AI Glossary

Look past the technical jargon to fully understand AI

As AI technology rapidly advances, the vocabulary surrounding it changes quickly too. This explanation of key AI concepts aims to help you comprehend critical ideas and get the most out of AI, especially in data analysis and analytics roles.


AI Assistants
Software agents that can perform tasks or services for an individual based on commands or questions. They use natural language processing and machine learning to assist with tasks, ranging from scheduling to information retrieval.
Artificial Intelligence

The simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. AI can be categorized as either weak or strong, with strong AI being comparable to human cognitive abilities.

Augmented Analytics
The use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation, and insight explanation to augment how people explore and analyze data in analytics and BI platforms.
Augmented Data Quality
The application of machine learning and AI techniques to improve data quality by identifying and correcting inaccuracies and inconsistencies in data.
Automated Insights
The process of using AI to analyze data and generate insights without human intervention, often presented in a natural language format.

A step-by-step procedure or formula for solving a problem. In AI, algorithms are used to process data, make decisions, and learn from outcomes.

Artificial Neural Network

A computational model based on the structure and functions of biological neural networks. Information that flows through the network affects the structure of the ANN because a neural network changes – or learns, in a sense – based on that input and output.

Augmented Data Integration
Tools and technologies that use machine learning and AI to assist with data integration tasks, thereby enhancing the capabilities of data integration specialists and enabling business users to conduct data integration themselves.
The use of AI to automatically classify data into predefined categories, often used in data management and information retrieval.
Autonomous AI

AI systems that can perform tasks in real-world environments without any sort of external control.



An algorithm used for training artificial neural networks, especially deep neural networks. It calculates the gradient of the loss function with respect to each weight by the chain rule, computing the gradient one layer at a time, iterating backward from the last layer to avoid redundant calculations.

Bayesian Networks

Probabilistic graphical models that represent a set of variables and their conditional dependencies via a directed acyclic graph (DAG). They are used for a variety of tasks including prediction, anomaly detection, diagnostics, automated insight, reasoning, time series prediction and decision making under uncertainty.

In machine learning, this refers to techniques used to adjust the dataset to account for imbalances in the training data, which can lead to biased models.


Citizen Data Scientist
A person who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics.
Conversational AI
A subset of AI that enables natural and effective interactions between humans and computers using natural language.
Computer Vision

The field of AI that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs, and take actions or make recommendations based on that information.

Cognitive BI
The use of cognitive computing technologies within business intelligence strategies to create systems that simulate human thought processes and help with decision-making.
Convolutional Neural Network

A deep learning algorithm which can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other.


Data Science
An interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
Data Labelling
The process of identifying raw data (like images, text files, videos, etc.) and adding one or more meaningful and informative labels to provide context so that a machine learning model can learn from it.
Domain Specific LLM

A language model that is specifically trained on a specialized dataset to perform tasks within a particular domain, such as legal, medical, or technical fields.

Data Governance
The process of managing the availability, usability, integrity, and security of the data in enterprise systems, based on internal data standards and policies that also control data usage.
Deep Learning

A class of machine learning algorithms that use a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input.


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, an experiment often refers to the process of testing different algorithms, hyperparameters, or data subsets to optimize the performance of machine learning models.
Enterprise Search

The practice of making content from multiple enterprise-type sources, such as databases and intranets, searchable to a defined audience.

Explainable AI
AI systems that offer human-interpretable explanations for their decisions, which is crucial for validating and trusting AI solutions.


The process of making predictions about the future based on historical and present data, commonly used in many fields such as finance, supply chain, and meteorology.
Fuzzy Logic

A form of logic that deals with reasoning that is approximate rather than precisely deduced from classical predicate logic. It’s used in systems that must make decisions based on imprecise or incomplete information.

Foundational Model
A large-scale model that serves as a base for building more specialized models. It is pre-trained on a broad dataset and can be fine-tuned for specific tasks.


GAN (Generative Adversarial Network)

A framework for training generative models, which includes a generator that creates samples and a discriminator that tries to distinguish between the generated samples and real data.

Generative AI
A type of AI that can generate new content, such as images, sounds, and text, that is similar but not identical to the content it was trained on.
GPT (Generative Pre-trained Transformer)

A type of language model that uses deep learning to produce human-like text. It is the third generation language prediction model in the GPT-n series created by OpenAI.



A problem-solving approach that employs a practical method or various shortcuts to produce solutions that may not be optimal but are sufficient for reaching an immediate goal.


