AI Glossary of Terms for Beginners

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Confused about some of the terms being used as you learn how to leverage AI?  So were we.  Our community has helped us compile a list of AI terms that should be helpful along the way of your AI learning journey.

Think we missed some or add others, please send us a message!

Adversarial Examples: Inputs intentionally designed to mislead or confuse AI models, revealing vulnerabilities in their decision-making processes.

Algorithm: A set of step-by-step instructions or rules followed by a computer program to solve a problem or complete a task.

Anomaly Detection: The process of identifying rare or abnormal patterns or events in data that deviate from the expected behavior.

Artificial Intelligence (AI): Computer systems that can perform tasks that normally require human intelligence, like problem-solving or decision-making.

Augmented Reality (AR): Technology that overlays digital information, such as images or videos, onto the real world, enhancing the user’s perception and interaction with their surroundings.

Autoencoder: A type of artificial neural network used for data compression and feature extraction, often used in unsupervised learning.

Bias in AI: Unfair or discriminatory outcomes resulting from AI systems that reflect existing biases in the data or algorithms used.

Big Data: Extremely large and complex data sets that cannot be easily managed or analyzed using traditional data processing methods.

Chatbot: A computer program designed to simulate human conversation, typically used for customer service or providing information.

Clustering: A technique used in machine learning to group similar data points together based on their characteristics or properties.

Computer Vision: A field of AI that focuses on enabling computers to interpret and understand visual information from images or videos.

Convolutional Neural Network (CNN): A type of neural network commonly used in computer vision tasks, designed to automatically detect and understand visual patterns in images or videos.

Data Analysis: The process of inspecting, cleaning, transforming, and modeling data to discover useful insights and support decision-making.

Data Labeling: The process of assigning descriptive or categorical tags to data points, used to create labeled datasets for supervised learning.

Data Mining: The practice of examining large datasets to discover patterns, relationships, or other valuable information.

Data Science: An interdisciplinary field that combines scientific methods, algorithms, and systems to extract knowledge and insights from data.

Deep Learning: A type of machine learning that uses artificial neural networks to process and analyze large amounts of data, enabling the computer to make predictions or decisions.

Decision Tree: A flowchart-like model that represents decisions and their possible consequences, commonly used in machine learning for classification or regression tasks.

Ensemble Learning: A technique that combines multiple machine learning models to improve prediction accuracy and robustness.

Explainable AI: AI systems designed to provide explanations or justifications for their decisions, making their reasoning transparent and understandable to humans.

Facial Recognition: Technology that identifies or verifies an individual’s identity by analyzing their unique facial features.

Feature Extraction: The process of selecting or identifying the most relevant and informative attributes or characteristics from a given dataset.

Federated Learning: A decentralized approach to machine learning where the training data remains on local devices or servers, preserving privacy while enabling model improvement.

GAN (Generative Adversarial Network): A type of deep learning model consisting of two networks—a generator and a discriminator—competing against each other to generate realistic data samples.

Genetic Algorithms: Algorithms inspired by the process of natural selection, used in optimization and problem-solving to find the best solutions.

Hyperparameter: A configuration setting or parameter that is set before the learning process begins, influencing the behavior and performance of machine learning algorithms.

Internet of Things (IoT): A network of physical devices, vehicles, appliances, and other objects embedded with sensors, software, and connectivity to exchange data and interact with each other.

Machine Learning (ML): A subset of AI that focuses on enabling computers to learn from data and improve their performance without being explicitly programmed.

Natural Language Generation (NLG): The process of automatically generating human-like text or speech based on input data or instructions.

Natural Language Processing (NLP): The ability of a computer system to understand, interpret, and generate human language, enabling interactions between humans and machines through speech or text.

Neural Networks: Algorithms inspired by the human brain’s structure and function, used in deep learning to recognize patterns and make sense of complex information.

Precision and Recall: Evaluation metrics used to measure the performance of classification models, indicating the accuracy of positive predictions (precision) and the ability to find all positive instances (recall).

Predictive Analytics: The practice of using historical data and statistical models to predict future outcomes or events.

Quantum Computing: A field of computing that uses quantum mechanics principles to perform complex calculations, potentially offering significant advantages over traditional computers in terms of speed and processing power.

Recommendation Systems: Algorithms that analyze user preferences and behaviors to provide personalized suggestions or recommendations for products, services, or content.

Reinforcement Learning: A type of machine learning that involves training an AI agent to make decisions based on rewards or punishments in a dynamic environment.

Robotics: The branch of technology that deals with the design, construction, and operation of robots, often combining AI techniques to enable autonomous or intelligent behavior.

Sentiment Analysis: The process of determining and understanding the emotional tone or sentiment expressed in text, often used for gauging public opinion or customer feedback.

Speech Recognition: Technology that converts spoken words into written text, allowing computers to understand and process human speech.

Supervised Learning: A type of machine learning where the model is trained on labeled data, meaning it is provided with input-output pairs to learn from and make predictions.

Swarm Intelligence: An AI approach inspired by the collective behavior of social insects, such as ants or bees, focusing on decentralized decision-making and coordination among simple agents to solve complex problems.

Transfer Learning: A technique where knowledge or learned representations from one task or domain are applied to another related task or domain, enabling faster learning and improved performance.

Unstructured Data: Data that does not have a predefined structure or format, such as text documents, images, or audio files, requiring special techniques for analysis and processing.

Unsupervised Learning: A type of machine learning where the model learns patterns and structures in unlabeled data without explicit guidance or predefined outcomes.

Virtual Assistant: An AI-powered application or software that can understand and respond to user commands or queries, providing assistance or performing tasks.Virtual Reality (VR): A simulated experience that can be similar to or completely different from the real world, typically delivered through special devices such as headsets, providing an immersive and interactive environment.

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