Key Terms in Artificial Intelligence: A Comprehensive Glossary

The Glossary

• Algorithm: A set of rules or instructions given to an AI, or a machine, to help it learn on its own.
• Artificial General Intelligence (AGI): AI systems that possess the ability to understand, learn, adapt, and implement knowledge in a way similar to human intelligence.
• Artificial Intelligence (AI): The simulation of human intelligence processes by machines, especially computer systems.
• Backpropagation: A method used in artificial neural networks to calculate a gradient that is needed in the calculation of the weights to be used in the network.
• Big Data: Extremely large data sets that can be analyzed computurally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.
• Biometrics: Biological measurements or physical characteristics that can be used to identify individuals.
• Chatbot: An AI program designed to interact with humans in their natural language.
• Cognitive Computing: A system that mimics the human brain’s functioning, learning, and problem-solving approach.
• Computer Vision: The science that aims to give a similar, if not better, capability to a machine or computer.
• Convolutional Neural Networks (CNNs): A class of deep learning neural networks, most commonly applied to analyzing visual imagery.
• Data Mining: The process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
• Data Science: An interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
• Deep Learning: A subfield of machine learning that is a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—to “learn” from large amounts of data.
• Evolutionary Computation: A family of algorithms for global optimization inspired by biological evolution.
• Expert Systems: Computer systems that emulate the decision-making ability of a human expert.
• Fuzzy Logic: A computing approach based on “degrees of truth” rather than the usual true or false (1 or 0) Boolean logic.
• Genetic Algorithms: A search heuristic that is inspired by Charles Darwin’s theory of natural evolution.
• Heuristic: A rule or method that helps you solve problems faster than you would if you did all the computing.
• Intelligent Agent: Any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.
• Knowledge Graph: A knowledge base that uses a graph-structured data model or topology to integrate data.
• Machine Learning (ML): An application of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
• Natural Language Processing (NLP): The ability of a computer program to understand human language as it is spoken.
• Neural Networks: A series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.
• Ontology: In computer science and information science, an ontology encompasses a representation, formal naming, and definition of the categories, properties, and relations between the concepts, data, and entities.
• Pattern Recognition: The assignment of a label to a given input value.
• Predictive Modeling: A statistical technique using machine learning and data mining to predict future events.
• Quantum Computing: An area of computing focused on developing computer-based technologies centered around the principles of quantum theory.
• Reinforcement Learning: An area of machine learning where an agent learns to behave in an environment, by performing certain actions and observing the results.
• 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.
• Sentiment Analysis: The use of natural language processing to systematically identify, extract, quantify, and study affective states and subjective information.
• Supervised Learning: A type of machine learning where the model is provided with labeled training data.
• Swarm Intelligence: The collective behavior of decentralized, self-organized systems, natural or artificial.
• TensorFlow: An open-source software library for machine learning and artificial intelligence.
• Unsupervised Learning: A type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses.
• Turing Test: A measure of a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, human behavior.
• Virtual Reality: A simulated experience that can be similar to or completely different from the real world.
• Computer-Generated Imagery (CGI): The application of computer graphics to create or contribute to images in art, printed media, video games, films, television programs, shorts, commercials, videos, and simulators.
• Machine Ethics: Part of the ethics of artificial intelligence, concerned with the moral behavior of artificially intelligent beings.
• Machine Perception: The capability of a computer system to interpret data in a manner that is similar to the way humans use their senses to relate to the world around them.
• Ambient Intelligence: Electronic environments that are sensitive and responsive to the presence of people.
• Algorithmic Bias: Systematic and repeatable errors in a computer system that create unfair outcomes.
• Autoencoder: A type of artificial neural network used to learn efficient data codings in an unsupervised manner.
• Decision Tree: A decision support tool that uses a tree-like model of decisions and their possible consequences.
• Feature Extraction: The process of reducing the amount of resources required to describe a large set of data.
• Hyperparameter Optimization: The process of choosing a set of optimal hyperparameters for a learning algorithm.
• Knowledge Engineering: A field of artificial intelligence that tries to emulate the judgment and behavior of a human expert in a given field.
• Markov Decision Processes: A mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker.
• Multilayer Perceptron: A class of feedforward artificial neural network.
• Sequence Learning: A type of learning where an intelligent agent is trained to predict the next action based on a series of actions.
• Stochastic Gradient Descent: A method to estimate parameters in a statistical or machine learning model.

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