Introduction
Starting a career in Artificial Intelligence (AI) requires familiarity with common industry terms and concepts. This AI glossary covers key topics in AI and machine learning, aiding in professional certifications, resume drafting, or job interviews. AI and Natural Language Processing (NLP) technologies are crucial for business operations but often complex.
To simplify discussions and ensure inclusivity, we’ve compiled an AI and NL-specific glossary. This beginner’s guide helps everyone, regardless of technical background, understand essential terms and the impact of technologies like generative AI, large language models, and deep learning. Our glossary aims to demystify generative AI and other AI categories by consolidating terms into a searchable list.
This AI glossary will be updated regularly.
A (Agents – Automation)
Agents
Entities that perceive their environment and take actions to achieve specific goals.
AGI (Artificial General Intelligence)
A form of AI that can understand, learn, and apply knowledge in a broad, human-like manner across a wide range of tasks.
AI (Artificial Intelligence)
The simulation of human intelligence processes by machines, especially computer systems.
AI Ethics
The field of study that examines the ethical and moral implications of AI systems.
Algorithm
A set of rules or instructions given to an AI, or any computer system, to help it learn on its own.
AnimateDiff
An AI technique or tool used in generating animated differences or transformations.
Attention
A mechanism in neural networks that focuses on specific parts of the input data to improve performance.
Augmented Intelligence
Enhancing human decision-making with AI rather than replacing it.
Automation
Using AI technology to perform tasks with minimal human intervention.
B (Bias – Black Box AI)
Bias
Systematic errors in AI systems that can lead to unfair or inaccurate outcomes.
Big Data
Extremely large datasets that can be analyzed computationally to reveal patterns, trends, and associations.
Black Box AI
AI systems whose internal workings are not visible or understandable to users.
C (Chain of Thought – Computer Vision)
Chain of Thought
A reasoning process used by AI to solve problems step-by-step.
Chatbot
A software application used to conduct an online chat conversation via text or text-to-speech.
ChatGPT
A language generation model developed by OpenAI, based on the GPT architecture.
CLIP (Contrastive Language–Image Pretraining)
A neural network that learns visual concepts from natural language descriptions.
CNN (Convolutional Neural Network)
A class of deep neural networks, most commonly applied to analyzing visual imagery.
Colab
A free, cloud-based notebook environment that allows you to write and execute Python in your browser.
Conversational AI
Technology enabling machines to hold human-like conversations.
Computer Vision
The field of AI focused on enabling machines to interpret and make decisions based on visual data.
D (Data Augmentation – Discriminator)
Data Augmentation
Techniques used to increase the amount of data by adding slightly modified copies of already existing data or newly created synthetic data.
Deep Learning
A subset of machine learning involving neural networks with many layers.
Diffusion
A process used in some AI models to generate data by reversing the noise process.
Discriminator
The part of a GAN that learns to distinguish between real and generated data.
E (Embedding – Explainable AI)
Embedding
A representation of data in a lower-dimensional space.
Emergence/Emergent Behavior
Complex patterns and behaviors that arise from simple interactions.
End-to-End Learning
Training a model to perform a task from raw input to output directly.
Expert Systems
AI programs that simulate the judgment and behavior of a human or an organization with expert knowledge and experience.
Explainable AI
AI systems designed to provide human-understandable explanations of their decisions and actions.
F (Fine-tuning – Foundation Model)
Fine-tuning
Adjusting a pre-trained model on a new, specific task.
Foundation Model
A large-scale pre-trained model that can be adapted to various downstream tasks.
G (GAN – GPU)
GAN (Generative Adversarial Network)
A class of machine learning frameworks where two neural networks contest with each other to generate new, synthetic instances of data.
Generative AI
AI models that can generate new content, such as text, images, or music.
Generative Pre-trained Transformer (GPT)
A type of language model that uses deep learning to generate human-like text.
Genetic Algorithm
Computational techniques inspired by natural selection to solve optimization problems.
GPU (Graphics Processing Unit)
A specialized processor designed to accelerate graphics rendering and is widely used in AI for parallel processing capabilities.
H
Hallucinate/Hallucination
When an AI model generates information or details that are not present in the input data.
