AI Glossary

The market for AI tools is rapidly expanding, driven by the growing adoption of AI in various industries. According to market research, the global AI tools market size is expected to reach $30.8 billion by 2026, growing at a CAGR of 33.2% from 2021 to 2026. The market growth is driven by factors such as the increasing demand for AI-powered chatbots and virtual assistants, the need for automating business processes, and the growing demand for intelligent decision-making tools. The AI tools market is segmented by type, including software tools, platform tools, and services. North America is expected to dominate the market, followed by Europe and the Asia Pacific region. The increasing adoption of AI in various industries such as healthcare, finance, and retail is expected to drive the growth of the AI tools market in the coming years.

Below is an AI glossary containing trending terms, definitions, and frameworks. It serves as a guide for beginners, AI aspirants, and data experts. If you think there are any terms we should add, please let us know.


AI (Artificial Intelligence) – A field of computer science that aims to create machines that can simulate human intelligence and perform tasks that typically require human-like reasoning, perception, and decision making.

Algorithm – A set of rules or procedures that a computer program uses to solve a problem or perform a task.

ANNs (Artificial Neural Networks) – A type of machine learning algorithm that is inspired by the structure and function of the human brain.

Application Programming Interface(API):

An API, or application programming interface, is a set of rules and protocols that allows different software programs to communicate and exchange information with each other. It acts as a kind of intermediary, enabling different programs to interact and work together, even if they are not built using the same programming languages or technologies. API’s provide a way for different software programs to talk to each other and share data, helping to create a more interconnected and seamless user experience.


Big Data – Refers to the large and complex data sets that are difficult to process using traditional data processing methods.

Bias – An unwanted characteristic in a machine learning model that causes it to produce inaccurate or unfair results.

Black Box – A term used to describe an AI system whose workings are opaque or not easily understood.


Chatbot – An AI-powered computer program that can conduct a conversation with humans via text or speech.

Computer Vision – A field of AI that aims to teach machines to interpret and understand visual information from the world.

Convolutional Neural Network (CNN) – A type of artificial neural network that is particularly well-suited for image recognition tasks.

Compute Unified Device Architecture(CUDA):

CUDA is a way that computers can work on really hard and big problems by breaking them down into smaller pieces and solving them all at the same time. It helps the computer work faster and better by using special parts inside it called GPUs. It’s like when you have lots of friends help you do a puzzle – it goes much faster than if you try to do it all by yourself.

The term “CUDA” is a trademark of NVIDIA Corporation, which developed and popularized the technology.


Data Mining – The process of discovering patterns and insights in large data sets using machine learning techniques.

Deep Learning – A subfield of AI that uses artificial neural networks with many layers to solve complex problems.

Decision Trees – A type of machine learning algorithm that uses a tree-like model to make decisions based on input data.

Data Processing:

The process of preparing raw data for use in a machine learning model, including tasks such as cleaning, transforming, and normalizing the data.


Ethics – The study of moral principles that govern human behavior and decision-making.

Explainability – The ability of an AI system to explain its reasoning and decision-making processes to humans.

Expert System – An AI system that emulates the decision-making ability of a human expert in a particular domain.


When we want a computer to understand language, we need to represent the words as numbers because computers can only understand numbers. An embedding is a way of doing that. Here’s how it works: we take a word, like “cat”, and convert it into a numerical representation that captures its meaning. We do this by using a special algorithm that looks at the word in the context of other words around it. The resulting number represents the word’s meaning and can be used by the computer to understand what the word means and how it relates to other words. For example, the word “kitten” might have a similar embedding to “cat” because they are related in meaning. Similarly, the word “dog” might have a different embedding than “cat” because they have different meanings. This allows the computer to understand relationships between words and make sense of language.


Facial Recognition – A technology that uses AI algorithms to identify and verify people based on their facial features.

Feature Engineering – The process of selecting and transforming raw data into features that are relevant to a machine learning model.

Fuzzy Logic – A mathematical framework that allows for uncertainty and imprecision in reasoning and decision-making.

Freemium: You might see the term “Freemium” used often on this site. It simply means that the specific tool that you’re looking at has both free and paid options. Typically there is very minimal, but unlimited, usage of the tool at a free tier with more access and features introduced in paid tiers.


Genetic Algorithms – A type of machine learning algorithm that uses concepts from natural selection to optimize solutions to a problem.