Intelligent Agent

A system that perceives its environment and takes actions which maximize its chances of success. An intelligent agent may learn from the environment to improve its performance over time.


Joint Attention

The shared focus of two individuals on an object. It is achieved when one individual alerts another to an object by means of eye-gazing, pointing, or other verbal or non-verbal indications.


Key Driver Analysis
A statistical technique used in customer satisfaction studies to identify the factors that are most important in driving the overall satisfaction.
Knowledge Representation

The area of artificial intelligence 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 dialog in natural language.


LLM (Large Language Model)

These are models that have been trained on vast datasets and can process and generate language in a way that captures the nuances and variations in human language.

Platforms that enable users to create applications through graphical user interfaces and configuration instead of traditional computer programming.
Logic Programming

A programming paradigm that uses logic to express computations. It is based on formal logic and can be used to perform logical inference in AI.


Machine Learning

A branch of AI that focuses on the development of computer programs that can access data and use it to learn for themselves. It involves algorithms that learn from patterns of data and make predictions.

Model Authoring
The process of creating, writing, and designing models in AI, which includes selecting the appropriate algorithms and designing the structure of the model.
Model Drift
The loss of predictive power of a machine learning model over time, as the environment changes and the data it was trained on no longer represents the current situation.
ML System

A system that incorporates machine learning algorithms to analyse data, learn from that data, and make informed decisions based on what it has learned.

Model Deployment
The process of integrating a machine learning model into an existing production environment to make practical business decisions based on data.
Model Training
The process of teaching a machine learning model to make predictions or decisions, typically by feeding it large amounts of data and allowing it to adjust its weights and biases.


Natural Language Processing (NLP)

A field of AI that focuses on the interaction between computers and humans through natural language. The ultimate objective of NLP is to read, decipher, understand, and make sense of the human languages in a valuable way.

Neural Network

A neural network is a network or circuit of neurons, or in a modern sense, an artificial neural network, composed of artificial neurons or nodes.

Natural Language Query
The use of natural language processing to allow users to perform searches by typing or speaking a question in natural language.



In the context of AI, optimization involves selecting the best element from some set of available alternatives with respect to some criterion. In machine learning, optimization algorithms are used to minimize the error of a model or to maximize a likelihood function.

Open Data

Data that is available for anyone to use, reuse, and redistribute without any legal, technological or social restriction.



A type of artificial neuron which is the basic unit in an artificial neural network. It can take several binary inputs and produce a single binary output based on a threshold.

Predictive Analytics
The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data.
Predictive AI
AI systems that make predictions about future outcomes based on historical data.
In the context of AI, especially with language models, a prompt is the initial input given to the model to generate a response or continuation.


Quantum Computing

A field of computing focused on developing computer technology based on the principles of quantum theory, which explains the behavior of energy and material on the quantum (atomic and subatomic) level.


A statistical method used in machine learning to model the relationship between a dependent variable and one or more independent variables.
Reinforcement Learning

A type of machine learning algorithm where an agent learns how to behave in an environment by performing actions and seeing the results. It is about taking suitable action to maximize reward in a particular situation.


Structured Data
Data that adheres to a pre-defined data model and is therefore easy to analyze. Structured data resides in fixed fields within a record or file.
Synthetic Data
Artificially generated data produced by computer algorithms, used as a substitute for real-world data in situations where actual data is not available or difficult to obtain.
Supervised Learning

A machine learning task where a model is trained on labeled data. The training process involves providing the model with input-output pairs, from which it must learn to generate predictions for new, unseen data.


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.


Unsupervised Learning

This type of machine learning algorithm is used to draw inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is cluster analysis.


Virtual Assistants

AI-powered software that can perform tasks or services for an individual based on commands or questions. Examples include Amazon’s Alexa, Apple’s Siri, and Google Assistant.


Weak AI

Also known as narrow AI, refers to AI systems that are designed and trained for a particular task. Virtual personal assistants, such as Apple’s Siri, are a form of weak AI.

What-if Scenarios
Analysis methods used to explore and evaluate the potential effects of different actions or decisions before they are taken in the real world.


XML (eXtensible Markup Language)

A flexible text format used to create structured documents by defining a set of rules for encoding documents in a format that is both human-readable and machine-readable.



A unit of information or computer storage equal to one septillion (one followed by 24 zeros) bytes. The size of datasets in big data is often compared to yottabytes.


Zero-shot Learning

A machine learning technique where the model is required to correctly predict the output for inputs that it has not seen during training. This is a challenging scenario in AI research.