—over the Internet (“the cloud”) to offer faster innovation, flexible resources, and economies of scale.

Edge Computing

A distributed computing paradigm that brings computation and data storage closer to the location where it is needed, to improve response times and save bandwidth. In the context of AI, edge computing can be used to process data from IoT devices locally, reducing latency.

Internet of Things (IoT)

The network of physical objects—”things”—that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the Internet.

Neuromorphic Computing

A branch of computing that mimics the neural structure of the human brain in hardware. It’s a subfield of AI focused on the hardware aspect of building AI systems.

Quantum Computing

A type of computation that utilizes quantum bits or ‘qubits’, which can be in multiple states at once, to perform complex calculations quickly. While still largely experimental, quantum computing could potentially revolutionize AI by dramatically increasing computational speed and power.

Data Center

A large group of networked computer servers typically used by organizations for the remote storage, processing, or distribution of large amounts of data.

High-Performance Computing (HPC)

The use of supercomputers and parallel processing techniques for solving complex computational problems.

Inference Engine

A part of an AI system that applies logical rules to the knowledge base to deduce new information, often used in production environments to make predictions based on a trained model.

AI Business and Strategy Glossary of Terms

This would include terms and concepts related to the application of AI in business and strategic decision-making, such as AI Strategy, AI Governance, and AI ROI.

AI Strategy

The approach a company takes to integrate AI into its business models, processes, and products. This can include the types of AI technology the company plans to use, the resources it will allocate to AI projects, and the outcomes it hopes to achieve with AI.

AI Governance

The system of rules and procedures that guide how AI is used within an organization. This includes policies for data privacy, algorithmic fairness, and ethical considerations.

AI ROI (Return on Investment)

The financial benefit or value gained from investing in AI technology. This is usually calculated by subtracting the cost of the AI investment from the financial gains it produces, then dividing by the cost of the investment.

Digital Transformation

The integration of digital technology into all areas of a business, fundamentally changing how you operate and deliver value to customers. It’s also a cultural change that requires organizations to continually challenge the status quo, experiment, and get comfortable with failure.

AI Maturity Model

A model that describes the stages through which an organization develops its AI capabilities, from initial experimentation to institutionalization of AI practices.

Predictive Analytics

The use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data.

Prescriptive Analytics

A form of advanced analytics which examines data or content to answer the question “What should be done?” or “What can we do to make ‘X’ happen?”, and is characterized by techniques such as graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics, and machine learning.

AI Bias

The concept that an AI system can have biased results due to flaws in the data or the algorithms used.


An AI program that can conduct a conversation with human users, usually over the internet.

Machine Learning as a Service (MLaaS)

A range of services that offer machine learning tools as part of cloud computing services. This can include tools for data visualization, facial recognition, natural language processing, and predictive analytics.

Robotic Process Automation (RPA)

The use of software with AI and machine learning capabilities to handle high-volume, repeatable tasks that previously required humans to perform.

Data-driven Decision Making (DDDM)

An approach to business governance that values decisions that can be backed up with verifiable data.

AI Regulations and Policy Glossary of Terms

Terms related to laws and regulations impacting AI, including things like Data Protection Regulation, AI Laws, and Intellectual Property Rights in AI.

Data Protection Regulation

Laws and regulations designed to protect individuals’ personal data, which can often come into play in AI systems that handle personal data. A key example is the General Data Protection Regulation (GDPR) in the EU.

AI Laws

Legal rules specifically aimed at AI systems. These can cover everything from the ethical use of AI to liability issues if an AI system causes harm.

Intellectual Property Rights in AI

Legal rights that could apply to AI systems or outputs, including copyright (for things like AI-generated music or text) and patents (for AI technology). This is a complex and evolving area of law.

Algorithmic Accountability

The principle that organizations should be held responsible for the decisions made by the AI systems they use, particularly if those decisions have a significant impact on individuals or society.

Algorithmic Transparency

The principle that the workings of AI systems should be open to scrutiny. This can be particularly important in the case of AI systems that make significant decisions, such as those used in credit scoring or judicial decisions.

AI Ethics Guidelines

Sets of principles designed to guide the ethical development and use of AI systems. These have been produced by various bodies, including government departments, academic institutions, and industry groups.

Automated Decision-Making

The process of making a decision by automated means without any human involvement. This includes decisions that are based solely on automated processing, including profiling.

Data Privacy

The aspect of information technology that deals with the ability an organization or individual has to determine what data in a computer system can be shared with third parties.

Data Breach

A security incident in which information is accessed without authorization.

Facial Recognition Systems

AI systems that identify or verify a person from a digital image or a video frame from a video source. These systems have been controversial due to their potential for misuse and infringement of privacy rights.

Bias in AI

AI systems might reflect, amplify, or produce biases that exist in the data used to train them, or in their algorithms.

AI for Good

A movement and a research field that aims to ensure that AI technology is used to benefit humanity, prevent harmful effects, and ensure that the benefits are shared by all.

We hope this Definitive Glossary of AI Terms has provided you with a better understanding of the lexicon of the world of AI, especially as custom AI for business becomes more commonplace.

If you’re interested in reading more you might want to check out our ebook, A Step-by-Step Journey to Custom AI for Your Business

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