Key Terms in Artificial Intelligence: A Comprehensive Glossary


Artificial Intelligence (AI) has become an integral part of our everyday life, powering everything from our personal assistants to our favorite video games. But for those who are new to the field, the terminology can often be confusing. This glossary aims to clarify 50 key terms in the field of AI, making the subject more accessible to all.


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.


Final Thoughts


With this glossary in hand, navigating the complex world of artificial intelligence becomes a less daunting task. Remember, understanding the terminology is just the first step on your journey into the fascinating world of AI. Stay curious and keep learning. The future of AI is still being written, and you could play a part in shaping it. We invite you to explore our glossary on key terms in robotics for more insights!


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