GPU (Graphics Processing Unit) – A specialized type of computer chip that is optimized for performing complex calculations in parallel, often used in deep learning.

GPT (Generative Pre-trained Transformer) – A type of AI model that uses unsupervised learning to generate natural language text.

Generative Adversarial Network(GAN):

A type of computer program that creates new things, such as images or music, by training two neural networks against each other. One network, called the generator, creates new data, while the other network, called the discriminator, checks the authenticity of the data. The generator learns to improve its data generation through feedback from the discriminator, which becomes better at identifying fake data. This back and forth process continues until the generator is able to create data that is almost impossible for the discriminator to tell apart from real data. GANs can be used for a variety of applications, including creating realistic images, videos, and music, removing noise from pictures and videos, and creating new styles of art.

Generative Art:

Generative art is a form of art that is created using a computer program or algorithm to generate visual or audio output. It often involves the use of randomness or mathematical rules to create unique, unpredictable, and sometimes chaotic results.

Giant Language model Test Room(GLTR):

GLTR is a tool that helps people tell if a piece of text was written by a computer or a person. It does this by looking at how each word in the text is used and how likely it is that a computer would have chosen that word. GLTR is like a helper that shows you clues by coloring different parts of the sentence different colors. Green means the word is very likely to have been written by a person, yellow means it’s not sure, red means it’s more likely to have been written by a computer and violet means it’s very likely to have been written by a computer.

GitHub: GitHub is a platform for hosting and collaborating on software projects

Google Colab: Google Colab is an online platform that allows users to share and run Python scripts in the cloud


Heuristics – A problem-solving approach that uses a set of rules or guidelines to guide decision-making.

Human-in-the-Loop – An approach to AI development that involves human input and oversight at various stages of the system’s development and deployment.

Hyperparameters – Parameters in a machine learning model that are not learned from the data but must be set before training.


Image Recognition – The ability of an AI system to recognize and classify objects in digital images.

Inductive Learning – A machine learning approach that involves inferring general rules from specific examples.

Inference – The process of applying a machine learning model to new data to make predictions or decisions.


Jupyter Notebook – An open-source web application that allows users to create and share documents that contain live code, equations, visualizations, and narrative text.

Just-In-Time Learning – A machine learning approach that involves training a model on the fly using data that is generated during use.

Java – A popular programming language


K-Means Clustering – A type of unsupervised machine learning algorithm that is used to group data points into clusters based on their similarity.


LSTM (Long Short-Term Memory) – A type of recurrent neural network that is particularly well-suited for processing sequential data.

Learning Rate – A hyperparameter that controls the rate at which a machine learning model learns from data during training.

Linear Regression – A type of supervised machine learning algorithm that is used to predict a continuous output variable based on one or more input variables.


LangChain is a library that helps users connect artificial intelligence models to external sources of information. The tool allows users to chain together commands or queries across different sources, enabling the creation of agents or chatbots that can perform actions on a user’s behalf. It aims to simplify the process of connecting AI models to external sources of information, enabling more complex and powerful applications of artificial intelligence.

Large Language Model(LLM):

A type of machine learning model that is trained on a very large amount of text data and is able to generate natural-sounding text.


Machine Learning – A field of AI that involves training machines to learn from data and make predictions or decisions.

Model – A mathematical representation of a machine learning algorithm that can be trained on data to make predictions or decisions.


Natural Language Processing (NLP) – A field of AI that involves teaching machines to understand and generate natural language text.

Neural Network – A type of machine learning algorithm that is inspired by the structure and function of the human brain.

Nonlinear Regression – A type of supervised machine learning algorithm that is used to predict a continuous output variable based on one or more input variables, where the relationship between the inputs and output is nonlinear.

Normalization – The process of scaling numerical data to a range of values between 0 and 1, to help improve the performance of machine learning algorithms.

Neural Radiance Fields(NeRF):

Neural Radiance Fields are a type of deep learning model that can be used for a variety of tasks, including image generation, object detection, and segmentation. NeRFs are inspired by the idea of using a neural network to model the radiance of an image, which is a measure of the amount of light that is emitted or reflected by an object.


Object Detection – The ability of an AI system to locate and identify objects within an image or video.

Overfitting – A common problem in machine learning where a model becomes too specialized to the training data and performs poorly on new data.

Optimization – The process of finding the best set of parameters for a machine learning model to achieve the desired performance.


