AI Glossary

Artificial Intelligence (AI): The capability of a machine to imitate intelligent human behavior. AI systems can perform tasks that typically require human intelligence, such as understanding natural language, recognizing patterns, solving problems, and decision making.

Machine Learning (ML): A subset of AI that enables machines to improve at tasks with experience. It involves training a model using large amounts of data and algorithms that allow it to learn how to perform the task.

Deep Learning: A subset of machine learning based on artificial neural networks with representation learning. Deep learning can learn from enormous amounts of unstructured data such as text, images, or video.

Neural Networks: Computational models that are loosely inspired by the human brain and consist of layers of interconnected nodes (neurons). They are used in deep learning models to process complex data inputs.

Natural Language Processing (NLP): A branch of AI that helps computers understand, interpret, and respond to human language in a way that is both meaningful and useful.

Computer Vision: An AI field that trains computers to interpret and understand the visual world using digital images from cameras and videos and deep learning models.

Algorithm: A set of rules or instructions given to an AI system to help it learn from data and make decisions.

Data Mining: The process of discovering patterns and useful information from large data sets using machine learning, statistics, and database systems.

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.

Big Data: Large, complex data sets that are analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

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

Bias in AI: Inherent or learned bias in AI systems usually arises from biases in training data or the algorithmic framework, leading to skewed or unfair outcomes.

Explainable AI (XAI): AI systems designed with a layer of transparency that allows humans to understand and trust the outputs of the model. This is crucial for deploying AI in sensitive or critical domains.

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.

AI Ethics: The branch of ethics that considers how AI should be designed, used, and managed to ensure fairness, accountability, and transparency in automated systems.