Artificial Intelligence (AI) is filled with technical terms and jargon that can overwhelm newcomers. This article will provide a beginner-friendly guide to some of the most commonly used AI terminologies, explaining each term in simple language and providing practical examples. Whether you are new to AI or just looking to brush up on your terminology, this article will be the best guide.
We will do our best to keep the information current and accurate to the best of our knowledge. However, don't hesitate to contact us if you feel a correction is required.
Artificial Intelligence (AI)
The ability of machines to simulate human intelligence and perform tasks that typically require human intelligence, such as learning, reasoning, problem-solving, and perception.
Machine Learning (ML)
A subset of AI that teaches machines to learn from data and improve their performance on a specific task without being explicitly programmed.
Deep Learning (DL) - A type of machine learning that uses neural networks with multiple layers to learn representations of data and perform complex tasks, such as image and speech recognition.
- AutoML - Automated Machine Learning
Natural Language Processing (NLP)
A branch of AI that teaches machines to understand, interpret, and generate human language.
A field of AI that teaches machines to interpret and analyze visual information from the world, such as images and videos.
Reinforcement Learning (RL)
A machine learning type involving an agent learning through trial and error to maximize rewards in a specific environment.
Neural Network (NN)
A computational model inspired by the structure and function of the human brain, consisting of layers of interconnected nodes that process and transmit information.
- Convolutional Neural Network (CNN) - A type of deep learning network used for image processing
A type of machine learning where the algorithm learns from labeled data to make predictions on new data.
Unsupervised Learning - A type of machine learning where the algorithm learns from unlabeled data to discover patterns and relationships.
The process of extracting valuable information from large datasets through machine learning, statistical analysis, and database techniques.
Large, complex datasets that require advanced computing tools and algorithms to process and analyze.
A type of machine learning model that uses a tree-like structure to make decisions and predictions based on multiple variables and outcomes.
A type of unsupervised learning that involves grouping similar data points together based on their similarities and differences.
Artificial Neural Network (ANN)
A computational model inspired by the structure and function of biological neural networks, used for various AI tasks such as image recognition, speech recognition, and natural language processing.
A technique where a pre-trained model is used as a starting point for training a new model on a different task or dataset.
A computer program that simulates human conversation
A subset of machine learning that uses algorithms to learn from data
Generative Adversarial Networks (GANs)
A type of neural network architecture used for generating new data
Natural Language Processing (NLP)
A subfield of AI that focuses on the interaction between computers and human language
A type of machine learning that involves training an agent to take actions in an environment to maximize a reward signal
A type of machine learning where a model learns from labeled data to predict outcomes for new data
A type of machine learning where a model learns from unlabeled data to discover patterns and relationships.