Hidden Layer
Layers between the input and output layers in a neural network that allow the network to learn complex patterns.
I
Inference
The process of making predictions or decisions based on a trained model.
Image Recognition
The ability of a computer to identify and process images in the same way that human vision does.
Information Processing Language (IPL)
Early high-level programming languages for manipulating data and processing information.
Internet of Things (IoT)
A network of physical objects embedded with sensors and software to connect and exchange data.
K
Knowledge-Based System (KBS)
A computer program that uses a knowledge base to solve complex problems and offer expert advice within a particular domain.
L
Large Language Model (LLM)
A type of AI model with a large number of parameters, capable of understanding and generating human language.
Latent Space
A low-dimensional space representing data in a compressed form.
Machine Learning
A subset of AI involving the development of algorithms that allow computers to learn from and make predictions based on data.
M
Mixture of Experts
A technique in machine learning where multiple models are trained and their outputs combined to improve performance.
Model
An AI system that has been trained to perform a specific task.
N
Natural Language Processing (NLP)
The field of AI focused on the interaction between computers and humans through natural language.
NeRF (Neural Radiance Fields)
A neural network that generates 3D scenes from 2D images.
Neural Network
A set of algorithms, modeled loosely after the human brain, designed to recognize patterns.
No-code AI
AI systems or tools that can be developed without traditional programming skills.
O
Optical Character Recognition (OCR)
Technology that converts different types of documents, such as scanned paper documents or PDFs, into editable and searchable data.
Overfitting
When a model learns the training data too well, including noise and details that do not generalize to new data.
P
Parameters
Variables in a model that are learned during training.
Pre-training
Training a model on a large dataset before fine-tuning it on a specific task.
Prompt
An input given to an AI model to generate a response.
Prompt Engineering
The process of designing prompts to elicit specific responses from AI models.
R
Reinforcement Learning
A type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize cumulative reward.
RLHF (Reinforcement Learning from Human Feedback)
A technique where human feedback is used to guide the learning process of an AI system.
Robotics
The field of engineering and science focused on the design, construction, and operation of robots.
Rule-Based System
AI systems that rely on predefined rules and logic to make decisions and perform tasks.
S
Search Algorithm
A method for finding specific data within a larger dataset.
Self-Organizing Maps (SOM)
A type of artificial neural network that is trained using unsupervised learning to produce a low-dimensional representation of high-dimensional data.
Sentiment Analysis
The use of AI to identify and categorize opinions expressed in text, especially to determine whether the writer’s attitude toward a particular topic is positive, negative, or neutral.
Speech-to-Text
Technology that converts spoken language into written text.
Strong AI (Artificial General Intelligence)
AI systems with human-like intelligence that can understand, learn, and apply knowledge across a wide range of tasks.
Structured Data
Data that is organized in a predefined manner, often in tables or spreadsheets.
T
TensorFlow
An open-source library for numerical computation and machine learning.
TPU (Tensor Processing Unit)
A type of processor designed by Google specifically for machine learning tasks.
Training Data
Data used to train a machine learning model.
Transfer Learning
A technique where a pre-trained model is adapted to a new task.
Transformer
A type of deep learning model designed for handling sequential data, such as language.
Turing Test
A test of a machine’s ability to exhibit intelligent behavior indistinguishable from that of a human.
Text-to-Image Generator
AI tools that create images based on textual descriptions.
Text-to-Speech
Technology that converts written text into spoken words.
Token
A piece of a word used in natural language processing to break down text into smaller parts.
Toxicity
The potential of an AI model to generate harmful or offensive content.
U
Underfitting
When a model is too simple to capture the underlying patterns in the data.
Unsupervised Learning
A type of machine learning where the model learns from unlabeled data.
V
Virtual Reality (VR)
Technology that immerses users in a simulated environment, often using headsets and motion tracking.
Voice Recognition
The ability of a machine to identify and process human voice.
W
Weak AI (Narrow AI)
AI systems designed and trained for a specific task, without the ability to generalize to other tasks.
X
XAI (Explainable AI)
AI designed to provide transparency and understanding of its decision-making processes.
Z
Zero-shot Learning
The ability of a model to make predictions on new, unseen data without any prior training on that specific data.