OpenAI is a research institute focused on developing and promoting artificial intelligence technologies that are safe, transparent, and beneficial to society


A common problem in machine learning, in which the model performs well on the training data but poorly on new, unseen data. It occurs when the model is too complex and has learned too many details from the training data, so it doesn’t generalize well.


Preprocessing – The process of cleaning, transforming, and preparing data for use in machine learning models.

Precision – A measure of how accurate a machine learning model is in predicting positive results.

Python – A popular programming language for data science and machine learning.

Prompt: A prompt is a piece of text that is used to prime a large language model and guide its generation


Q-Learning – A type of reinforcement learning algorithm that involves learning an optimal policy for an agent to take actions in an environment.

Quantum Computing – A type of computing that uses quantum-mechanical phenomena to perform operations on data.


Random Forest – A type of machine learning algorithm that uses a collection of decision trees to make predictions.

Reinforcement Learning – A type of machine learning that involves training an agent to take actions in an environment to maximize a reward signal.

Regression – A type of machine learning algorithm that is used to predict a continuous output variable based on one or more input variables.


SVM (Support Vector Machine) – A type of machine learning algorithm that is used for classification and regression analysis.

Supervised Learning – A type of machine learning that involves training a model on labeled data to make predictions or decisions.

Synthetic Data – Artificially generated data that is used to train machine learning models.

Spatial Computing:

Spatial computing is the use of technology to add digital information and experiences to the physical world. This can include things like augmented reality, where digital information is added to what you see in the real world, or virtual reality, where you can fully immerse yourself in a digital environment. It has many different uses, such as in education, entertainment, and design, and can change how we interact with the world and with each other.

Stable Diffusion:

Stable Diffusion generates complex artistic images based on text prompts. It’s an open source image synthesis AI model available to everyone. Stable Diffusion can be installed locally using code found on GitHub or there are several online user interfaces that also leverage Stable Diffusion models.

Supervised Learning:

A type of machine learning in which the training data is labeled and the model is trained to make predictions based on the relationships between the input data and the corresponding labels.


TensorFlow – A popular open-source machine learning framework developed by Google.

Transfer Learning – A machine learning technique that involves using a pre-trained model as a starting point for a new task or problem.

Text Mining – The process of extracting useful information from unstructured text data.

Temporal Coherence:

Temporal Coherence refers to the consistency and continuity of information or patterns across time. This concept is particularly important in areas such as computer vision, natural language processing, and time-series analysis, where AI models need to process and understand data that evolves over time.

Temporal coherence can be viewed from different perspectives, depending on the specific application:

  1. In computer vision, temporal coherence might refer to the smoothness and consistency of visual content in videos, where objects and scenes should maintain their properties and relationships across frames.
  2. In natural language processing, it could refer to the consistency and flow of information in a text or conversation, ensuring that the AI model generates responses or summaries that logically follow previous statements or events.
  3. In time-series analysis, temporal coherence could relate to the consistency of patterns and trends in the data, such that the AI model can predict future values based on past observations.


Unsupervised Learning – A type of machine learning that involves training a model on unlabeled data to find patterns and structure in the data.

U-Net – A type of convolutional neural network that is commonly used for image segmentation tasks.

Unicode – A universal character encoding standard that assigns a unique number to each character


 Validation Set – A subset of the data used to evaluate the performance of a machine learning model during training.

Variational Autoencoder (VAE) – A type of generative model that learns a low-dimensional representation of data and can generate new data samples.

Vector – A mathematical object that represents a quantity with both magnitude and direction, often used to represent data in machine learning.


Word Embedding – A technique for representing words as dense vectors in a high-dimensional space, often used in natural language processing tasks.

Weight – A parameter in a machine learning model that determines the importance of a particular feature or input.

Weight Initialization – The process of setting the initial values of the weights in a neural network to optimize training performance.


A webhook is a way for one computer program to send a message or data to another program over the internet in real-time. It works by sending the message or data to a specific URL, which belongs to the other program. Webhooks are often used to automate processes and make it easier for different programs to communicate and work together. They are a useful tool for developers who want to build custom applications or create integrations between different software systems.


Xavier Initialization – A specific method of weight initialization that is designed to improve the convergence of neural networks during training.


YAML – A human-readable data serialization language often used for configuration files in machine learning applications.

Yield Curve – A visual representation of the relationship between the interest rates of debt securities with different maturities.


Zero-Shot Learning – A type of machine learning that involves training a model to recognize new categories or classes of objects without any examples of those categories during training.