AI Glossary
A step-by-step procedure or formula for solving a problem, often used in AI systems for processing data.
Predictive Analytics: The use of statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data.
The simulation of human intelligence processes by machines, especially computer systems, including learning, reasoning, and self-correction.
A network of artificial neurons designed to mimic the way the human brain processes information, widely used in machine learning and deep learning.
A technique used in data mining to discover interesting relationships between variables in large datasets, often used for market basket analysis.
A cloud-based service model that provides access to AI tools, platforms, and technologies without the need to build or maintain infrastructure.
The framework and processes used to ensure that AI systems are developed, implemented, and used responsibly, ethically, and transparently.
Extremely large datasets that require advanced processing technologies to analyze and extract meaningful insights.
AI Ethics: A field focused on the moral implications and societal impacts of artificial intelligence systems.
A systematic error introduced into AI models due to skewed data, human influence, or other factors, leading to unfair or inaccurate results.
A step-by-step procedure or formula for solving a problem, often used in AI systems for processing data.
Predictive Analytics: The use of statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data.
A technology that uses AI to analyze conversations (text or voice) to gain insights, improve customer interactions, detect sentiment, and optimize communication strategies.
A specialized deep learning algorithm commonly used in computer vision tasks such as image classification and object detection.
A marketing approach that harnesses the collective efforts of a large group or community to promote a product, brand, or service. It involves using crowdsourcing techniques and AI to optimize outreach and engagement efforts.
A field of AI that enables computers to interpret and make decisions based on visual inputs such as images or videos.
The process of analyzing large sets of data to discover patterns, correlations, and trends that can be used for predictive analytics or decision-making.
A type of machine learning that uses neural networks with many layers to analyze large amounts of data.
A technique used to reduce the number of features in a dataset while retaining as much information as possible, often used in preprocessing steps.
The practice of processing data closer to its source (at the edge of the network) rather than in a centralized data center, often used to enable real-time AI applications.
AI systems that provide clear and understandable explanations of how decisions or predictions are made, aiming to improve transparency and trust.
The process of selecting and transforming raw data into a set of features that can be used by machine learning algorithms.
The practice of selecting, modifying, or creating new features to improve the performance of machine learning models.
A machine learning framework that consists of two neural networks competing with each other to generate data that mimics real-world data.
A high-level neural networks API that runs on top of TensorFlow, designed to simplify the process of building deep learning models.
A subset of AI that involves training algorithms to recognize patterns and make decisions based on data.
The use of technology to automate marketing tasks such as email campaigns, social media posting, lead nurturing, customer segmentation, and performance tracking. It improves efficiency and consistency across marketing efforts.
A field of AI that focuses on the interaction between computers and human language, enabling machines to understand and respond to text or speech.
A set of algorithms, modeled after the human brain, designed to recognize patterns by interpreting sensory data.
A modeling error that occurs when a machine learning model learns too much from the training data, resulting in poor generalization to new, unseen data.
A type of machine learning where agents learn to make decisions by performing actions and receiving rewards or penalties.
A type of neural network designed for sequence prediction tasks, such as time-series forecasting or natural language processing.
The use of AI and other tools to manage and optimize social media presence, including content creation, scheduling, engagement, monitoring, and analytics. It helps businesses improve their brand visibility and audience interaction.
A type of machine learning where the model is trained on labeled data (input-output pairs).
A supervised learning algorithm used for classification and regression tasks, which finds the hyperplane that best separates different classes in the data.
Clustering: The process of grouping similar data points together based on certain characteristics or patterns, often used in unsupervised learning.
An open-source framework for machine learning and deep learning developed by Google, widely used for building and training neural networks.
A machine learning technique where a model developed for one task is reused for a different but related task, reducing the need for large datasets.
The dataset used to teach an AI model how to recognize patterns and make predictions or decisions.
Data that is used to assess the performance of a fully trained AI model to ensure it generalizes well to new, unseen data.
A scenario where a machine learning model is too simple and fails to capture the underlying patterns in the data, resulting in poor performance.
A type of machine learning where the model identifies patterns in data without labeled outcomes.
Data used to evaluate the performance of a machine learning model during training and to fine-tune the model’s parameters.
An AI-based system that enables users to interact using voice commands. It uses speech recognition and natural language processing (NLP) to interpret and respond to spoken queries, often used in customer service or virtual assistants.